Posted: March 12th, 2023

NUR 410

2

Impact of Telemedicine in the Care of Kidney Transplant Recipients

April Showers

University of Maryland Global Campus

NURS 410: Applying Evidence-Based Practice in Nursing

Professor Shakur

March 1, 2023

Impact of Telemedicine in the Care of Kidney Transplant Recipients

Every humanity should have equitable access to healthcare to improve quality of life. Finding a cure for patients with kidney failure to avoid relentless dialysis therapy through organ transplantation has made a great leap in medicine to restore their normal life. Having a new organ comes with strict discipline to ensure its graft survival. Consistent patient engagement to the transplant team is the key to success of a stable kidney function. Telemedicine has become an alternative option to stay in touch with their provider instead of the traditional in-person visit. The COVID-19 pandemic outbreak has forced our organization to adopt this new model of care in our practice to provide continuity of care. The benefits of this delivery of care during pandemic is paramount, however, with infection rates deescalating and some life normalcy returning, it begs the question if the utilization of telemedicine is sustainable in a long run. The purpose of this assignment is to provide a literature review to support the PICO(T) looking at telemedicine and transplant patients.

Background

Telemedicine of telehealth is evidenced-based practice that has been employed by other disciplines for years but not in transplantation. Telemedicine is described as the use of telecommunication technologies to connect between two parties – a patient and the provider in real-time through video to deliver health care related services at a distance, or basically a remote virtual interaction in real time. As mentioned earlier, the effects of the pandemic have triggered a practice to adapt to this new delivery of care which eventually contributed a positive impact to our patient population by restoring consistent engagement to their provider. However, as the rate of COVID cases started to wean down, the management has decided to cut down virtual visits which then prompted further inquiry to this issue. The PICOT question formulated for this topic is – will the utilization of telemedicine or telehealth appointments result in improved compliance to treatment regimen and overall plan of care compared to face-to-face traditional follow up care?

There are a few patients in the practice who were not seen for months that resulted in losing their kidney transplant, which unfortunately leading them back to dialysis. Schmid et al. (2017) iterated that consistency in follow-up care is the key to long-term survival of the graft as well as to maintain a stable kidney function. Using telehealth will be an alternative to in-person consultations so patients can stay engaged with their provider. After so many months of using telehealth then reverting to the traditional way of face-to-face visits, it begs the question the influence of telemedicine to patient care compliance.

To look for related topics of interest is by using the UMGC Library OneSearch engine and plugging in key words such as telemedicine or telehealth, renal/kidney transplant recipients, adherence or compliance, and positive outcomes which confines the search to peer-reviewed scholarly articles within the past five years. Surprisingly, there were quite a few research literatures that have been developed relating to the importance of integrating telemedicine to follow-up kidney/renal transplant recipients even before the emergence of COVID pandemic. Most studies were done in Europe and Australia, and very restricted studies were done in the United States prior to pandemic.

Literature Review

Telemedicine is not new in healthcare but lagging its integration to the transplant community until the pandemic hits the community worldwide. According to Wei et al. (2022), many patients favored telemedicine because of the feasibility of not travelling long distances while saving money for transportation, food, and lodging. Additionally, patients are pleased to conduct these virtual visits at the convenience of their homes and at work (Varsi et al., 2021). Thus, not missing any workdays or trying to get a day off to see their practitioner is a huge benefit of doing telehealth. On the contrary, many providers worry that this convenience from patient may compromise adherence to other therapies such as not doing routine blood work, mismanaged blood pressure and blood glucose levels, inappropriate or missing immune medications, etc., which can be more costly if there is a loss in graft function which would require more hospitalizations. On the other hand, with the feasibility of telemedicine, providers can negate any unplanned hospital admissions. Schmid et al. (2017) conducted a randomized, controlled trial of 46 individuals divided into 2 groups between standard of care group versus tele-medically supported case management group which indicates that the tele group having the liberty to have access to the team can prevent further development of serious complications. Authors also stated that the medical team can intervene sooner which shortened the length of unplanned hospital stays.

Telemedicine has the potential of keeping the patient engaged. With providers being unable to make an assessment physically in-person, patients would likely heighten their self-care management skills such as understanding the parameters of their lab values, blood pressure and heart rate readings, cholesterol and blood sugar levels, and immunosuppressant (IS) therapeutic levels. Schmid et al. (2017) observed that patients in the telemedicine group have higher GFR function than those patients receiving the standard of care. Therefore, authors implied that the group of tele-medically supported care are more adherent. Furthermore, with the use of telemedicine, patients can do their blood work closer to their homes in other laboratory clinics, thus avoiding duplication of lab examination and travel costs (Pape et al., 2017). Despite the benefits of using virtual visits, in a qualitative study conducted by Varsi et. al. (2021) to 15 patients, their subjects/patients expressed some technical difficulties such as poor sound quality, reception, and image quality which led them to resort to using the telephone to complete the meeting. Regardless of these technical challenges, researchers emphasized that patients were not deterred from using video consultation expressing that the problems could be typical glitches when starting a new system.

Moreover, telemedicine can provide a holistic approach into patient follow-up care. Pape et al. (2017) integrated into their study a video/virtual visits to assess psychosocial and cardiovascular function: psychosocial team can evaluate overall mental and psychosocial disabilities and what not and make appropriate interventions to enhance adherence to care while a cardiac team or physiologist has designed an exercise program that patient can use at home to improve their general physical endurance. Similarly, in a study by Schmid et al. (2017), individuals in telemedicine/care management group who have higher percentage of adhering to follow up care lead them to have better cardiac and kidney function and eventually afforded them to return to work during the first year of posttransplant compared to group receiving standard of care who are less adherent and failed to return to work within same period.

Lastly, besides the promising benefits of utilizing telemedicine, integrating it into practice does not seem to be a seamless approach. Varsi et al. (2021) argued that its implementation into clinical practice may not be realized if reimbursement is not comparable to that of in-person visits. That means, televisits reimbursements is less than in-person visits. Considering the aforementioned technological challenges, technical systems must meet requirements and function more efficiently to avoid undue interruptions and delays of other patients waiting for their appointments. Furthermore, Wei et al. (2022) conducted a study to 2801 patients and found that patients with government insurance like Medicare and Medicaid are less likely to use telemedicine compared to private-insured patients. Researchers implied that patients with Medicare or Medicaid are 65 years old and above, with disability, or with ESRD (end-stage renal disease) could possibly have low socioeconomic status which related to poor access to smartphones or computers and Wi-Fi coverage that are essential for tele-visits. Likewise, older populations have a low usage of telemedicine likely due to “mistrust of technologies, suboptimal cognition and motor skills, and visual perception difficulties” (Wei et al., 2022, p. 4). Authors reiterated the importance of having support system to assist them in navigating new tools especially nowadays that advanced technologies continue to evolve in healthcare.The literature review provides evidence that telemedicine is feasible in medical practice. It is effective and efficient but not necessarily applicable to everyone. In kidney transplant, the goal of managing kidney transplant recipients is to prolong graft survival which means finding the best possible intervention to keep patients involved in their care. Most patients who have existing chronic diseases like hypertension, diabetes, renal disease on dialysis which prevent them from holding any jobs and puts them into low socioeconomic status, are unable to sustain internet access to do tele-video capability. Other patients who have jobs prior to transplants and wanted to resume their jobs after the surgery may find tele-visits more convenient to avoid frequent trips to the doctor’s office and avoid multiple absences from work. On the contrary, depending on their socioeconomic status or if they hold employment or not, telemedicine may or may not be applicable because of complex health and social conditions surrounding their disease process.

Utilizing telemedicine is not a seamless approach. Both patients and providers expressed frustrations in dealing with technological difficulties – poor reception, poor quality of image and sound that can impede the process, however, these hiccups are of minute importance and will not dissuade them from using it in the future. And last, it is important to point out that the success of the studies is related to how researchers design their investigation – their methods and procedures. In one study, they hired part-time workers dedicated in doing the research and the other study subjects get a thorough evaluation by psychosocial and cardiac team. Resources allocated in research can highly influence the findings of the study.

Application to Nursing Practice

As mentioned previously, telemedicine is not new in healthcare. It gained its popularity with the outbreak of COVID pandemic. Though patients and healthcare teams find telemedicine to be an enormous potential in clinical practice, there is not enough evidence or studies to suggest that it is financially sound for the organization. Learning it from current practice and the reviewed literature, telemedicine reimbursement is not equivalent to face-to-face visit which may likely be the reason why all articles ended their narrative of encouraging academia and researchers to conduct more scientific testing to explore its relevance in clinical practice. Therefore, more studies in telemedicine should be pursued in this population to demonstrate its feasibility and sustainability into clinical practice, and perhaps with improved positive outcomes as illustrated by longer graft survival can present cost compensation in all stakeholders.

Conclusion

Telemedicine came into our practice by chance in perfect timing. Kidney transplant recipients require frequent monitoring to ensure longevity of their graft survival. A standard of care like face-to-face visits with the provider demonstrates that it is not feasible to every patient or in every scenario due to many reasons. The uptake of telehealth is positive in all literatures: patients find it convenient for so many levels while providers can implement early intervention prior to complications and avert unnecessary hospital admissions. Therefore, telehealth is a great alternative and/or supplement to in-person visit. Transplant centers may still have trepidation of fully integrating it into clinical practice likely due to unable to scale the cost in terms of reimbursements, long-term cost of technologies with associated software, and allocations of appropriate staffing.

References

Pape, L., Zwaan, M. D., Tegtbur, U., Feldhaus, F., Wolff, J. K., Schiffer, L., Lerch, C., Hellrung, N., Kliem, V., Lonnemann, G., Nolting, H. D., & Schiffer, M. (2017). The KTx360°-study: A multicenter, multisectoral, multimodal, telemedicine-based follow-up care model to improve care and reduce health-care costs after kidney transplantation in children and adults.
BMC Health Services Research, 17.

https://doi.org/10.1186/s12913-017-2545-0

Schmid, A., Hils, S., Kramer-Zucker, A., Bogatyreva, L., Hauschke, D., De Geest, S., & Pisarski, P. (2017). Telemedically supported case management of living-donor renal transplant recipients to optimize routine evidence-based aftercare: A single-center randomized controlled trial.
American Journal of Transplantation, 17(6), 1594-1605.

https://doi.org/10.1111/ajt.14138

Varsi, C., Stenehjem, A. E., Borosund, E., & Nes, L. S. (2021). Video as an alternative to in-person consultations in outpatient renal transplant recipient follow-up: A qualitative study.
BMC Nephrology, 22.

https://doi.org/10.1186/s12882-021-02284-3

Wei, T. R., Berner, E. S., Qu, H., & Agarwal, G. (2022). Factors associated with telemedicine utilization among post-transplant patients at a university kidney and pancreas transplant centers.
Clinical Transplantation, 36(4)
.

https://doi.org/10.1111/ctr.14578

LITERATURE REVIEW NUR 410.

PICOT question

In the adult population (P), will the administration of probiotics (I) with antibiotic use reduce the incidence of Clostridium difficile infection (CDI) infections (O) during antibiotics therapy (T)?

7 pages of content; please only put redundant paragraphs to fill the pages. it is a 400-point assignment.

Specific Assignment Instructions

Below is the outline to be followed for creating your paper:

Introduction this is NOT a level 1 headings)

1. State the topic you selected and why.

Topic: The effect of probiotics to reduce incidence of C-diff in adult hospitalized patient on antibiotics therapy.

2. Background

· Provide some background information about the topic. Why is it important? Use data to support your argument.

· Explain your search and methods – List a minimum of four keywords that you used for your search, the databases you used to perform your search, and how you selected the articles you are reviewing.

Part Answer: I used CINAHL complete database, Health Source: Nursing/ Academic Edition, Nursing reference Centre plus, Nursing & Allied Health, and ScienceDirect. 

I used “C. difficile,” “probiotics,” “adult,” and “antibiotics “for most of the search. Sometimes I must use two or three of the search times to get a result. The parameters are “2019-2023”, peer-reviewed, and full text.

3. Synthesis of the Findings

4. Synthesize what you have learned from the articles you have evaluated.

5. Summarize the overall findings of the articles.

Application to Nursing Practice

6. Describe how these can be applied to nursing research and practice.

Conclusion

7. Provide a brief conclusion to wrap up the contents presented in the paper.

Reference Page

· Provide a reference page with appropriate APA-formatted citations of all sources used within the paper.

M A J O R A R T I C L E

e2512 • cid 2021:73 (1 November) • Wombwell et al

Clinical Infectious Diseases

Received 18 February 2020; editorial decision 8 May 2020; accepted 15 June 2020; published
online June 23, 2020.

Correspondence: M.  E. Patterson, Division of Pharmacy Practice and Administration,
University of Missouri–Kansas City School of Pharmacy, 4245 Health Sciences Bldg, 2464
Charlotte St, Kansas City, MO 64108-2718 (pattersonmar@umkc.edu).

Clinical Infectious Diseases® 2021;73(9):e2512–8
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society
of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
DOI: 10.1093/cid/ciaa808

The Effect of Saccharomyces boulardii Primary
Prevention on Risk of Hospital-onset Clostridioides
difficile Infection in Hospitalized Patients Administered
Antibiotics Frequently Associated With C. difficile
Infection
Eric Wombwell,1,2 Mark E. Patterson,1, Bridget Bransteitter,3 and Lisa R. Gillen2

1Division of Pharmacy Practice and Administration, University of Missouri–Kansas City School of Pharmacy, Kansas City, Missouri, USA, 2Department of Pharmacy, Centerpoint Medical Center,
Independence, Missouri, USA, and 3Department of Medicine, Centerpoint Medical Center, Independence, Missouri, USA

(See the Editorial commentary by McFarland on pages e2519–20.)

Background. Hospital-onset Clostridioides difficile infection (HO-CDI) is a costly problem leading to readmissions, morbidity,
and mortality. We evaluated the effect of a single probiotic strain, Saccharomyces boulardii, at a standardized dose on the risk of
HO-CDI within hospitalized patients administered antibiotics frequently associated with HO-CDI.

Methods. This retrospective cohort study merged hospital prescribing data with HO-CDI case data. The study assessed patients
hospitalized from January 2016 through March 2017 who were administered at least 1 dose of an antibiotic frequently associated
with HO-CDI during hospitalization. Associations between S. boulardii administration, including timing, and HO-CDI incidence
were evaluated by multivariable logistic regression.

Results. The study included 8763 patients. HO-CDI incidence was 0.66% in the overall cohort. HO-CDI incidence was 0.56%
and 0.82% among patients coadministered S. boulardii with antibiotics and not coadministered S. boulardii, respectively. In adjusted
analysis, patients coadministered S. boulardii had a reduced risk of HO-CDI (odds ratio [OR], 0.57 [95% confidence interval {CI},
.33–.96]; P =  .04) compared to patients not coadministered S. boulardii. Patients coadministered S. boulardii within 24 hours of
antibiotic start demonstrated a reduced risk of HO-CDI (OR, 0.47 [95% CI, .23–.97]; P = .04) compared to those coadministered
S. boulardii after 24 hours of antibiotic start.

Conclusions. Saccharomyces boulardii administered to hospitalized patients prescribed antibiotics frequently linked with
HO-CDI was associated with a reduced risk of HO-CDI.

Keywords. Clostridium infections; Clostridioides difficile; probiotics; nosocomial infection; infection control.

Clostridioides difficile infection (CDI) has become one of the most
common healthcare-associated infections in the United States,
with the incidence nearly doubling between 2001 and 2010 [1,
2]. The Centers for Disease Control and Prevention (CDC) con-
siders CDI an urgent threat requiring prevention and monitoring
[1]. Risk factors for CDI include increasing age, proton pump in-
hibitor (PPI) use, and most significantly, broad-spectrum anti-
biotics that are hypothesized to accelerate C. difficile colonization
by reducing levels of beneficial bacteria that serve as barriers to

infection [3]. Coadministering probiotics with antibiotics may
prevent CDI development by reinforcing the barrier of good bac-
teria lost through antibiotic administration [4, 5].

Beyond restoring altered intestinal microflora, administering
probiotics may stimulate the immune system to prevent path-
ogen adhesion and invasion, and clear pathogens and toxins
from the intestinal tract [6]. Saccharomyces boulardii, a spe-
cific yeast-derived probiotic, may prevent CDI by inducing
direct inhibitory actions against C.  difficile toxins [5, 6]. Two
studies demonstrate upregulation of total and specific intes-
tinal antibodies (immunoglobulin A) to toxin A in response to
S. boulardii exposure, consequently reducing C. difficile path-
ogenicity [7, 8]. Other evidence demonstrates that S. boulardii
directly inhibits C. difficile toxin by releasing a protease to hy-
drolyze C. difficile toxins [6, 9].

The available literature examining the clinical outcomes
of probiotics in preventing hospital-onset C.  difficile infec-
tion (HO-CDI) is contentious. Three randomized clinical trials

mailto:pattersonmar@umkc.edu?subject=

http://orcid.org/0000-0002-2600-6887

S. boulardii Primary Prevention • cid 2021:73 (1 November) • e2513

[10–12] and 1 cohort study [13] demonstrate S.  boulardii’s pri-
mary or secondary prevention effects on HO-CDI. In contrast,
the largest randomized trial of probiotics to date [14] and 1 cohort
study of S. boulardii [15] show no protective associations. Overall
these studies are small in scale and do not take into account the
timing of probiotic initiation relative to antibiotic start [5, 11,
15]. A meta-analysis found that administering probiotics >2 days
after initiating antibiotics significantly reduced the efficacy by half
[16]. Given mixed evidence, research needs to assess the extent to
which administrative timing impacts probiotics’ beneficial effects.

Given the limitations of previous studies, the Infectious
Diseases Society of America (IDSA) and the Society of
Healthcare Epidemiology of America (SHEA) state that “there
are insufficient data at this time to recommend administration
of probiotics for primary prevention of CDI” [17]. The IDSA
and SHEA guideline writers cite specific limitations such as
studies including patients with abnormally high rates of CDI,
using inconsistent probiotic formulations across studies, or as-
sessing patient populations at low risk for CDI [17]. These lim-
itations underscore the need for further research. The guideline
recommendations from IDSA and SHEA conclude with sug-
gested areas of further research including: (1) What preventive
measures can be taken to reduce the incidence of CDI? (2) Can
administration of probiotics effectively prevent CDI?

Our primary objective was to evaluate the effect of pre-
scribing a single probiotic formulation S.  boulardii on risk of
developing HO-CDI in a large cohort of hospitalized patients
receiving antibiotics frequently associated with HO-CDI.
Our secondary objective was to evaluate the extent to which
timing of S. boulardii initiation relative to antibiotic start affects
HO-CDI risk.

METHODS

This retrospective observational cohort study compares the risk
of HO-CDI in hospitalized patients who only received anti-
biotics frequently associated with HO-CDI vs those who re-
ceived antibiotics in combination with S. boulardii. The study
merged C.  difficile case data with medication administration
records to evaluate associations between HO-CDI incidence
and S.  boulardii administration occurring between 1 January
2016 and 31 March 2017. The study setting is a 220-bed level 2
trauma center nonacademic hospital.

In December 2015, our institution established S. boulardii at
a dose of 500 mg twice daily as the only formulary probiotic.
This decision resulted from an internally performed literature
review of probiotic agents by the Pharmacy and Therapeutics
Committee. The committee concluded from the literature re-
viewed that the use of S.  boulardii at a dose of 500 mg twice
daily was more often associated with positive outcomes for re-
ducing antibiotic-associated diarrhea and CDI. Corresponding
with the formulary change, an electronic pop-up box was added

to the physician electronic order entry system with an option
to order S. boulardii. The pop-up box appeared following the
entry of an antibiotic order for β-lactams, fluoroquinolones,
or lincosamides. The S.  boulardii order was not an automatic
reflex order. The pop-up box also listed precautions for use of
S.  boulardii, including (1) a history of organ transplantation
with current receipt of antirejection medication; (2) concom-
itant receipt of chemotherapy or radiation; (3) low neutrophil
count; (4) diagnosis of AIDS; or (5) active gastrointestinal ulcer.

The cohort included adult patients (1) admitted to an in-
patient medical unit with an average length of stay equaling
≥3  days; and (2) having barcode administration evidence
for reception of at least 1 dose of an antibiotic frequently as-
sociated with HO-CDI, which was defined as clindamycin,
fluoroquinolones, third- and later-generation cephalosporins,
carbapenems, and penicillins [18–20]. For the purpose of this
study, “antibiotic” will subsequently refer to these defined anti-
biotics frequently associated with HO-CDI. The unit of analysis
was defined as the first hospitalization during the study period.
Only the first hospitalization per unique patient was included
in the analytic dataset to remove within-patient changes in
S. boulardii exposure across separate hospitalizations.

Patients were classified as either having been or not having
been administered S.  boulardii during hospitalization, based
upon having barcode administration evidence for reception
of at least 1 dose of S.  boulardii, or no evidence of barcode
scan for administration, respectively. Saccharomyces boulardii
(Florastor Daily Probiotic Supplement, Biocodex, Redwood
City, California) administration consisted of two 250-mg cap-
sules by mouth twice daily. Each 250  mg capsule contains 5
billion colony-forming units (CFUs) for a total daily adminis-
tration of 20 billion CFUs. The dose of 20 billion CFUs is con-
sistent with previously published studies assessing CDI primary
prevention [21].

The study used C. difficile case data defined by and reported to
the National Health Safety Network from our institution’s infec-
tion control office. HO-CDI cases were defined as positive if an
unformed stool specimen tested positive for C. difficile ≥3 days
after admission and >8 weeks from a previous positive spec-
imen result, consistent with the CDC definition for HO-CDI
[22–23]. Cases were laboratory confirmed by polymerase chain
reaction for the gene encoding toxin B alone without a reflex al-
gorithm. Patients required the following criteria prior to testing:
(1) ≥3 loose stools within 24 hours; (2) no laxative/stool soft-
eners within 48 hours; (3) no positive C. difficile test in the last
30 days; and (4) no negative C. difficile test in the last 7 days.
Samples were required to be watery; if the stool sample received
by the laboratory was formed or semiformed, the laboratory re-
jected the sample.

To evaluate the effect of S.  boulardii administration on
HO-CDI risk, we conducted our first multivariable lo-
gistic regression to estimate the risk of HO-CDI during

e2514 • cid 2021:73 (1 November) • Wombwell et al

a hospitalization conditional upon the coadministration
of S.  boulardii with antibiotic(s) vs antibiotic(s) without
S.  boulardii. To account for both potential confounders and
selection bias, the model included propensity scores gener-
ated by a separate multivariable logistic model testing the
likelihood of patients receiving S. boulardii conditional upon:
(1) antibiotic(s) administered during hospital admission;
(2) metronidazole administration during hospitalization but
>48 hours prior to CDI diagnosis; (3) PPI administration
at any time during hospitalization prior to CDI diagnosis;
(4) intensive care unit (ICU) admission; (5) patient age of
65 years or greater; and (6) gender. Each hospitalization was
assigned a propensity score used as a covariate in the final
multivariate model.

To evaluate the extent to which the timing of S. boulardii in-
itiation relative to the first dose of antibiotic affected HO-CDI
risk, we conducted a second multivariable logistic regression
among only the subgroup of patients receiving S. boulardii. This
model tested the risk of HO-CDI conditional upon early vs late
coadministration of S.  boulardii with antibiotics. “Early” was
defined as S. boulardii administered within 24 hours of first an-
tibiotic dose administration; “late” was defined as S. boulardii
administered ≥24 hours after the first antibiotic dose. Similar
to the model run in the full cohort, propensity scores for this
second analysis were generated with a multivariable logistic
model testing the likelihood of patients receiving early vs late
S. boulardii conditional upon the same baseline covariates in-
cluded in the primary model run in the full cohort. Each hospi-
talization was assigned a propensity score used as a covariate in
the final multivariable model. Receiver operating characteristic
curves were used to calculate C-statistics to estimate the overall
global fit for all multivariable logistic regressions, including
those used to generate propensity scores. All statistical analyses
were conducted using SPSS version 24.0 software (IBM SPSS,
Chicago, Illinois).

RESULTS

A total of 8763 patients administered at least 1 dose of an an-
tibiotic frequently associated with HO-CDI were assessed.
The cohort was 39% male and averaged 64  years of age.
Patients coadministered S. boulardii and antibiotics were more
often male (P < .0001) and ≥65 years of age (P < .0001) com-
pared to patients only administered antibiotics. Ceftriaxone,
piperacillin-tazobactam, levofloxacin, and ciprofloxacin were
the most frequently administered antibiotics, administered in
44%, 32%, 25%, and 21% of patients, respectively. Carbapenem,
fluoroquinolone, and cephalosporin administration was signif-
icantly higher in patients coadministered S. boulardii compared
with those not coadministered S.  boulardii (P  <  .0001). PPIs
were administered in 46% of patients. PPI administration was
significantly higher in patients coadministered S. boulardii and

antibiotics (50%) compared with patients not coadministered
S. boulardii (41%) (P < .0001; Table 1).

The overall incidence of HO-CDI during the study period was
0.66%. Patients admitted to the ICU and patients administered
PPIs demonstrated a higher incidence of and risk for HO-CDI
(Tables 2 and 3). The incidence of HO-CDI was lower in pa-
tients coadministered S. boulardii and antibiotics (0.56%) com-
pared to patients administered antibiotics without S. boulardii
(0.82%). With respect to administration timing, the incidence
of HO-CDI in patients receiving early S. boulardii was less than
half of those patients administered antibiotics alone without
S. boulardii (0.38% vs 0.82%) (Table 2).

When adjusting for possible confounders and selection bias
using the propensity score, the risk for HO-CDI was signifi-
cantly less in patients administered S. boulardii and antibiotics
(odds ratio [OR], 0.57 [95% confidence interval {CI}, .33–.96])
(Table 4) compared to patients administered antibiotics alone.
Early S. boulardii administration displayed a stronger HO-CDI
preventive effect compared to late S.  boulardii administration
(OR, 0.47 [95% CI, .23–.97]) (Table 5).

DISCUSSION

When adjusted for potential confounders, patients adminis-
tered S. boulardii in conjunction with antibiotics frequently as-
sociated with HO-CDI had a lower risk of developing HO-CDI
compared with patients not administered S.  boulardii in this
single-center study. The protective effect was more pronounced
in the early S. boulardii subgroup vs the late S. boulardii sub-
group, suggesting that early administration might offer greater
HO-CDI risk reduction compared to late administration. A re-
cent meta-analysis on the use of probiotics for primary pre-
vention of HO-CDI revealed similar findings alluding to the
importance of probiotic initiation timing relative to the first
dose of antibiotic [16]. The meta-analysis observed that the pre-
ventive effect was limited to probiotic initiation within 1–2 days
of antibiotic start and a lack of benefit when administered out-
side of 2 days.

To explore this idea further, we conducted a post hoc
multivariable logistic regression conditional upon the
coadministration of early S.  boulardii vs no S.  boulardii with
antibiotics. The analysis resulted in a reduced odds risk of
HO-CDI for early S. boulardii vs no S. boulardii (OR, 0.44 [95%
CI, .23–.84]; P = .013; C-statistic = 0.601), demonstrating a con-
sistent stronger effect for early S.  boulardii and HO-CDI risk
reduction. Presumptively, an earlier initiation of a preventive
therapy would have a greater effect than a preventive therapy
started further from the inciting event. Here we provide data to
support that assumption with S.  boulardii administration rel-
ative to antibiotic initiation. The secondary analysis included
a small number of events and therefore is considered a provi-
sional finding requiring further investigation.

S. boulardii Primary Prevention • cid 2021:73 (1 November) • e2515

Our study has several limitations. First, our sample originates
from a single medical center and decreases the generalizability
of the findings. Second, we were unable to assess preexisting
conditions, such as immunosuppression and CDI history, or
patient severity status as risk factors for HO-CDI within the
study or provider-level preventive practices. To mitigate this
limitation, we included ICU admission as a confounder to ac-
count for patients at a higher acuity level with severe illness.
Third, providers were cautioned to avoid prescribing probiotics
in patients (1) with a history of organ transplantation currently
receiving antirejection medication; (2) receiving chemotherapy
or radiation; (3) with low neutrophil count; (4) diagnosed with
AIDS; or (5) having an active gastrointestinal ulcer [24–26].
There was no system in place to ensure S. boulardii was not pre-
scribed to a patient who met these criteria. Consequently, we
cannot make conclusions regarding the probiotic effectiveness
in these patient groups.

This study has several strengths. First, including propensity
scores in our models helped us account for both confounding

and selection bias. The propensity score helps account for ob-
servable risk factors either associated with an increase or de-
creased risk of HO-CDI. In our analysis, patients administered
S.  boulardii could be considered at higher risk for HO-CDI
as they were more often ≥65 years of age, administered a PPI
during hospitalization at a higher rate, and more likely to re-
ceive a third- or fourth-generation cephalosporin, carbapenem,
and fluoroquinolone than non–S. boulardii recipients. Adding
age, PPI, and antibiotic class utilization as covariates in the
multivariable model used to derive the propensity score allows
the propensity score to account for all of these factors simul-
taneously. Furthermore, the propensity score helps account
for underlying factors driving selection bias that could not be
measured in our dataset, including, but not necessarily lim-
ited to, patient history of HO-CDI or provider-level behaviors.
Second, our observational study design enables us to measure
real-world effectiveness based upon daily clinical encounters
in contrast to clinical trials that may not reflect outcomes or
patients seen in everyday practice. Evidence from real-world

Table 1. Demographic and Baseline Characteristics, by Cohort

Characteristic
Total Patients

(N = 8763)
Patients Not Coadministered

Saccharomyces boulardii (n = 3276)
Patients Coadministered

Saccharomyces boulardii (n = 5487)
P

Value

Demographics

Age, y, mean ± SD 64.3 ± 18.4 62.3 ± 19.2 65.4 ± 17.7 <.001

Age ≥65 y 4631 (52.8) 1606 (49.0) 3025 (55.1) <.001

Male sex 3390 (38.7) 1259 (38.4) 2131 (38.8) <.001

Intensive care unit 467 (5.3) 223 (6.8) 244 (4.4) <.001

Concomitant medications

Metronidazole 1211 (13.8) 483 (14.7) 728 (13.3) .05

Proton pump inhibitor 4050 (46.2) 1331 (40.6) 2719 (49.6) <.001

Antibiotic(s) administered
during hospitalization

Penicillins 3173 (36.2) 1231 (37.6) 1942 (35.4) .04

Amoxicillin 69 (0.8) 45 (1.4) 24 (0.4) <.001

Amoxicillin-clavulanate 292 (3.3) 82 (2.5) 210 (3.8) .001

Ampicillin 66 (0.8) 41 (1.3) 25 (0.5) <.001

Ampicillin-sulbactam 182 (2.1) 84 (2.6) 98 (1.8) .02

Piperacillin-tazobactam 2780 (31.7) 1030 (31.4) 1750 (31.9) .7

Cephalosporins 4228 (48.2) 1407 (42.9) 2821 (51.4) <.001

Ceftriaxone 3824 (43.6) 1278 (39.0) 2546 (46.4) <.001

Cefdinir 134 (1.5) 27 (0.8) 107 (2.0) <.001

Cefepime 408 (4.7) 123 (3.8) 285 (5.2) .002

Ceftaroline 58 (0.7) 12 (0.4) 46 (0.8) .008

Ceftolozane-tazobactam 4 (0.0) 1 (0.0) 3 (0.1) 1.0

Ceftazidime-avibactam 1 (0.0) 0 (0.0) 1 (0.0) 1.0

Carbapenems 187 (2.1) 40 (1.2) 147 (2.7) <.001

Ertapenem 120 (1.4) 20 (0.6) 100 (1.8) <.001

Meropenem 83 (0.9) 24 (0.7) 59 (1.1) .1

Fluoroquinolones 3768 (43.0) 1280 (39.1) 2488 (45.3) <.001

Ciprofloxacin 1863 (21.3) 661 (20.2) 1202 (21.9) .06

Levofloxacin 2168 (24.7) 695 (21.2) 1473 (26.8) <.001

Lincosamides 369 (4.2) 155 (4.7) 214 (3.9) .06

Clindamycin 369 (4.2) 155 (4.7) 214 (3.9) .06

Data are presented as no. (%) unless otherwise indicated. χ 2 statistical analysis of differences between groups.
Abbreviation: SD, standard deviation.

e2516 • cid 2021:73 (1 November) • Wombwell et al

practice can better inform changes in clinical guidelines or for-
mulary policies. Third, isolating our study to a single agent at
a set dose improves our ability to determine the effects of uni-
form doses and inform specific dose recommendations. In con-
trast, previous studies included multiple probiotics in the same
treatment group and could not tease out the effects of isolated

agents or dosages. Fourth, we conducted a subgroup analysis to
explore the importance of S.  boulardii administration timing
relative to antibiotic start and resultant HO-CDI incidence. This
subgroup analysis suggests that earlier probiotic administration
timing improves probiotic primary prevention efficacy. Finally,
our point estimate of OR = 0.57 is similar with those found in

Table 2. Incidence of Hospital-onset Clostridioides difficile Infection for Baseline Characteristics

Characteristic Total Patients (N = 8763) HO-CDI Event (n = 58) No HO-CDI (n = 8705) P Value

Demographics

Age ≥65 y 4631 (52.8) 34 (0.7) 4597 (99.3) .4

Male sex 3390 (38.7) 25 (0.7) 3365 (99.3) .5

Intensive care unit 467 (5.3) 9 (1.9) 458 (98.1) .001

Concomitant medications

Metronidazole 1211 (13.8) 8 (0.7) 1203 (99.3) 1.0

Proton pump inhibitor 4050 (46.2) 35 (0.9) 4015 (99.1) .03

Antibiotic(s) administered

Penicillins 3173 (36.2) 36 (1.1) 3137 (98.9) <.001

Amoxicillin 69 (0.8) 0 (0.0) 69 (100.0) .5

Amoxicillin-clavulanate 292 (3.3) 1 (0.3) 291 (99.7) .5

Ampicillin 66 (0.8) 0 (0.0) 66 (100.0) .5

Ampicillin-sulbactam 182 (2.1) 4 (2.2) 182 (97.8) .01

Piperacillin-tazobactam 2780 (31.7) 34 (1.2) 2746 (98.8) <.001

Cephalosporins 4228 (48.2) 31 (0.7) 4197 (99.3) .4

Ceftriaxone 3824 (43.6) 24 (0.6) 3800 (99.4) .7

Cefdinir 134 (1.5) 1 (0.7) 133 (99.3) .9

Cefepime 408 (4.7) 8 (2.0) 400 (98.0) .001

Ceftaroline 58 (0.7) 2 (3.4) 56 (96.6) .009

Ceftolozane-tazobactam 4 (0.0) 1 (25.0) 3 (75.0) 1.0

Ceftazidime-avibactam 1 (0.0) 0 (0.0) 1 (100.0) .9

Carbapenems 187 (2.1) 5 (2.7) 182 (97.3) .001

Ertapenem 120 (1.4) 2 (1.7) 118 (98.3) .2

Meropenem 83 (0.9) 5 (6.0) 78 (94.0) <.001

Fluoroquinolones 3768 (43.0) 28 (0.7) 3740 (99.3) .4

Ciprofloxacin 1863 (21.3) 14 (0.8) 1849 (99.2) .6

Levofloxacin 2168 (24.7) 15 (0.7) 2153 (99.3) .8

Lincosamides 369 (4.2) 0 (0) 369 (100.0) .1

Clindamycin 369 (4.2) 0 (0) 369 (100.0) .1

Saccharomyces boulardii 5487 (62.6) 31 (0.56) 5456 (99.4) .2

Early S. boulardii 3936 (44.9) 15 (0.38) 3921 (99.6) .01

Late S. boulardii 1551 (17.7) 16 (1.03) 1535 (99.0) .5

Data are presented as no. (%) unless otherwise indicated. χ 2 statistical analysis for development of hospital-onset Clostridioides difficile infection.
Abbreviation: HO-CDI, hospital-onset Clostridioides difficile infection.

Table 3. Risk Associations of Hospital-onset Clostridioides difficile Infection Determined by Unadjusted Bivariate Analysis (N = 8763)

Characteristic OR (95% CI) P Value

Demographics

Age ≥65 y 1.27 (.75–2.14) .4

Male sex 1.20 (.71–2.03) .5

Intensive care unit 3.31 (1.62–6.77) .001

Concomitant medications

Metronidazole 1.00 (.47–2.1) 1.0

Proton pump inhibitor 1.78 (1.05–3.01) .03

Intervention

Saccharomyces boulardii and antibiotic 0.68 (.41–1.1) .2

Abbreviations: CI, confidence interval; HO-CDI, hospital-onset Clostridioides difficile infection; OR, odds ratio.

S. boulardii Primary Prevention • cid 2021:73 (1 November) • e2517

meta-analyses by Johnston et al (Relative Risk [RR], 0.34 [95%
CI, .24–.49]) [27], Goldenberg et al (RR, 0.36 [95% CI, .26–.51])
[28], Lau et al (RR, 0.40 [95% CI, .29–.53]) [29], and Shen et al
(RR, 0.42 [95% CI, .30–.57]) [16]. Similar results across multiple
studies further strengthens confidence in our estimates.

We observed a number needed to treat for early S. boulardii
administration of 228, amounting to 17 cases of HO-CDI that
may have been prevented among the 3936 patients adminis-
tered S.  boulardii within 24 hours of antibiotic start during
the study period. Given the magnitude of benefit and the rel-
atively low cost of probiotics compared to the high cost of
treating CDIs during hospitalization and postdischarge, adding
S. boulardii probiotic as a primary preventive strategy may be
a valuable strategy to reduce HO-CDI. Furthermore, results
from a cost-effectiveness analysis suggest that probiotic use is a
cost-effective strategy in preventing CDI in hospitalized adults
receiving antibiotics [30].

CONCLUSIONS

This study provides evidence to support prescribing a uni-
form probiotic formulation as primary prophylaxis of
HO-CDI in a high-risk population, a noted weakness in the
existing literature [17]. We observed that coadministration
of probiotic S. boulardii at a dose of 20 billion CFUs per day
with antibiotic therapy frequently associated with HO-CDI
in an older hospitalized patient population significantly re-
duced the incidence of HO-CDI. Furthermore, initiating
probiotics within 24 hours of antibiotic start appears to offer
a more pronounced barrier to HO-CDI development. Our
findings in conjunction with evidence from previous studies
support continued exploration of probiotics for primary pre-
vention of HO-CDI, with particular consideration of earlier
probiotic administration timing.

Notes
Disclaimer. The views expressed in this publication represent those of

the authors and do not necessarily represent the official views of Hospital
Corporation of America (HCA) or any of its affiliated entities.

Financial support. This research was supported in part by HCA and/or
an HCA-affiliated entity.

Potential conflicts of interest. The authors: No reported conflicts of
interest. All authors have submitted the ICMJE Form for Disclosure of
Potential Conflicts of Interest. 

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Table 5. Adjusted Risk Association of Hospital-onset Clostridioides difficile Infection With Early Versus Late Saccharomyces boulardii (N = 8763)

Probiotic Timing HO-CDI Event Rate, No. (%) Adjusted Risk, OR (95% CI) P Value

Early Saccharomyces boulardii 15/3936 (0.38) 0.47 (.23–.97) .041

Late S. boulardii 16/1551 (1.0) Ref Ref

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Abbreviations: CI, confidence interval; HO-CDI, hospital-onset Clostridioides difficile infection; OR, odds ratio.

Table 4. Adjusted Risk Associations of Hospital-onset Clostridioides difficile Infection With Saccharomyces boulardii Using Propensity Scores in
Multivariable Regression (N = 8763)

Cohort
HO-CDI Event Rate,

No. (%)
Adjusted Risk, OR
(95% CI) P Value

No Saccharomyces boulardii (antibiotic only) 27/3276 (0.82) Ref Ref

S. boulardii and antibiotic 31/5487 (0.56) 0.57 (.33–.96) .035

Adjusted multivariable logistic regression included propensity score. C-statistic from model used to derive propensity score = 0.620; C-statistic for adjusted risk association = 0.634.

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pharmacy

Article

Influence of Probiotics on the Development
of Clostridioides difficile Infection in Patients
Receiving Fluoroquinolones

Mary E. Sheffield 1,2,*, Bruce M. Jones 1,2,* , Blake Terrell 2, Jamie L. Wagner 3 and Christopher M. Bland 1,2

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Citation: Sheffield, M.E.; Jones, B.M.;

Terrell, B.; Wagner, J.L.; Bland, C.M.

Influence of Probiotics on the

Development of Clostridioides difficile

Infection in Patients Receiving

Fluoroquinolones. Pharmacy 2021, 9,

141. https://doi.org/10.3390/

pharmacy9030141

Academic Editor: Jon Schommer

Received: 1 July 2021

Accepted: 13 August 2021

Published: 18 August 2021

Publisher’s Note: MDPI stays neutral

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Copyright: © 2021 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

1 St. Joseph’s/Candler Health System, Savannah, GA 31405, USA; cmbland@uga.edu
2 Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy,

Savannah, GA 31324, USA; blake.terrell25@uga.edu
3 Department of Pharmacy Practice, University of Mississippi School of Pharmacy, Jackson, MS 39216, USA;

jwagner@umc.edu
* Correspondence: sheffiem@sjchs.org (M.E.S.); jonesbru@sjchs.org (B.M.J.); Tel.: +1-912-819-8556 (B.M.J.)

Abstract: Fluoroquinolones are associated with an increased risk of Clostridioides difficile infection
(CDI). Probiotic supplementation has been shown to reduce the risk of antibiotic-associated diarrhea
with variable effects on CDI. The objective of this study was to evaluate receipt of probiotics on
development of primary CDI among hospitalized patients receiving fluoroquinolones. A retro-
spective cohort was evaluated that consisted of two groups of 100 patients each, admitted August
2018 through August 2020 that received ≥3 days of definitive monotherapy with levofloxacin or
ciprofloxacin within 72 h of admission. Primary outcome was incidence of CDI. Secondary outcomes
included rates of C. difficile diagnostic stool testing, additional infectious diagnostic testing, and
non-CDI related gastrointestinal side effects. Patients on fluoroquinolones who received probiotics
had a non-statistically significantly lower incidence in overall cases of CDI compared to those who
did not receive probiotics (0% vs. 3%, p = 0.246). Patients who received probiotics had statistically
significantly fewer C. difficile diagnostic stool tests performed (4% vs. 16%, p = 0.005) and fewer
additional infectious diagnostic testing performed (4% vs. 10%, p = 0.096), respectively. Further
research is warranted to optimize and standardize probiotic prescribing in high-risk patients.

Keywords: antibiotic-associated diarrhea; CDI; ciprofloxacin; Clostridioides difficile; levofloxacin; probiotics

1. Introduction

Clostridioides difficile, previously Clostridium difficile, is a Gram-positive, anaerobic,
spore-forming bacterium responsible for C. difficile infection (CDI), including the devel-
opment of pseudomembranous colitis and toxic megacolon [1]. Complications include
severe diarrhea, dehydration, sepsis, and even death. In 2014, CDI became recognized
as the leading cause of hospital-acquired infections in the United States [2]. Primary pre-
vention of CDI is of interest due to the risk of recurrence (~20–25%) and associated health
consequences, including increased morbidity and mortality, hospital length of stay, and
healthcare costs [3].

The primary risk factor for development of CDI is antibiotic exposure. Risk varies
based on antibiotic classes and patient risk factors; however, the highest associated risk has
been identified with the use of clindamycin, fluoroquinolones, and ceftriaxone [1,4]. Fluoro-
quinolones are one of the most frequently and inappropriately prescribed antibiotic classes
in the United States. Recent data published by the CDC revealed that despite antimicrobial
stewardship efforts, 47% of inpatient fluoroquinolone use is inappropriate [5]. Addition-
ally, fluoroquinolone use has been associated with increased prevalence of the C. difficile
ribotype 027 strain, which has significantly higher rates of morbidity and mortality [1].

Probiotic supplementation has been shown to reduce the risk of antibiotic-associated
diarrhea and variable effects on primary CDI [6–10]. Due to conflicting and insufficient

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Pharmacy 2021, 9, 141 2 of 7

data, often due to poor quality studies, routine use is not recommended per the 2018
Infectious Diseases Society of America (IDSA) guidelines [11]. The biggest potential impact
for use in clinical practice could be among patients receiving high-risk antibiotics, but
data are limited. The aim of this study was to evaluate the receipt of probiotics on the
development of primary CDI among patients receiving fluoroquinolones compared to
those who did not receive probiotics.

2. Materials and Methods
2.1. Setting and Study Design

This multi-center, retrospective, observational cohort was conducted after institutional
review board approval from St. Joseph’s/Candler Health System, a 714-bed not-for-
profit, comprehensive, community health system consisting of two hospitals. A computer-
generated list identified admitted patients who received intravenous (IV) or oral lev-
ofloxacin or ciprofloxacin from 1 August 2018 to 31 August 2020. Ciprofloxacin and
levofloxacin were specifically evaluated from the fluoroquinolone class as they are the
main two formulary agents used within the health system. Included patients were ran-
domized into two groups of 100 patients, each based on concomitant use of at least one
dose of probiotic(s) during definitive therapy, versus those that did not receive probi-
otics. The two probiotics used during the study period were Saccharomyces boulardii and
a Lactobacillus spp. predominant blend of Lactobacillus acidophilus, Lactobacillus bulgaricus,
Bifidobacterium bifidum, and Streptococcus thermophiles (Risa-Bid®)(Rising Pharmaceuticals,
Allendale, NJ, USA). Probiotic selection varied based on product availability and provider
preference. Dosing regimens were not standardized and were up to the discretion of the
treating physician.

Patients were included if they were ≥18 years of age and received at least 3 days
of definitive monotherapy with IV or oral levofloxacin or ciprofloxacin within 72 h of
hospital admission. Patients were excluded if they had a documented history of prior CDI,
antibiotic use in the outpatient setting within 90 days of hospitalization, co-administration
of additional systemic antibiotics for more than 24 h during definitive therapy, immuno-
compromised, history of inflammatory bowel disease or irritable bowel syndrome, or
pregnancy. The primary outcome was the incidence of primary CDI, defined as symp-
tomatic patients with positive stool testing. Symptomatic was defined as three or more
episodes of diarrhea with an elevated white blood cell count (WBC) or fever, 7 or more
bowel movements, or 1.5 L of stool output over a 24-h period [11,12]. Secondary outcomes
evaluated were rates of C. difficile diagnostic stool testing performed, rates of additional
infectious diagnostic testing performed, and rates of non-C. difficile related gastrointestinal
(GI) side effects.

Within this institution, C. difficile stool testing can be ordered directly by physicians,
or via a nurse-driven protocol under which nurses may order testing within the first three
days of admission. The protocol requires three or more episodes of diarrhea in a 24 h
period and one additional symptom of CDI (WBC ≥ 15,000 K/mm3, temperature ≥ 38 ◦C,
loss of appetite, nausea, or abdominal pain or tenderness). Stool samples are processed
with a 2-step algorithm. A combined glutamate dehydrogenase (GDH)/Toxin A/B test is
completed first. A reflex polymerase chain reaction (PCR) test is performed if the GDH is
positive, but the toxin is negative.

2.2. Demographics and Patient Characteristics

Patient demographic data included age, sex, and race. Charlson Comorbidity Index
scores were calculated to compare confounding risk factors between both groups. Objective
laboratory values collected included WBC, C-reactive protein, erythrocyte sedimentation
rate, serum creatinine, procalcitonin, and albumin. Data regarding antibiotic and pro-
biotic administration, including product used, timing of initial doses, number of doses
administered, duration of therapy, and use of proton pump inhibitors (PPI) or histamine-2
receptor antagonists (H2RA) was also obtained. Additional infectious diagnostic testing

Pharmacy 2021, 9, 141 3 of 7

included GI PCR panel, stool culture, fecal fat test, fecal occult blood test, stool WBCs, or
repeat imaging.

2.3. Statistical Analysis

Data was collected in REDCap® (version 9.6.3, Vanderbilt University, Nashville, TN,
USA). Bivariate data were evaluated using chi-square test and Fisher’s exact test for
nominal data, as appropriate. Continuous data were evaluated using Mann–Whitney U
test. A two-sided alpha value of 0.05 was deemed statistically significant. All data were
analyzed using SPSS version 27.0 (IBM).

3. Results

There were 1104 patients who received IV or oral ciprofloxacin or levofloxacin who
were screened for inclusion to obtain 100 eligible patients in each group. Of the 1104
screened, 904 patients were excluded based on less than 3 days of IV or oral ciprofloxacin
or levofloxacin (46%), additional systemic antibiotics for more than 24 h during definitive
therapy (32%), and antibiotic use in the outpatient setting within 90 days of hospitalization
(9%). Patient demographics and clinical characteristics are summarized in Table 1. Between
both groups, the median patient age was 67 years, 62% were female, and the median
Charlson Comorbidity Index score was four. Use of levofloxacin versus ciprofloxacin was
consistent across both groups with a mean duration of seven days.

Table 1. Demographics and patient characteristics. † Median (interquartile range) unless otherwise noted. * Two patients
received doses of both probiotics. Abbreviations: PPI, proton pump inhibitor; H2RA, histamine-2 receptor antagonist; BID;
twice daily; TID, three times a day; FQ, fluoroquinolone.

Characteristic † Probiotic Use
(n = 100)

No Probiotic Use
(n = 100) p-Value

Age, years 68 (57–78) 64 (55–74.75) 0.120

Male, no. (%) 41 (41) 35 (35) 0.382

Race, no. (%)
Caucasian 72 (72) 57 (57) 0.027

Black 28 (28) 41 (41) 0.053
Hispanic 0 (0) 1 (1) 1.000

American Indian/Alaskan Native 0 (0) 1 (1) 1.000

Charlson comorbidity index 4 (2–5) 4 (2–5) 0.652

Definitive monotherapy, no. (%)
0.670Levofloxacin 53 (53) 56 (56)

Ciprofloxacin 47 (47) 44 (44)

Fluoroquinolone duration, days 7 (5–10) 7 (5–9) 0.277

PPI use, no. (%) 41 (41) 61 (61) 0.005

H2RA use, no. (%) 26 (26) 25 (25) 0.871

Prior antibiotic use, no. (%) 51 (51) 9 (9) <0.001

Probiotic use, no. (%) *
Lactobacillus 76 (76) –

Saccharomyces 22 (22) –

Probiotic frequency, no. (%)
Daily 30 (30) –
BID 24 (24) –
TID 44 (44) –

Other 2 (2) –

Duration of probiotics, days 6 (4–9) –

Time from the start of FQ to first
probiotic dose, days 0 (0,1) –

Higher percentage of PPI use in the non-probiotic group was observed (61% vs. 41%,
p = 0.005). Antibiotic use prior to switching to definitive therapy was 51% in the probiotics
group and 9% in the non-probiotic group (p < 0.001). Probiotic dosing or frequency has not
yet been standardized at our institution. However, most patients who received probiotics
were already receiving a daily probiotic or started probiotics at the start of fluoroquinolone

Pharmacy 2021, 9, 141 4 of 7

therapy. Of the patients receiving probiotics, 76% received Lactobacillus spp., 22% received
S. boulardii, and 2% received doses of both probiotics.

For the primary outcome, patients on fluoroquinolones who received probiotics had
a non-statistically significant lower incidence in overall cases of CDI compared to those
who did not receive probiotics (0% vs. 3%, p = 0.246). Regarding secondary outcomes,
patients who received probiotics had statistically significantly fewer C. difficile diagnostic
stool tests performed compared to those who did not receive probiotics (4% vs. 16%,
p = 0.005). Additionally, patients receiving probiotics had fewer additional infectious
diagnostic testing performed compared to those who did not receive probiotics (4% vs.
10%, p = 0.096) (Figure 1). Between both groups, approximately 70% of patients reported
no gastrointestinal-related side effects during their admission. Of the 30% and 35% of
patients who did experience side effects, vomiting was statistically significantly higher in
the non-probiotic group (9% vs. 2%, p = 0.030) (Table 2).

Figure 1. Rates of additional infectious diagnostic testing ordered. Abbreviations: GI, gastrointestinal; PCR, polymerase
chain reaction; FOBT, fecal occult blood test; WBC, white blood cell.

Table 2. Incidence of non-C. difficile related gastrointestinal side effects. † Abdominal pain (n = 1), constipation (n = 1).

Symptoms Probiotic Use
(n = 100)

No Probiotic Use
(n = 100) p-Value

No Symptoms 70 (70) 65 (65) 0.450
Nausea 11 (11) 13 (13) 0.663

Vomiting 2 (2) 9 (9) 0.030
Bloating 3 (3) 4 (4) 1.000

Gas 6 (6) 10 (10) 0.297
Non-CDI diarrhea 17 (17) 20 (20) 0.585

Other 0 (0) 2 (2) † 0.497

4. Discussion

Overall, this study provides further insight into the use of probiotics for primary
prevention of CDI in high-risk patients, for which there are currently limited data. Al-
though not statistically significant, zero cases of CDI were observed among patients on
fluoroquinolones who received probiotics. The rate of CDI observed in patients receiving
fluoroquinolones without probiotics is consistent with the current literature. The reported
incidence of hospital-onset CDI has been shown to be approximately 8 per 10,000 patient-
days [13,14]. Great interest surrounds the potential use of probiotics for prevention of
CDI. Not only is CDI associated with a significant increase in healthcare cost and hospital
length of stay, it is also a Center for Medicare and Medicaid Service’s (CMS) core measure.

Pharmacy 2021, 9, 141 5 of 7

In 2016, healthcare facility-onset CDI was added to the CMS hospital-acquired condition
reduction program. Hospitals that do not meet the 75-percentile cut off are subjected to
CMS reimbursement penalties, furthering the financial burden on these institutions [15].

The use of probiotics has been shown to have beneficial effects on the GI tract through
various proposed mechanisms, including restricting pathogenic growth by competing for
essential nutrients, inhibiting adhesion of C. difficile in the intestine, producing antimicrobial
metabolites, reducing osmotic diarrhea and restoring intestinal metabolic homeostasis [16,17].
Hudson et al. retrospectively evaluated prophylactic probiotic use in patients receiving broad-
spectrum antibiotics for at least 3 days. In the 2.5-year study period, 5574 hospital encounters
were reviewed and showed a C. difficile-associated diarrhea (CDAD) incidence rate of 0.96% in
patients who received probiotics compared to 2.19% in patients who did not receive probiotics
(p = 0.007; number needed to treat of 88) [6]. A 2018 meta-analysis and systematic review by
McFarland et al. evaluated three randomized controlled trials and seven observational studies,
and showed a reduced incidence rate of CDI with use of Bio-K+® for primary prevention
of CDI [7]. Shen et al. analyzed data from 19 studies and concluded that the initiation of
probiotics within two days of antibiotics reduced the risk of CDI by greater than 50%. A
greater risk reduction was evident when probiotics were given within two days of antibiotic
initiation compared to 3–7 days (p = 0.02) [8]. Johnson et al. evaluated 11 studies and overall
showed a protective effect against CDI (RR 0.39), especially with administration of Bio-K+®

probiotics compared to placebo (RR 0.21) [9]. Goldenberg et al. conducted a meta-analysis of
31 randomized controlled trials and concluded that probiotics significantly reduce CDI risk
compared to placebo with a number needed to treat of 12. Post hoc subgroup analyses showed
probiotics were only effective among trials with a > 5% baseline CDI risk [10].

These positive results were directly challenged with the publication of the PLACIDE
trial in 2013, the largest randomized control trial for this population to date. Allen et al.
failed to show a statistically significant benefit of probiotics in the prevention of antibiotic-
associated diarrhea (AAD) or CDI [18]. A recent multi-center trial by Heil et al. evaluated
the impact of a computerized clinical decision support tool to prescribe probiotics to high-
risk patients for primary prevention of CDI [19]. Over the 13-month post intervention
period, 2489 patients (16.6%) received probiotics and no difference in CDI was observed
compared to the pre-intervention period. A propensity-score match evaluation was per-
formed and patients who received probiotics did not have lower rates of CDI compared to
patients who did not receive probiotics (RR 1.46; 95% CI 0.87-2.45; p = 0.15). These results
from Heil et al. further demonstrate a lack of impact of probiotics on CDI primary pre-
vention. Variations in study designs, probiotic formulations, duration of therapy, baseline
rates of CDI, and inclusion of low-risk patient populations, provide additional challenges
when directly comparing between studies. To our knowledge, this is the first study to
examine the use of probiotics with fluoroquinolones specifically. The unique design of the
trial allowed us to investigate the impact of probiotics within a specific high-risk patient
population that has not been previously studied. With widespread use of fluoroquinolones,
we believe these results could potentially translate to other health systems.

Within our secondary outcomes, we did identify a statistically and clinically significant
decrease in C. difficile diagnostic stool testing as well as additional infectious diagnostic
testing. Reported symptoms overall trended down with probiotic use, with a statistically
significant decrease in vomiting. Together these secondary results imply that probiotic use
may reduce frequency and severity of AAD and GI side effects to a tolerable level, avoiding
additional and un-necessary patient work up. Utilization of nursing protocols featuring
broad, generalized symptoms like nausea and abdominal pain for CDI testing criteria
likely attribute to excess testing. While probiotics may or may not reduce the incidence of
primary CDI, there could be a potential pharmacoeconomic benefit by avoiding adverse
drug reactions and conserving hospital resources. These findings introduce a new and
meaningful outcome worth evaluating in CDI trials going forward.

Probiotic dosing and frequency have not yet been standardized at our institution.
Most patients who received probiotics were already receiving a daily probiotic as continu-

Pharmacy 2021, 9, 141 6 of 7

ation of a home medication or started probiotics at the start of fluoroquinolone therapy
based on provider discretion. Standardization of probiotic use is of interest among many
hospital institutions due to the potential cost savings benefit and improved CMS measure
compliance. However, the literature provides little guidance for standardization of use,
patient selection, or product choice. Additionally, implementation of new protocols may
prove to be difficult without more robust data. As shown in Heil et al., only 16.6% of
eligible patients received probiotics over the 13-month intervention period.

Several limitations are present within this study. The retrospective nature of this study
design limited the ability to control confounding factors amongst non-standardized use
of probiotics from prescribing physicians. There was a higher percentage of PPI use in
the non-probiotic group, which is a risk factor for CDI. However, patients in the probiotic
group had a much higher percentage of antibiotic use prior to switching to definitive
therapy. These confounding factors may have influenced the findings of this study. The
inclusion and exclusion criteria were chosen to focus the aim of this study on a specific
population with high risk and frequent use.

5. Conclusions

Although not statistically significant, zero cases of CDI were observed among patients
on fluoroquinolones who received probiotics. The role of probiotics for primary prevention
of CDI remains unclear. When administered to patients receiving fluoroquinolones, the
use of probiotics resulted in a statistically and clinically significant decrease in diagnostic
stool testing, without an increase in side effects. With the continued inappropriate and
excessive use of fluoroquinolones, the results of this trial provide important data in a
tangible target population. Further research is warranted to optimize and standardize
probiotic use specifically in high-risk patients.

Author Contributions: Conceptualization, M.E.S., B.M.J., J.L.W. and C.M.B.; methodology, M.E.S.,
B.M.J., J.L.W. and C.M.B.; software, M.E.S. and J.L.W.; validation, B.M.J., J.L.W. and C.M.B.; formal
analysis, J.L.W.; investigation, M.E.S. and B.T.; resources, B.M.J., J.L.W. and C.M.B.; data curation,
J.L.W.; writing—original draft preparation, M.E.S.; writing—review and editing, B.M.J., J.L.W. and
C.M.B.; visualization, M.E.S., B.M.J., J.L.W. and C.M.B.; supervision, B.M.J., J.L.W. and C.M.B.; project
administration, M.E.S., B.M.J. and C.M.B.; funding acquisition, none. All authors have read and
agreed to the published version of the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Institutional Review Board of St. Joseph’s/Candler
Health System (IRB# 20-028 on 20 October 2020).

Informed Consent Statement: Informed consent was waived by the Institutional Review Board
given the retrospective nature of the analysis, pursuant to 45 CFR 46.102(I). This study was deemed
to present minimal risk of harm to subjects and involved no procedures for which written consent is
normally required outside of the research.

Data Availability Statement: The data presented in this study are available upon request from the
corresponding authors.

Acknowledgments: Preliminary data was presented on 27 April 2021 at the virtual Georgia Society
of Health-System Pharmacists Spring meeting.

Conflicts of Interest: Bruce M. Jones is on the Speaker’s Bureau for Abbvie, Paratek, and LaJolla;
participates in consulting for Paratek, Merck, Melinta, and Abbvie; and has received grant funding
from ALK-Abello. Christopher M. Bland is on the Speaker’s Bureau for Merck, participates in
consulting for Merck, and has received grant funding from Merck. Mary E. Sheffield, Blake Terrell,
and Jamie L. Wagner declare that they have no conflict of interest.

Pharmacy 2021, 9, 141 7 of 7

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• Introduction
• Materials and Methods

Setting and Study Design

Demographics and Patient Characteristics

Statistical Analysis

• Results
• Discussion
• Conclusions

References

2123

Age and Ageing 2021; 50: 2123–

2132

https://doi.org/10.1093/ageing/afab169
Published electronically 28 August 2021

© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics
Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

RESEARCH PAPER

Reduced Clostridioides difficile infections in
hospitalised older people through multiple
quality improvement strategies
Carla Maria Dohrendorf1,2,†, Steffen Unkel3,†, Simone Scheithauer4, Martin Kaase4,
Volker Meier5, Diana Fenz4, Jürgen Sasse6, Manfred Wappler7, Jutta Schweer-Herzig7,
Tim Friede3, Utz Reichard8, Helmut Eiffert8, Roland Nau1,2, Jana Seele1,2

1Department of Geriatrics, Evangelisches Krankenhaus Göttingen-Weende, Göttingen, Germany
2Department of Neuropathology, University Medical Center Göttingen, Göttingen, Germany
3Department of Medical Statistics, University Medical Center Göttingen; Göttingen, Germany
4Institute for Infection Control and Infectious Diseases, University Medical Center Göttingen; Göttingen, Germany
5Hospital hygiene, Evangelisches Krankenhaus Göttingen-Weende; Göttingen, Germany
6Clinic for Geriatric Medicine, DRK Kliniken-Nordhessen; Kaufungen, Germany
7Clinic for Geriatric Medicine, Evangelisches Krankenhaus Gesundbrunnen Hofgeismar; Hofgeismar, Germany
8MVZ Wagnerstibbe for Medical Microbiology, Göttingen, Germany

Address correspondence to: Dr. Jana Seele, Department of Neuropathology, University Medical Center Göttingen, Göttingen,
Germany & Department of Geriatrics, Evangelisches Krankenhaus Göttingen-Weende, Göttingen, Germany, Robert-Koch-Str. 40,
37075 Göttingen, Germany; Tel: +49-551-39-20489; Fax: +49-551-39-10800. Email: jana.seele@med.uni-goettingen.de
†Contributed equally

Abstract
Objectives: To reduce infections with Clostridioides difficile (CDI) in geriatric patients by interventions easily implementable
in standard clinical care.
Methods: Prevalence and incidence of CDI between January 2015 and February 2020 were analysed (n = 25,311 patients).
Pre-intervention status was assessed from April 2016 to March 2017 (n = 4,922). Between May 2017 and August 2019, a
monocentric interventional crossover study (n = 4,655) was conducted including standard care and three interventions: (A)
sporicidal cleaning of hospital wards, (B) probiotics and (C) improvement in personal hygiene for CDI patients. This was
followed by a multicentric comparison of the interventional bundle (A + B + C) between September 2019 and February
2020 (n = 2,593) with the pre-intervention phase. In 98 CDI cases and matched controls individual risk factors for the
development of CDI were compared.
Results: Time series analyses of CDI cases revealed a reduction in the prevalence of CDI in all three participating centres prior
to the multicentric intervention phase. In the monocentric phase, no effect of individual interventions on CDI prevalence was
identified. However, an aggregated analysis of CDI cases comparing the pre-intervention and the multicentric phase revealed a
significant reduction in CDI prevalence. Risk factors for the development of CDI included use of antibiotics, anticoagulants,
previous stay in long-term care facilities, prior hospital admissions, cardiac and renal failure, malnutrition and anaemia.
Conclusions: The observed reduction in CDI may be attributed to heightened awareness of the study objectives and specific
staff training. Individual interventions did not appear to reduce CDI prevalence. A further randomised trial would be necessary
to confirm whether the bundle of interventions is truly effective.

Keywords: Clostridioides difficile infections,reduction of spores,probiotics, interventional study,geriatric patients,older people

Key Points
• Reduction of infections with toxigenic Clostridioides difficile in geriatric patients.
• Conduction of a monocentric interventional cross-over study followed by a multicentric evaluation of the interventional

bundle.
• Combination of the reduction of spores in the environment and strengthening of the patients’ gut flora.

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https://doi.org/10.1093/ageing/afab169

C. M. Dohrendorf et al.

Introduction

Infections with Clostridioides (C.) difficile (CDI) are among
the leading causes of nosocomial infections [1]. Transmission
occurs primarily by ingestion of C. difficile spores shed in
vast quantities with the stools by infected and colonised indi-
viduals. Spores can be found in the environment of affected
patients and are easily distributed via the hands of healthcare
providers because of their resistance to conventional alcohol-
based disinfectants [2].

Depending on immunocompromising factors, perturba-
tions in the gut microbiome, the C. difficile strain involved
and the ingested dose, an exposure to C. difficile can result
in asymptomatic colonisation or lead to an infection, whose
clinical presentation ranges from mild diarrhoea to toxic
mega-colon, sepsis and death [3].

Geriatric inpatients are particularly vulnerable as many
known risk factors for the development of CDI pertain to
them: old age, exposure to antibiotics, long hospitalisation
duration, stay in long-term care facilities and severe
comorbidities such as chronic renal failure or malnutrition
[4]. The risk of infection among persons ≥65 years
was 8.65 times higher than the risk among patients
<65 years (95% confidence interval [CI] 8.16 to 9.31).
Over 80% of CDI deaths occur in patients ≥65 years
[1, 5].

There are several different approaches to prevent CDI.
One is to limit exposure to C. difficile spores, e.g. by routine
use of sporicidal agents for surface cleaning or by a daily
change of hospital bed linen [6, 7]. Despite current standard
infection control measures that focus on the isolation of
infected patients, C. difficile spores can still be found in ward
environments [6–8]. They are at least in part presumed to
stem from asymptomatic carriers. In a study based on a small
number of patients, 84% of nosocomial CDI appeared to
be caused by strains introduced by asymptomatic carriers
[9]. Other preventive approaches concentrate on the role of
the microbiome: probiotics have shown promising results in
the prevention of CDI, especially in geriatric wards, with
reductions in CDI incidence of 61–66% as reported in three
meta-analyses [10–12].

In the present study, we aimed to develop and evaluate the
effect of interventions to reduce infections with C. difficile
that are easily implemented in geriatric standard hospital
care. These included the reduction of spores in the ward
environment, the use of probiotics and improvement in the
personal hygiene of CDI patients.

Methods

Study design and setting

The incidence [(CDI cases/occupancy days)∗1,000] and
prevalence [(CDI cases/total cases)∗100] of all CDI and of
nosocomial CDI were monitored in the three participating
centres [Geriatrische Klinik, Evangelisches Krankenhaus
Göttingen-Weende (EKW), Klinik für Geriatrie, DRK

Kliniken-Nordhessen Kassel (DRKK), Klinik für Geriatrie,
Evangelisches Krankenhaus Gesundbrunnen Hofgeismar
(EKH)] from January 2015 to February 2020 (last month
included). The development of symptoms of a CDI within
48 h of admission to a geriatric ward was defined as a case
acquired outside the geriatric departments and >48 h after
admission as a nosocomial case. From May 2017 to August
2019 a monocentric four-armed interventional effectiveness
study with crossover design was conducted at the EKW. The
following interventions were implemented in four different
hospital wards of the EKW: (group A) cleaning of all surfaces
in all patients‘ rooms (Cleanisept� Wipes Forte containing
benzalkonium chloride and didecyldimethylammonium
chloride, Dr. Schumacher, Malsfeld, Germany), all hand
contact surfaces (Cleanisept� Wipes Forte) and floors
(Ultrasol� active containing peracetic acid, Dr. Schumacher,
Malsfeld, Germany) with a sporicidal disinfectant; (group
B) daily provision of probiotics (Actimel� containing
Streptococcus thermophilus, Lactobacillus bulgaricus and
Lactobacillus casei, Danone Germany, München-Haar,
Germany) for all patients; and (group C) improvement
in personal hygiene of patients with symptomatic CDI
including a sporicidal laundry service for the personal
clothes and daily change of the bedding. These interventions
were carried out in addition to the standard hospital care
which included isolation of patients with a symptomatic
infection and sporicidal cleaning of the infected patient’s
room once per day. The fourth ward (group D, standard
hospital care) served as a control. Every ward comprised
19–25 beds and went through every intervention for
6 months and 3 weeks with 1 week wash-out in between.
The interventions were randomly allocated to the wards
(Figure 1). In the multicentric phase, a bundle comprising all
three interventions in addition to the standard hospital care
was implemented in the geriatric wards of EKW (4), EKH
(3) and DRKK (3) in a total of 294 beds from September
2019 to February 2020 (Figure 1).

Twice per week the study nurse and/or study coordinator
spoke with the hospital staff (including nurses, physicians
and cleaning staff) about the study concept and implementa-
tion of interventions as well as to listen closely to their needs
and concerns.

Endpoints of the monocentric phase were the influence
of the interventions A, B and C compared to standard
hospital care on the infection rate and the dependence of the
CDI rate on (1) the immune status of the patient, (2) the
duration of the hospital stay, (3) the medication, especially
the use of antibiotics and (4) comorbidities and underlying
diseases.

The endpoint of the multicentric phase was the CDI
prevalence during the multicentric phase (September 2019–
February 2020) compared to a 1-year interval prior to the
start of the monocentric phase (April 2016–March 2017).
All endpoints of both the monocentric and the multicentric
phase were evaluated on total CDI and nosocomial CDI as
defined previously.

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Reduced Clostridioides difficile infections in hospitalised older people

Figure 1. Study design and implemented interventions. (A) The incidence and prevalence of CDI was calculated from January 2015
to February 2020. The monocentric phase of the study was implemented from May 2017 to August 2019 in the geriatric centre of
EKW. The interventions were carried out for 6 months and 3 weeks with 1 week wash-out following. Afterwards the interventions
rotated on the wards. The standard hospital care served as control. During the multicentric phase from September 2019 to February
2020 the bundle of the interventions A, B and C in addition to the standard hospital care was implemented in the geriatric centres
of EKW, EKH and DRKK. (B) The interventions served to (A) reduce C. difficile and its spores in the hospital environment, (B)
strengthen the gut microbiome of all patients and (C) improve the personal hygiene of patients with a symptomatic CDI in addition
to (D), the standard hospital care.

Participants

Pre-intervention status was assessed from April 2016 to
March 2017 (4,922 patients). The monocentric phase
included 4,655 inpatients admitted to the geriatric depart-
ment of the EKW between May 2017 and September 2019;
in the multicentric phase 2,593 inpatients in the geriatric
departments of EKW, EKH and DRKK between September
2019 and February 2020 were analysed.

For change point analyses, CDI cases from January 2015
to February 2020 were analysed (25,311 patients).

Microbiology

Stool samples of symptomatic patients were analysed by an
enzyme immunoassay (EIA) detecting Clostridoides-specific
glutamate dehydrogenase (GDH) to prove the presence of
C. difficile. Then, the bacterial DNA encoding Toxin B was
amplified by polymerase chain reaction (PCR) to identify
toxigenic strains.

Case definition and data collection

A CDI case was defined by (i) unformed stools and (ii)
positive results for GDH EIA and Toxin B PCR. CDI cases
were identified by the routine hospital infection surveillance
systems. Only the patient’s first episode during the study
period was recorded as a new case. CDI was considered
severe, if the patient died, was transferred to an ICU or
received colonic surgery with CDI as a plausible cause based
on the medical records.

Data of CDI patients were compared with data of control
patients by matched-pairs analysis. For the control group,
patients without CDI or known C. difficile colonisation who

had stayed at the EKW during the monocentric phase for
at least 2 days were selected in a 1:1 matching with respect
to age and sex. Laboratory data, medication during hospital
stay, demographics and comorbidities were collected using
the medical records. Laboratory results were collected at
admission to the geriatric ward (or from the sample closest to
this time point; t0). As a second blood analysis in CDI cases
the measurements obtained closest to the C. difficile-positive
stool sampling were used (t1). For control patients, t1 was
set at 12 days after admission (or the latest results available,
if the stay was shorter than 12 days). This was based on a pre-
evaluation of CDI patients from 2016 in which the median
of the interval admission – CDI was 12 days.

Ethics

This study was approved by the Ethics Committee of
the University Medical Centre Göttingen, Georg-August-
University Göttingen (application number 22/1/17).
Individual informed consent was waived since all measures
implemented in this study aimed to maintain and improve
the quality of patient care. Upon admission, all patients gave
consent that their data can be used for research purposes by
staff subjected to medical confidentiality. A data protection
declaration was signed by all project members.

Statistical power calculation and data analysis

A full survey was carried out on all wards of the EKW
(phase 1) and in all participating centres (phase 2). Statistical
power calculation based on the pre-defined endpoints was
performed prior to the beginning of the study under the
following assumptions. Monocentric phase (phase 1): on
average, 225 patients will be treated on each ward of the

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C. M. Dohrendorf et al.

EKW during one intervention period of 6 months plus
3 weeks (1 week wash-out period), i.e. after four rotations,
900 patients are treated with intervention A, B, C and stan-
dard care (D). Compared with the pre-intervention status,
the number of CDI cases will be reduced by each interven-
tion (A, B, C) by 25%. These assumptions led to an estimated
power of >80%. Multicentric phase (phase 2): a total of
2,700 patients will be treated in the centres participating in
the 6-months period of the bundle intervention. Without
interventions, a total number of 90 CDI cases were assumed
to occur. The number of CDI cases will be reduced by 50%
during the multicentric phase. These assumptions led to an
estimated power of >95%.

Univariate analysis of individual risk factors was con-
ducted that compared CDI patients with their matched
controls, and a subgroup analysis was performed, including
only nosocomial cases and their respective control patients.
Dichotomous variables were compared by using Fisher’s
exact test. Categorical variables (with more than two cat-
egories on one variable) were compared using Wilcoxon’s
signed rank test after being ranked according to the severity
of the impairment of the function studied. Continuous
variables were compared using Wilcoxon’s signed rank test
for matched pairs, and if matching was not feasible by
Mann–Whitney U test. Change point analyses were carried
out to detect the point at which a structural break (change
in the normally distributed mean) of the times series of
monthly CDI prevalences and incidences occurred [13].
The results of the change point analyses were visualised in
a similar fashion as in statistical process control, in which
control charts are used to distinguish between common and
special causes of variation. Control charts typically include
a plot of the data over time, and one or more additional
lines, which e.g. represent the means of the time series, or
indicate when a signal of special cause variation has occurred
[14].

A two-tailed P value ≤ 0.05 was considered statisti-
cally significant. Statistical analyses were conducted using
R version 4.0.1. [15] and GraphPad Prism, Version 6.01
(2012).

Results

Change point analyses of CDI prevalence and
incidence

In the present study the effect of interventions to reduce
infections with C. difficile that are easily implementable
into geriatric standard hospital care were evaluated. These
included (A) the reduction of spores in the ward, (B) the
use of probiotics and (C) improvement in personal hygiene
of CDI patients. The prevalence and incidence of CDI cases
were analysed from January 2015 to February 2020, i.e. from
16 months prior to the start of the monocentric phase up
to the end of the multicentric phase of the study. Change
point analyses revealed that the prevalence of nosocomial and
total (nosocomial, i.e. acquired in the geriatric department,

plus externally acquired) CDI cases significantly dropped
at the Departments of Geriatrics of the EKW between
March and April 2015, at the EKH between March and
April 2017 and at the DRKK between April and May 2016
(Figure 2). A change point in regard to the incidence of CDI
cases was only detectable for total CDI cases at the EKW
(Supplementary Figure 1).

Influence of the individual interventions on CDI
infection rate (monocentric phase)

During the monocentric phase of the study, the interven-
tions A–D were carried out as individual interventions on
four different geriatric wards of the EKW from May 2017
to August 2019 with a rotation of the interventions after
6 months and 3 weeks, each separated by 1 week of wash-out.
An analysis of CDI cases at the end of the monocentric phase
showed that none of the experimental interventions A, B or
C (total CDI incidence A: 1.37, B: 1.99, C: 1.33; nosocomial
CDI incidence A: 1.20, B: 1.29, C: 1.04) reduced the
CDI infection rate compared to standard hospital care D
(total CDI incidence 0.86, nosocomial CDI incidence 0.59).
Because of differences in the composition of the patients
treated on the individual wards, their CDI incidence was
not equal. Total CDI incidence on the individual wards
ranged from 1.02 to 1.97, and nosocomial CDI incidence
from 0.91 to 1.42 illustrating the necessity of the chosen
study design (allocation of the sequence of interventions
to the wards randomly, every ward went through every
intervention).

Individual risk factors for the development of CDI

During the monocentric phase of the study, data of 98
matched pairs (CDI vs. control) were analysed for individual
risk factors for the development of CDI (Tables 1 and 2,
Supplementary Table 1). The median age in both groups was
82 years. Of the 98 CDI cases, 88.8% were nosocomial
infections, 11.2% were recurrent episodes and 4.3% met
criteria of severe CDI. Median length of stay at the geriatric
ward was 6.5 days longer for CDI patients, and they were
discharged with a worse outcome. CDI was significantly
associated with prior hospital stays, previous residency in a
long-term care facility, invasive feeding and worse scores in
geriatric assessments including the Charlson Comorbidity
Index (CCI). CDI patients suffered more often from cardiac
failure, malnutrition, anaemia, hypothyroidism, renal failure
and dialysis, while Parkinson’s disease and conservatively
treated fractures were more frequent in the control group.

CDI patients showed higher levels of serum immunoglob-
ulin A (IgA), infection parameters (both at time of admission
and sampling) and plasma alanine transaminase, whereas
total plasma calcium was lower as a consequence of lower
total protein and albumin concentrations. When plasma
calcium was corrected for albumin using Payne’s formula
[16], calcium was not lower in CDI than in the respective
control patients. Plasma potassium levels fell in CDI patients

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Reduced Clostridioides difficile infections in hospitalised older people

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Figure 2. Change point analysis of nosocomial (A, B, C) and total (D, E, F) CDI prevalence from January 2015 (month 0) to
February 2020 (month 60) in the geriatric centres of EKW (A, D), EKH (B, E) and DRKK (C, F). The prevalence per month was
calculated as CDI cases/total cases∗100.

from admission to time of stool sampling, unsurprisingly
during a diarrhoeal illness.

Medication associated with CDI were anticoagulants,
loop diuretics, antiepileptics, antimotility agents, and signif-
icant only for nosocomial CDI, benzodiazepines. Calcium
channel blockers were prescribed more often in the control
group. The use of proton pump inhibitors and immuno-
suppressants was not significantly different. The number of
total prescriptions during the geriatric stay was higher in the
nosocomial CDI group.

Further risk factors for development of CDI included the
use of antibiotics, higher numbers of different antibiotics and

more days of antibiotic use. Odds ratios (OR) were similar
for the different routes of administration.

Influence of the intervention bundle on CDI
prevalence (multicentric phase)

As individual interventions during the monocentric phase
did not reduce the incidence of CDI, we decided to com-
bine all interventions in addition to the standard hospital
care during the multicentric phase in the three participat-
ing geriatric centres for 6 months. An aggregated analysis
of the nosocomial and total (nosocomial plus ambulant)

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Reduced Clostridioides difficile infections in hospitalised older people

Table 2. Use of antibiotics and other medication in CDI cases and controls
CDI vs. controls Nosocomial CDI vs. controls

Variable CDI, No. (%)
(N = 98)

controls, No. (%)
(N = 98)

OR P value Nosocomial CDI,
No. (%)
(N = 87)

controls, No. (%)
(N = 87)

OR P value

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Antibiotics 74 (76%) 50 (51%) 2.96 0.0006∗ 68 (78%) 46 (53%) 3.19 0.0007∗
Aminopenicillins 48 (49%) 23 (23%) 3.13 0.0003∗ 43 (49%) 21 (24%) 3.07 0.0009∗
Piperacillin/tazobactam 32 (33%) 12 (12%) 3.47 0.001∗ 31 (36%) 11 (13%) 3.82 0.0006∗
Linezolid 6 (6%) 0 (0%) n.a. 0.029∗ 6 (7%) 0 (0%) n.a. 0.029∗
Days with antibiotic use,
median (IQR)

6 (0.75; 12) 1 (0; 6) <0.0001† 8 (1; 12) 1 (0; 6) <0.0001†

Number of antibiotics used,
median (IQR)

1 (0.75; 3) 1 (0; 1.25) <0.0001† 2 (1; 3) 1 (0; 2) <0.0001†

Anticoagulants 57 (58%) 35 (36%) 2.50 0.003∗ 55 (63%) 35 (40%) 2.55 0.004∗
low molecular weight
heparin

19 (19%) 7 (7%) 3.13 0.019∗ 19 (22%) 7 (8%) 3.19 0.018∗

unfractionated heparin 8 (8%) 1 (1%) 8.62 0.035∗ 8 (9%) 1 (1%) 8.71 0.034∗
Loop diuretics 61 (62%) 48 (49%) 1.72 0.084∗ 58 (67%) 44 (51%) 1.95 0.045∗
Calcium channel blockers 25 (26%) 41 (42%) 0.48 0.023∗ 24 (28%) 38 (44%) 0.49 0.039∗
Benzodiazepines 14 (14%) 7 (7%) 2.17 0.165∗ 14 (16%) 4 (5%) 3.98 0.023∗
Antiepileptics 23 (23%) 11 (11%) 2.43 0.037∗ 20 (23%) 10 (11%) 2.30 0.070∗
Number of total
prescriptions, median (IQR)

9 (7; 12) 9 (6; 11) 0.145 10 (7; 12) 9 (6; 11) 0.029†

OR = Odds ratio, IQR = Interquartile range ∗P values calculated with Fisher exact test †P values calculated with Wilcoxon signed-rank test

CDI cases of all three participating centres comparing the
1-year interval before the start of the study (April 2016
until March 2017) with the 6-month interval of the mul-
ticentric phase (from September 2019 to February 2020)
revealed a significant reduction in CDI cases during the
study (nosocomial: OR 0.60, 95% CI 0.40–0.90; total: OR
0.56, 95% CI 0.39–0.81; Figure 3). When analysing each
centre separately, the reduction in the odds of infection were
statistically significant in EKH (nosocomial: OR 0.53, 95%
CI 0.25–1.11; total: OR 0.40, 95% CI 0.20–0.79) and
DRKK (nosocomial: OR 0.39, 95% CI 0.16–0.95; total:
OR 0.41, 95% CI 0.18–0.94), whereas in EKW it failed to
reach statistical significance (nosocomial: OR 0.83, 95% CI
0.46–1.50; total: OR 0.84, 95% CI 0.49–1.42; Figure 3).

Discussion

In this study, change point analyses and pre-to-post inter-
vention comparison revealed that the total number as well
as number of nosocomial CDI cases declined in the partic-
ipating centres. No single intervention in the monocentric
phase was found to be effective, meaning that in respect to
the a priori defined endpoint of the monocentric phase of
this study, each intervention by itself was not found to reduce
CDI prevalence. However, the bundle of interventions in
combination with the associated staff awareness and educa-
tion appeared to be effective in the multicentric prior-post
comparison.

The change point analyses suggested that the heightened
awareness through the study objectives accompanied by
discussions and staff training was key to the reduction in

Figure 3. Analysis of nosocomial and total CDI cases in the
three participating centres. The CDI prevalence prior the study
(April 2016–March 2017) was compared with the CDI preva-
lence during the multicentric phase of the study (09/2019–
02/2020). The Odds ratio was calculated for each centre and
in an aggregated analysis for all centres. Odds ratio and 95%
confidence intervals are shown.

CDI incidence. This is similar to a situation in Great Britain
in 2008, where as a consequence of the high incidence of
CDI, a national target was set for a 30% reduction in CDI by
2010–11, which was achieved in spite of very heterogeneous
approaches towards CDI prevention [17].

Conceivable reasons for the failure of the monocentric
phase of this study were (a) the lack of effectivity of the

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individual interventions, or (b) insufficient adherence to the
intervention protocols by the participating staff or patients,
or both.

As to (a): The evidence for the effectivity of some indi-
vidual interventions was fairly low: chlorine or oxygen-based
daily and terminal cleaning to reduce the concentration of
C. difficile spores was of all interventions most likely to
reduce CDI [18]. Installing a laundry service for infected
patients was supported by only few observations [6, 7]. The
use of probiotic yoghurt was supported by more studies [11,
19–22]. In studies with a baseline CDAD risk of 0 to 2%
and 3 to 5%, probiotics were found to be ineffective, but
studies including patients with a baseline risk of >5% for
developing CDAD showed a risk reduction of approximately
70% [11]. In all participating centres the baseline risk was
<5%. In regard to the probiotics, the effect was not always
reproducible [23], and as a consequence of strong economic
interests a publication bias in favour of positive studies is
suspected.

(b) The staff compliance varied: achieving compliance
with appropriate cleaning technique was difficult, because
depending on the institution and also on the composition
of the flooring, staff and patients complained of bad odour
and mucosal irritation. As a consequence, the areas cleaned
by chlorine bleach had to be reduced in all centres during
the multicentric study. Probiotic yoghurt was not distributed
daily on all wards as a consequence of work overload or
prejudices on the part of the staff in regard to the inter-
ventions. Moreover, the laundry service was not propagated
rigorously enough. The compliance of the patients depended
on this measure: The laundry service was rejected by many
patients because of the fear of losing their clothing. In total,
in EKW 10% of the patients accepted the laundry service.
In EKH and DRKK, it was not accepted by the patients.
The daily distribution of probiotic yoghurt was the measure
most readily accepted by all patients. On an average, 81% of
the patients received and accepted the probiotic yoghurt in
all participating centres. The supply of a probiotic yoghurt
will be continued in EKW. Especially for geriatric patients
who often suffer from swallowing disorders, yoghurt is an
appropriate snack.

Change point analyses of the entire study period revealed
that all centres reduced the prevalence of CDI infections.
Bundled interventions of different compositions are con-
sidered highly effective [18]. After we had noticed that no
individual measure appeared to be effective in the monocen-
tric study, we decided to use all interventions as a bundle.
The bundle of interventions was effective, when all centres
were analysed together, and in two of three centres, when
each centre was analysed separately. This discrepancy is a
consequence of the different change points: in EKW the
change point already occurred in March 2015, i.e. prior to
the control period of the multicentric study, whereas in EKH
and DRKK the change points occurred during the control
period of the multicentric study. No further change point
occurred at the beginning of the multicentric phase, i.e. the
bundle intervention. Due to the different times when the

change occurred, we hypothesise that the time of the change
point depended on the awareness of the problem of frequent
CDI infections by the heads of the institutions and the
educational measures taken thereafter. In EKW, the change
point corresponded to the start of the planning of this project
including discussions among the staff about correct hygiene
measures. In DRKK the change point was associated with
efforts of the chief physician (J.Sa.) to implement rational
antibiotic therapy. In EKH, the change point was close to
joint meetings in preparation of the multicentric phase of
this study. In particular, the colleagues of this institution
were impressed by a lecture of M.K. on the pathophysiol-
ogy, molecular biology and epidemiology of CDI. In this
respect, the results of the present study resemble experiences
published concerning the prevention of overwhelming post-
splenectomy infection (OPSI) where a bundle of measures
involving vaccination, antibiotic prophylaxis and patient
education can prevent infections, although the effectiveness
of some of the individual interventions remains unclear [24].

In general, CDI incidences and prevalences were com-
parable to other clinical studies [25]. In the present study,
CDI was defined by unformed stools and positive results for
GDH EIA and Toxin B PCR. In older patients, infections
may present with mild symptoms, which may have led to a
slight underestimation of the true incidence [2]. Individual
risk factors of CDI were similar in the present study as in
previous investigations: We confirmed the main risk fac-
tors, previous use of antibiotics, renal failure, malnutrition,
but not previous ICU stay, proton pump inhibitor and
immunosuppressant treatment. Since we matched for age,
we could not reproduce this well-known fact [26]. The effect
of stay in long-term care facilities, lower functional status and
comorbidities may be mediated by several factors. Frequent
contact with healthcare facilities entails increased exposure
to C. difficile spores [27]. Impairment of the immune sys-
tem (e.g. by malnutrition or renal failure) facilitates the
development of CDI directly or may result in other infec-
tions demanding antibiotic therapy thereby disbalancing
the gut microbiome. Anticoagulation was associated with
an increased risk of developing CDI and nosocomial CDI.
Approximately 40% of the patients at our institution receive
anticoagulants mostly due to nonvalvular atrial fibrillation or
flutter (AF) [28]. AF is associated with a variety of diseases
and with an increased mortality (odds ratio approx. 2.5)
[29]. Therefore, we hypothesise that in the present study
anticoagulated patients had a higher burden of diseases than
patients receiving no anticoagulation, rendering them more
susceptible to CDI. The median CCI of anticoagulated CDI-
infected patients was 4, whereas the median CCI of CDI-
infected not anticoagulated patients was 3, and the CCI of
the respective control patients in both subgroups was 2.

Our study has several limitations. It consisted of an obser-
vational and a randomised intervention part. In the ran-
domised intervention part (monocentric phase), no reduc-
tion in CDI was achieved. For the observational part, our
study succeeded in reducing C. difficile infections in the par-
ticipating departments. In line with the majority of the other

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Reduced Clostridioides difficile infections in hospitalised older people

studies on CDI in acute care hospitals, for the multicentric
study we used a simple pre- and post-intervention design.
The use of a step-wedge design might have improved the
quality of the data [18].

Conclusion

In conclusion, the observed reduction in CDI may be
attributed to a heightened awareness of the study objectives,
as well as to specific staff training. The individual interven-
tions did not appear to reduce CDI prevalence. The bundle
of interventions and the accompanying staff training reduced
the high incidence and prevalence of CDI by approx. 40%,
whereas the individual measures appeared to be ineffective.
The results of the present study could be characterised by the
proverb ‘A danger foreseen is half avoided’ [30]. It remains
open whether the bundle of interventions was truly effective
or whether the reduction in CDI incidence primarily was
achieved by an increased awareness of the problem and
by training measures accompanying the implementation of
this study. Therefore, the promising effects of the combined
intervention would need to be confirmed in a future cluster-
randomised multicentre trial in which the wards represent
the clusters.

Supplementary Data: Supplementary data mentioned in
the text are available to subscribers in Age and Ageing online.

Acknowledgements: We thank Cynthia Bunker for project
administration and careful language editing.

Declaration of Sources of Funding: This study was funded
by Gemeinsamer Bundesausschuss, Innovationsausschuss,
project number: 01VSF16059. The funding bodies did not
influence the design of the study, collection, analysis, and
interpretation of data and manuscript writing.

Declaration of Conflicts of Interest: SS and TF report
other grants from Gemeinsamer Bundesausschuss (G-
BA), Innovationsausschuss. TF received personal fees
from Novartis, Bayer, Janssen, SGS, Roche, Boehringer
Ingelheim, Daiichi-Sankyo, Galapagos, Penumbra, Parexel,
Vifor, BiosenseWebster, CSL Behring, Fresenius Kabi,
Coherex Medical, LivaNova, all outside the submitted work.
RN received honoraria for lectures from Bayer Vital, Pfizer,
Bristol-Myers Squibb and Desitin, and research support
from Novartis, B. Braun Foundation, Deutsche Gesellschaft
für Geriatrie, and Strathmann GmbH, all unrelated to the
submitted work. The other authors declare that they have no
conflicts of interest.

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• Reduced Clostridioides difficile infections in hospitalised older people through multiple quality improvement strategies

Introduction

Methods

Results

Discussion

Conclusion

6 Supplementary Data:

7 Acknowledgements:

8 Declaration of Sources of Funding:

9 Declaration of Conflicts of Interest:

American Journal of Infection Control 47 (2019) 2�8

Contents lists available at ScienceDirect

American Journal of Infection Control

journal homepage: www.aj ic journal .org

Major Article

Predictors of Clostridium difficile infection and predictive impact of
probiotic use in a diverse hospital-wide cohort

D1X XMartha L. Carvour D2X XMD, PhD a,b,*, D3X XShane L. Wilder D4X XBS c, D5X XKeenan L. Ryan D6X XPharmD, PhC d,
D7X XCarla Walraven D8X XPharmD, BCPS-AQ ID d, D9X XFares Qeadan D10X XPhD a, D11X XMeghan Brett D12X XMDb,
D13X XKimberly Page D14X XPhD, MPH, MS a

a Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico, Albuquerque, NM
b Division of Infectious Diseases, Department of Internal Medicine, University of New Mexico, Albuquerque, NM
c University of New Mexico School of Medicine, Albuquerque, NM
d Department of Pharmacy, University of New Mexico Hospital, Albuquerque, NM

Key Words:

* Address correspondence to Martha L. Carvour, MD,
Medicine, University of New Mexico Health Sciences Cen
ico, MSC 10-5550, Albuquerque, NM 87131.

E-mail address:MCarvour@salud.unm.edu (M.L. Carv
Funding/support: Supported by the Infectious Diseas

tion Medical Scholars Program and the University of New
tional Sciences Center (National Institutes of Health gran

Conflicts of interest: None to report.

https://doi.org/10.1016/j.ajic.2018.07.014
0196-6553/© 2018 Association for Professionals in Infect

Background: Hospital-based predictive models for Clostridium difficile infection (CDI) may aid with surveil-
lance efforts.
Methods: A retrospective cohort of adult hospitalized patients who were tested for CDI between May 1, 2011,
and August 31, 2016, was formed. Proposed clinical and sociodemographic predictors of CDI were evaluated
using multivariable predictive logistic regression modeling.
Results: In a cohort of 5,209 patients, including 1,092 CDI cases, emergency department location
(adjusted odds ratio [aOR], 1.91; 95% confidence interval [CI], 1.51, 2.41; compared with an intensive
care unit reference category, which had the lowest observed odds in the study) and prior exposure to a
statin (aOR, 1.26, 95% CI, 1.06, 1.51), probiotic (aOR, 1.39; 95% CI, 1.08, 1.80), or high-risk antibiotic (aOR,
1.54; 95% CI, 1.29, 1.84), such as a cephalosporin, a quinolone, or clindamycin, were independent predic-
tors of CDI. Probiotic use did not appear to attenuate the odds of CDI in patients exposed to high-risk
antibiotics, but moderate-risk antibiotics appeared to significantly attenuate the odds of CDI in patients
who received probiotics.
Conclusions: Emergency department location, high-risk antibiotics, probiotics, and statins were indepen-
dently predictive of CDI. Further exploration of the relationship between probiotics and CDI, especially in
diverse patient populations, is warranted.
© 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc.. All

rights reserved.

Clostridium difficile
Health care�associated infection
Hospital epidemiology
Probiotics

PhD, Department of Internal
ter, 1 University of New Mex-

our).
es Society of America Founda-
Mexico Clinical and Transla-

t UL1TR001449).

ion Control and Epidemiology, Inc. Published by Elsevier Inc.. All rights reserved.

Clostridium difficile (recently renamed Clostridioides difficile1) is the
most common cause of health care�associated infection in the United
States, affecting nearly half a million patients per year and requiring
an estimated $4.8 billion in direct acute care costs.2-4 Although mor-
tality rates after C difficile infection (CDI) have improved,5 recurrence
after treatment occurs in as many as 20% of cases.6 New antimicrobial
therapies for CDI—as well as alternative methods to prevent or treat

CDI, such as prebiotic and probiotic agents and fecal microbiota trans-
plantation—have been developed.7-12

CDI prevention and treatment have become high priorities in the
health care system. Hospital-level CDI data are compared with
national benchmarks, and in January 2015, the Centers for Medicare
and Medicaid Services began to withhold funding for hospitals in the
lowest quartile. Hospital-onset CDI data are publicly reported on
Medicare’s Hospital Compare website.13 Meanwhile, the association
of CDI and antimicrobial exposure14,15 has prompted increased sup-
port of antimicrobial stewardship programs in acute care hospitals.

Recently, we reviewed our hospital’s experience with CDI over an
approximately 5-year period at the University of New Mexico (UNM)
Hospital, where CDI rates have been higher than expected compared
with national benchmarks. We sought to identify CDI predictors that
might be monitored or modified at the hospital level, with the long-
term goal of reducing CDI rates at UNM.

http://crossmark.crossref.org/dialog/?doi=10.1016/j.ajic.2018.07.014&domain=pdf

mailto:MCarvour@salud.unm.edu

https://doi.org/10.1016/j.ajic.2018.07.014

https://doi.org/10.1016/j.ajic.2018.07.014

https://doi.org/10.1016/j.ajic.2018.07.014

http://www.ScienceDirect.com

http://www.ajicjournal.org

Table 1
Antibacterial risk strata used for logistic regression modeling, with corresponding unadjusted and adjusted odds ratios for Clostridium difficile infection for each stratum

High-risk antibacterial agents Moderate-risk antibacterial agents Low-risk antibacterial agents

Cefaclor
Cefadroxil
Cefazolin
Cefdinir
Cefepime
Cefixime
Cefotaxime
Cefoxitin
Cefpodoxime
Cefprozil
Ceftaroline
Ceftazidime
Ceftriaxone
Cefuroxime
Cephalexin
Ciprofloxacin
Clindamycin
Levofloxacin
Moxifloxacin
Norfloxacin
Ofloxacin

Amoxicillin
Amoxicillin-clavulanate
Ampicillin
Ampicillin-sulbactam
Avibactam-ceftazidime
Dicloxacillin
Ertapenem
Imipenem-cilastatin
Meropenem
Nafcillin
Oxacillin
Penicillin G benzathine
Penicillin G potassium
Penicillin G sodium
Penicillin V potassium
Piperacillin
Piperacillin-tazobactam
Vancomycin*

Amikacin
Azithromycin
Aztreonam
Clarithromycin
Colistimethate
Dapsone
Daptomycin
Doxycycline
Erythromycin
Fosfomycin
Gentamicin
Linezolid
Minocycline
Nitrofurantoin
Rifabutin
Rifampin
Rifapentine
Rifaximin
Streptomycin
Sulfadiazine
Sulfamethoxazole
Sulfamethoxazole-trimethoprim
Tetracycline
Tigecycline
Tobramycin
Trimethoprim

Unadjusted OR = 1.46 (95% CI, 1.24, 1.72) Unadjusted OR = 1.07 (95% CI, 0.93, 1.22) Unadjusted OR = 0.96 (95% CI, 0.83, 1.11)
Adjusted OR = 1.60 (95% CI, 1.33, 1.92)y Adjusted OR = 1.03 (95% CI, 0.89, 1.20)y Adjusted OR = 0.83 (95% CI, 0.71, 0.98)y

CI, confidence interval; OR, odds ratio.
*Vancomycin administered by any systemic route (eg, intravenous infusion) was classified as a moderate risk antibacterial agent. Vancomycin administered by mouth, by feeding
tube, or per rectum was classified as a possible Clostridium difficile treatment agent. Other treatment agents included metronidazole and fidaxomicin. Treatment agents were not
included in the low-, moderate-, or high-risk strata.
yAdjusted ORs are from a multivariable model containing location type, age (�65 vs <65 years), all 3 antibacterial risk strata, antifungal agents, probiotics, and statins.

M.L. Carvour et al. / American Journal of Infection Control 47 (2019) 2�8 3

METHODS

Hospital setting

The UNM Hospital is a >500-bed academic medical center in
Albuquerque, New Mexico, which serves as a safety net hospital for a
geographically expansive, “majority-minority” state and offers care
for medically underserved populations throughout the state. It is also
the only level 1 trauma center in NewMexico.

Data source

Data were retrospectively obtained from the UNM Clinical and
Translational Science Center Clinical Data Warehouse, which extracts
data from the UNM electronic medical records for research use. A
unique study number was assigned to each patient in the cohort to
permit linkage across analytic files, and original identifiers were
removed before transmission of the data to the research team. The
UNM institutional review board reviewed and exempted the study.

Cohort selection

All hospitalized adult patients (�18 years of age) with �1 CDI
assay recorded between May 1, 2011, and August 31, 2016, were eli-
gible. A new CDI assay system was implemented at the UNM Hospital
in April 2011, so data collection for our study began the month after
this change. CDI tests using any assay (eg, enzyme immunoassay or
nucleic acid amplification/polymerase chain reaction, which were in
combined use during the study period) and any diagnostic result (eg,
positive or negative) were included. In keeping with our hospital’s

laboratory protocol, only specimens conforming to the shape of the
container were eligible.

Outcome definition

Patients with any positive CDI assay results at any time during the
study period were classified as CDI cases. If �1 positive result was
recorded for the patient, the first positive result during the study
period was used as the index record. Patients with �1 CDI assay with
no recorded positive result during the study period were classified as
not having CDI, and the first negative result in the study period was
used as the index record.

Sociodemographic predictors

Sex and race/ethnicity were based on the electronic medical record.
Age was defined as the patient’s age at the time of diagnosis and was
analyzed as both a continuous and categorical variable (eg, �65 vs
<65 years, based on findings elsewhere in the CDI literature16).

Spatiotemporal predictors

Season was defined using the month during which the CDI assay
was performed (eg, December-February, March-May, June-August,
and September-November). For modeling purposes, season was used
instead of year of diagnosis. Seasonality may be associated with other
important patterns (eg, other seasonal outbreaks and antimicrobial
prescribing patterns)17 and may be carried forward to future years as
a meaningful temporal unit.

Table 3
Multivariable predictive logistic regression model for Clostridium difficile infection (n = 4,278)

Unadjusted OR (95% confidence interval) Adjusted OR (95% confidence interval)*

Location type
Emergency department 1.72 (1.37, 2.15) 1.91 (1.51, 2.41)
General inpatient unit 1.13 (0.94, 1.36) 1.11 (0.92, 1.34)
Other inpatient unit 1.19 (0.80, 1.75) 1.26 (0.84, 1.90)
Intensive care unit 1.00 (reference category) 1.00 (reference category)

High-risk antibacterial agent within preceding 180 days 1.46 (1.24, 1.72) 1.54 (1.29, 1.84)
Probiotics within preceding 180 days 1.55 (1.24, 1.95) 1.39 (1.08, 1.80)
Statin within preceding 180 days 1.24 (1.05, 1.46) 1.26 (1.06, 1.51)

OR, odds ratio.
*Adjusted ORs are from a model containing all of the variables shown in this table. The C-statistic for the adjusted model is 0.59.

Table 2
Characteristics of patients with and without CDI (n = 5,209 unless otherwise specified)

CDI (n = 1,092) No CDI (n = 4,117) P value*

Male sex 559 (51.2%) 2,105 (51.1%) .97
Race/ethnicity (n = 5,073) .61

White non-Hispanic 425 (39.9%) 1,551 (38.7%)
Hispanic 379 (35.6%) 1,507 (37.6%)
American Indian/Alaskan Native 189 (17.7%) 653 (16.3%)
Black non-Hispanic 22 (2.1%) 85 (2.1%)
Other 51 (4.8%) 211 (5.3%)

Age
Mean (median, SD) in years 56.6 (57.0, 17.1) 57.5 (59.0, 17.1) .12
N (%) �65 365 (33.4%) 1,497 (36.4%) .07

Season .56
December-February 291 (26.7%) 1,066 (25.9%)
March-May 273 (25.0%) 1,057 (25.7%)
June-August 268 (24.5%) 1,076 (26.1%)
September-November 260 (23.8%) 918 (22.3%)

Location type (n = 4,599) <.0001
General inpatient 519 (54.3%) 2,078 (57.0%)
Emergency department 204 (21.3%) 538 (14.8%)
Intensive care unit 195 (20.4%) 882 (24.2%)
Other 38 (4.0%) 145 (4.0%)

Proton pump inhibitor within preceding 180 days (n = 4,822) 477 (45.7%) 1,699 (45.0%) .70
Immunosuppressant within preceding 180 days

Any (n = 4,851) 453 (43.2%) 1,645 (43.3%) .99
Steroid (n = 4,822) 365 (34.9%) 1,255 (33.2%) .30

Statin within preceding 180 days (n = 4,822) 236 (22.6%) 720 (19.1%) .01
Probiotic within preceding 180 days (n = 4,822) 117 (11.2%) 284 (7.5%) .0002
Antibacterial agent within preceding 180 days (n = 4,822)

High risk 817 (78.2%) 2,685 (71.1%) <.0001
Moderate risk 508 (48.6%) 1,775 (47.0%) .35
Low risk 349 (33.4%) 1,295 (34.3%) .59

Antifungal agent within preceding 180 days (n = 4,822) 126 (12.1%) 482 (12.8%) .54
Diabetes, �1 study criterion (n = 4,989) 384 (36.0%) 1,335 (34.1%) .24

CDI, Clostridium difficile infection.
*All P values are from an unadjusted logistic regression model in which the modeled outcome is CDI and the variable listed in the table is the single predictor in the model. The P
values shown in the table were used to determine eligibility for the multivariable selection procedure (eligible if P< .10).

4 M.L. Carvour et al. / American Journal of Infection Control 47 (2019) 2�8

Location was defined as the last recorded hospital unit prior to the
CDI assay (eg, the presumed location of the patient at the time of diag-
nosis), and locations were classified into 4 broad categories—emer-
gency department (ED); general inpatient units, including medical and
surgical wards; intensive care units; and other inpatient units, includ-
ing obstetrics/gynecology, rehabilitation, and preadmission units.

Clinical predictors

Antibacterial and antifungal agents, bacterial and fungal probiot-
ics, steroids and other immunosuppressants, statins,18,19 proton
pump inhibitors (PPIs),20 and antidiabetes medications21 recorded
within the 180-day window prior to the CDI diagnosis were included.

Antibacterial agents were classified into low, moderate, and high CDI
risk strata (Table 1).14,15,22

Diabetes was defined as having either a prescription for �1 antidia-
betes medications (eg, metformin or insulin) within the 180-day win-
dow prior to the CDI assay or any hemoglobin A1c �6.5% during the
study period, using the hemoglobin A1c value nearest to the date of
the CDI assay.

Predictive modeling

Sociodemographic and clinical predictors of CDI were evaluated
with predictive logistic regression modeling. Variables with P < .10 in
an unadjusted model were eligible for inclusion in a multivariable

M.L. Carvour et al. / American Journal of Infection Control 47 (2019) 2�8 5

model. Variables with P < .05 in the multivariable model were
retained. Manual forward and backward selection procedures were
applied, and the resulting models were compared.

Although stringent P value cutoffs (as presented earlier) were
applied to achieve a parsimonious model, a sensitivity analysis was
performed with <.20 used for entry into the model and <.10 for
retention. The classification of antibacterial agents into low-, moder-
ate-, and high-risk strata was internally evaluated in our dataset by
comparing the odds ratios (ORs) across strata. Analyses were con-
ducted in SAS version 9.4 (SAS Institute, Cary, NC). P values <.05 were
deemed statistically significant.

Power calculations

A multivariable model with �10 predictors was anticipated. Thus,
a minimum of 100 cases was desired (eg, 10 CDI cases per predictor
variable).23 Hospital epidemiologic surveillance data available before
this study suggested that an average of 200-300 cases of CDI occurred
each year. Because this annual estimate can include recurrent cases, a
conservative minimum of 100 cases per year was expected. To permit
stratified analyses,>5 years of data were included.

Post hoc analyses

During the planned analysis, probiotics were identified as a
positive predictor of CDI. Because probiotics are a proposed pre-
ventive therapy for CDI, their role as a surrogate marker of CDI
risk (ie, as a clinical predictor but not necessarily a causal factor)
was considered. A series of exploratory post hoc analyses was
performed to better understand the context of probiotic use
within the dataset, assess for potential evidence of bias, and gen-
erate future hypotheses.

First, to determine whether this finding reflected a diagnostic
lag—that is, whether probiotics were ordered in the days or weeks
before a CDI diagnosis in the setting of concurrent or unapparent
CDI—probiotic orders recorded in the 0-60 days before the assay
were compared with those recorded between 61 and 120 days and
between 121 and 180 days.

Next, to determine whether the apparent relationship of probiot-
ics and positive CDI assays was modified by any other variable in the
model, antibacterial or antifungal use, or the type of assay used to
diagnose CDI, interaction terms were tested for each of these varia-
bles in a multivariable model containing location type, age (�65 vs
<65 years), antibacterial use, and antifungal use.

Last, to evaluate whether the observed association was driven by a
particular subtype of probiotics, probiotic orders were stratified into
bacterial (including Lactobacillus or Bifidobacterium species) and fun-
gal (including Saccharomyces species) subtypes. Adjusted ORs were
compared for these subtypes.

RESULTS

Cohort characteristics

The cohort consisted of 5,209 patients who were tested for CDI
during the study period, including 1,092 cases with �1 positive CDI
assay during the study period. The characteristics of patients with
and without CDI are summarized in Table 2. CDI cases were more
likely to be <65 years of age (P = .07); to be located in the ED at the
time of the diagnosis (P < .0001); and to receive a statin (P = .01), pro-
biotic (P = .0002), or high-risk antibacterial agent (P < .0001) in the
180-day window before the CDI diagnosis. All of these variables were
eligible for multivariable modeling.

There were no significant differences between groups with
respect to sex, race/ethnicity, seasonality, diabetes, or other

medication types (Table 2). The organization of antibacterial agents
into high-, moderate-, and low-risk strata corresponded to an
expected gradient in the ORs across the 3 strata (Table 1), although
the low- and moderate-risk strata had similar odds and overlapping
confidence intervals (CIs).

Multivariable model results

Inpatient location type, statins, probiotics, and high-risk anti-
bacterial agents were significant independent predictors of CDI in
the multivariable model (Table 3). Patients in the ED had the
highest odds of a positive CDI assay (adjusted odds ratio [aOR],
1.91; 95% CI, 1.51, 2.41; compared with the intensive care unit
reference category, which had the lowest odds). Receipt of high-
risk antibacterial agents in the 180 days preceding CDI was asso-
ciated with a >50% increase in the odds of a positive assay (aOR,
1.54; 95% CI, 1.29, 1.84).

The dichotomized age variable (�65 vs <65 years) was not
retained in the multivariable model (P = .07). In the sensitivity analy-
sis, using P < .20 for entry into the model and P < .10 for retention,
the dichotomized age variable was retained (aOR, 1.15; 95% CI, 0.98,
1.34; for age <65 vs �65 years). However, this model produced simi-
lar characteristics (Akaike information criterion and C-statistic) and
similar beta estimates for the other variables in the model compared
with the primary model. Similarly, forcing PPI or steroid use into the
model shown in Table 3 revealed comparable beta estimates for all
other variables.

Probiotics analysis

Probiotics recorded between 0 and 60 days and between 61 and
120 days were associated with significantly increased odds of CDI,
with the highest odds observed between 61 and 120 days (Appendix
Table A1). This pattern differed for another prescription-related pre-
dictor (ie, statins; Appendix Table A1), which was examined for com-
parison. Probiotics did not significantly interact with the type of
diagnostic assay (P = .40) or the year of diagnosis (P = .20).

A significant interaction was observed between probiotics and
moderate-risk antibacterial agents (P = .01; Appendix Fig A1), in
which coadministration of probiotics and moderate-risk antibacterial
agents in the 180 days preceding the CDI diagnosis attenuated the
odds associated with probiotics alone. A similar overall pattern was
observed for both low- and high-risk antibacterial agents, although
these interactions were not statistically significant (P = .08 for each
test for interaction; Appendix Fig. A1). This pattern was not observed
with antifungal agents (P = .19; Appendix Fig. A1).

Bacterial probiotics, including Lactobacillus and Bifidobacterium
species, were associated with the highest independent odds of a posi-
tive CDI assay (aOR, 1.49; 95% CI, 1.11, 2.01; compared with no probi-
otics and adjusted for location type, age �65 vs <65 years,
antibacterial and antifungal agents, and statins). Fungal probiotics,
including Saccharomyces species, were associated with a weaker
increase in the odds of CDI (aOR, 1.22; 95% CI, 0.75, 1.98; compared
with no probiotics and adjusted as shown earlier).

DISCUSSION

In this diverse cohort of >5,200 hospitalized patients, including
>1,000 CDI cases, several factors independently predicted the occur-
rence of CDI. Important contextual factors about our cohort should be
noted. As a majority-minority state, New Mexico represents a unique
study population with respect to race and ethnicity. Approximately
one-third of the patients in our study identified as Hispanic, and
>16% identified as American Indian/Alaskan Native.

6 M.L. Carvour et al. / American Journal of Infection Control 47 (2019) 2�8

A distinctive set of geographic and socioeconomic factors also
influences health care in New Mexico. More than 40% of New Mexi-
cans live in an area with a primary care health professional shortage,
and about 20% of the state’s population lives at or below the poverty
line.24 The UNM Hospital provides care for many medically under-
served patients throughout the state.

Our model should be interpreted with 2 important methodologi-
cal provisos in mind. First, it was constructed for the purposes of pre-
dicting CDI among those tested, not for demonstrating causal
relationships between any 1 variable and the outcome of CDI. Second,
patients with and without CDI were all tested for CDI and therefore
may have shared more clinical factors compared with others in the
hospital population. Thus, our results cannot be extrapolated directly
to the risk of CDI resulting from the predictors that were significant—
or not significant—in our model.

As an example, we did not observe significantly increased odds of
CDI among patients who received PPIs or steroids. However, the
cohort consisted of hospitalized patients in whom CDI was already
suspected, with high overall rates of PPI (45.1%) and steroid (33.6%)
use. Although PPIs and steroids did not predict CDI in our study, this
does not exclude the possibility that either PPIs or steroids could
increase CDI risk.

Similarly, we did not find age �65 years to be a significant predic-
tor of CDI. In fact, older patients in our cohort were less likely to have
CDI. This must also be interpreted in the context of the study popula-
tion—adult hospitalized patients—for which our model predicts the
odds of diagnosis and not necessarily the incidence of infection.
Patients �65 years old may have been diagnosed at home, at nursing
facilities, or at other hospitals where Medicare-eligible patients may
be seen.

Variables that independently predicted CDI in our study (Table
3) were inpatient location type and use of high-risk antibacterial
agents, statins, and probiotics. Patients were most likely to have a
recorded location in the ED at or immediately preceding their CDI
diagnosis. This finding could represent a number of underlying
factors, such as a high frequency of ED visits, perhaps comprising
a primary health care access point for many patients in the com-
munity; high frequencies of antibiotic prescribing or CDI testing
in the ED; or potential delays in admission to other inpatient
units owing to precautionary isolation practices. Similarly, statin
use may constitute a measure of increased health care access in
our study. The relationship of statin use and CDI remains a sub-
ject of interest in the literature,18,19 although the nature and
direction of this relationship is not yet clear.

The positive association of probiotic use with subsequent CDI
was unexpected. Prior studies have suggested that probiotics may
prevent CDI, although results have varied depending on the type,
timing, and setting of CDI, as well as the type of probiotic.8-12,25-
27 To date, there is neither scientific consensus nor Food and
Drug Administration approval for the uniform use of probiotics to
prevent CDI.

As described above, this result should be interpreted with care.
Probiotics independently predicted the odds of CDI in our cohort, but
this does not demonstrate that probiotics caused or contributed to
the causes of CDI. Even so, if probiotics had exerted a strong protec-
tive effect in the cohort, we might have expected probiotics to impose
a negative (or perhaps a null) predictive impact. Recognizing that the
direction of the association in our study was unexpected, we under-
took a series of post hoc analyses to better understand the context of
this result.

First, we anticipated that diagnostic lags between the onset of
CDI symptoms (at which time probiotics might have been
ordered) and CDI diagnoses may have created an inaccurate
impression that probiotic use actually preceded the infection. As
shown in the Appendix (Table A1), however, the odds of CDI

after a probiotic prescription remain elevated for up to 4 months
after recorded probiotic use; in fact, the highest odds were
observed for probiotics recorded 2-4 months before the CDI diag-
nosis. Thus, it is not likely that short-term diagnostic lags can
fully explain our observation.

Similarly, if the observed impact of probiotics differed significantly
over time—that is, if this was concentrated early in the study period—
we might have concluded that probiotics were markers of existing
CDI and that most of the cases driving the association were preexist-
ing or recurrent infections. However, the relationship of probiotics
and CDI diagnosis did not change significantly over time, as evi-
denced by the absence of a significant statistical interaction between
probiotics and the year of diagnosis.

Next, we considered the possibility that patients were more likely
to be treated with probiotics during periods of increased exposure to
the health care environment. The observed temporal patterns in the
Appendix Table A1 do not support this. The highest odds of probiotic
use occurred in a time window distinct from that in which the CDI
diagnosis was made, and this pattern differed from the association of
CDI and statin prescriptions—another possible surrogate for health
care exposures.

Finally, we anticipated that probiotics might be a surrogate
marker for another correlated variable or set of variables. To obtain
preliminary, hypothesis-generating information, we assessed
whether the predictive impact of probiotics differed according to
other clinical factors, including antimicrobial therapies. This analysis
revealed several further, unexpected findings (Appendix Fig. A1).

Coadministration of probiotics with high-risk antibacterial agents
in the 180 days preceding the CDI diagnosis did not significantly
attenuate the odds of CDI associated with high-risk antibacterial ther-
apies (Appendix Fig A1C). Instead, coadministration of moderate-risk
antibacterial agents with probiotics in the 180 days preceding the
CDI diagnosis actually appeared to attenuate the odds of CDI associ-
ated with probiotics (Appendix Fig A1B). This general pattern was
observed for all antibacterial strata but not for antifungal agents
(Appendix Fig A1). Meanwhile, bacterial probiotics were also stronger
predictors of CDI than fungal probiotics.

Prior evidence suggests that race, ethnicity, and socioeconomic
position may all impact CDI risk28 and microbiomic composition
at various anatomic sites.29,30 If so, specific probiotic therapies for
CDI may only be useful insofar as we understand the underlying
microbiomic environments across which these are applied. Fur-
ther attention may need to be directed to understanding the CDI
epidemic and its microbiomic drivers in diverse and medically
underserved populations to distinguish between the causes of CDI
on a population level—a worldwide problem and one clearly still
observed in our hospital—and not just the causes of individual
cases.31,32

In this diverse cohort, patients with CDI were most commonly
diagnosed while in the ED and were likely to have prior exposures to
high-risk antibacterial agents, probiotics, and statins. Our study is
limited by the retrospective and observational nature of data collec-
tion. Specific information about clinical impressions, adherence with
prescribed therapies, and exposure to therapies other than those
recorded in the electronic medical record was not available for this
study. Future prospective CDI research should consider potential dif-
ferences in microbiomic composition, CDI prevention, and CDI treat-
ment in diverse and medically underserved populations.

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APPENDIX

This document contains 2 supplemental exhibits from the post hoc analysis of probiotic use and Clostridium difficile infection as described in
the main article text.

8 M.L. Carvour et al. / American Journal of Infection Control 47 (2019) 2�8

Fig. A1. Association of probiotics and Clostridium difficile infection stratified by coexposure to low-risk antibacterial agents (A), moderate-risk antibacterial agents (B), high-risk
antibacterial agents (C), or antifungal agents (D) (n = 4,278). A statistically significant interaction was observed between probiotics and moderate-risk antibacterial agents
(P = .01). Adjusted ORs and 95% CIs are shown for patients who received probiotics with or without an antimicrobial agent in the same 180-day period. Each panel (A-D) repre-
sents a separate model. ORs are adjusted for location type, age (�65 vs <65 years), statins, and all other antibacterial and antifungal strata not already included in the interaction
term. For instance, the moderate-risk panel (B) shows the ORs for a hybrid variable combining moderate-risk antibacterial agents with or without probiotics, and the ORs in that
figure are adjusted for low- and high-risk antibacterial agents, antifungal agents, statins, age, and location. CI, confidence interval; OR, odds ratio.

Table A1
Comparative odds of Clostridium difficile infection for patients exposed to probiotics or statins in different time windows within the 180-day period before diagnosis

Time window Probiotics OR
(95% confidence interval)*

Statins OR
(95% confidence interval)*

0-60 days (n = 4,209) 1.32 (0.99, 1.77) 1.24 (1.03, 1.51)
61-120 days (n = 1,199) 1.84 (1.02, 3.32) 0.84 (0.58, 1.20)
121-180 days (n = 948) 0.95 (0.41, 2.18) 1.31 (0.90, 1.91)

OR, odds ratio.
*ORs correspond to a multivariable model containing probiotics, statins, location type, age (�65 vs<65 years), all 3 antibacterial risk strata, and antifungal agents. ORs represent the
odds of C difficile infection for patients exposed to the medication vs those not exposed (eg, probiotic vs no probiotic, statin vs no statin).

• Predictors of Clostridium difficile infection and predictive impact of probiotic use in a diverse hospital-wide cohort

Methods

Hospital setting

Data source

Cohort selection

Outcome definition

Sociodemographic predictors

Spatiotemporal predictors

Clinical predictors

Predictive modeling

Power calculations

Post hoc analyses

Results

Cohort characteristics

Multivariable model results

Probiotics analysis

Discussion

References

Appendix

Research Article
Mechanism of Antibacterial Activity of Bacillus
amyloliquefaciens C-1 Lipopeptide toward Anaerobic
Clostridium difficile

Jia Lv,1 Rong Da,2 Yue Cheng,1 Xiaohong Tuo,1 Jie Wei,1 Kaichong Jiang,

1

Adediji Omolade Monisayo,1 and Bei Han 1

1School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, China
2Department of Clinical Laboratory, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

Correspondence should be addressed to Bei Han; hanbei@mail.xjtu.edu.cn

Received 11 November 2019; Revised 13 January 2020; Accepted 4 February 2020; Published 4 March 2020

Academic Editor: Frederick D. Quinn

Copyright © 2020 Jia Lv et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Probiotics may offer an attractive alternative for standard antibiotic therapy to treat Clostridium difficile infections (CDI). In this
study, the antibacterial mechanism in vitro of newly isolated B. amyloliquefaciens C-1 against C. difficile was investigated. The
lipopeptides surfactin, iturin, and fengycin produced by C-1 strongly inhibited C. difficile growth and viability. Systematic
research of the bacteriostatic mechanism showed that the C-1 lipopeptides damage the integrity of the C. difficile cell wall and
cell membrane. In addition, the lipopeptide binds to C. difficile genomic DNA, leading to cell death. Genome resequencing
revealed many important antimicrobial compound-encoding clusters, including six nonribosomal peptides (surfactins
(srfABCD), iturins (ituABCD), fengycins (fenABCDE), bacillibactin (bmyABC), teichuronic, and bacilysin) and three
polyketides (bacillaene (baeEDLMNJRS), difficidin (difABCDEFGHIJ), and macrolactin (mlnABCDEFGHI)). In addition, there
were other beneficial genes, such as phospholipase and seven siderophore biosynthesis gene clusters, which may contribute
synergistically to the antibacterial activity of B. amyloliquefaciens C-1. We suggest that proper application of antimicrobial
peptides may be effective in C. difficile control.

1. Introduction

Clostridium difficile is an anaerobic, gram-positive, spore-
forming bacterium. Clinical signs of C. difficile infection
(CDI) range from mild diarrhea to fulminant colitis [1].
The incidence and severity of CDI have increased signifi-
cantly, especially by the recently emerged and highly viru-
lent epidemic strain BI/NAP1/027 [2]. With increasing
antibiotic resistance of C. difficile, there is an urgent need
to develop new agents and efficient methods for the treat-
ment and control of CDI [1, 3]. Distinct from the traditional
antibiotics, many novel antimicrobial agents, such as ramo-
planin, surotomycin, and cadazolid, are currently being
investigated in clinical trials for the treatment of CDI. Suro-
tomycin is an orally dosed lipopeptide antibiotic that acts
by disrupting the cell membrane [4].

Bacteriocins are ribosomally synthesized antimicrobial
peptides with high activity against other bacteria. Bacterio-
cins are secreted by some probiotic microorganisms, such
as Lactobacillus species, Saccharomyces boulardii, and bifido-
bacteria [5]. Thus, further evaluation should be given to the
bacterial resources, antimicrobial mechanisms, and biosafety
of probiotics in considering bacteriocins as an alternative or
adjunctive therapeutic method for CDI.

Except the ribosomally synthesized antimicrobial pep-
tides, Bacillus species could synthetize a mixture of lipopep-
tides by nonribosomal peptide synthetases, which mainly
include surfactin, iturin, lichenysin, and fengycin families,
with broad-spectrum biological activities [6–8]. With the
described functional secondary metabolites, many Bacillus
spp. strains have been developed as biofertilizers and bio-
pesticides, and they are currently regarded as promising

Hindawi
BioMed Research International
Volume 2020, Article ID 3104613, 12 pages
https://doi.org/10.1155/2020/3104613

https://orcid.org/0000-0002-7841-554

3

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https://doi.org/10.1155/2020/3104613

environmentally friendly means for plant protection and
plant growth promotion, and as secondary metabolite facto-
ries [9]. However, for B. amyloliquefaciens, there are only a
few reports that describe an antimicrobial activity against
toxin-producing C. difficile [10].

We isolated B. amyloliquefaciens C-1 from ready-to-eat
fruit samples. The bacterial strain is stored in the China
Center for Type Culture Collection as a patent strain with
the number of CCTCC M2010177. The supernatant of C-1
showed high antioxidant activity and inhibitory activity
against foodborne pathogens (Escherichia coli O157:H7, B.
cereus, S. aureus, etc.) and human pathogens (C. difficile,
Klebsiella pneumoniae, Enterococcus faecium, etc.) [11, 12];
however, no effect against fungi was found.

Comparative genomic analysis showed evolutionary
traits for B. amyloliquefaciens strain adaptation to host habi-
tats [13]. The C-1 strain exhibited a biosurfactant activity
phenotype against pathogens. The molecular bases/mechan-
isms of this pathogen-specific activity were unknown. In this
study, we investigated the anti-C. difficile mechanisms of the
secreted B. amyloliquefaciens C-1 extracellular lipopeptides.
We systematically investigated the effects of C-1 lipopeptide
on C. difficile cell growth, morphological structure, cell wall
and membrane integrity, and genome. Then, we resequenced
the entire C-1 genome and identified relevant gene clusters,
locations, and potential regulatory sequences, including
genes for bacteriocins, ribosomally synthesized antibacterial
peptides, phospholipase, siderophores, and genes that pro-
vide resistance to toxic compounds.

2. Material and Methods

2.1. Bacterial Strains and Culture. B. amyloliquefaciens C-1, a
patent strain (Chinese patent no. ZL201410260574.2), was
isolated and stored in our lab. It was inoculated into
fermentation medium (12.4 g/l tryptone, 20 g/l glucose, 5 g/l
NaCl, 1.5 g/l K2HPO4·3H2O, 0.04 g/l MnSO4·H2O, 1.7 g/l
FeSO4·7H2O, and 1.2 g/l MgCl2·6H2O, pH 7.2) and grown
with shaking of 200 rpm in flasks for 72 h at 30°C. Clostrid-
ium difficileATCC 9689, 700057, and BAA-1870 strains were
obtained from the American Type Culture Collection and
stored at -80°C. C. difficile strains were cultured in sterile
Reinforced ClostridiumMedium (RCM) and incubated over-
night at 35°C in an anaerobic chamber (Coy Laboratory
Products Inc., Ann Arbor, Michigan) with an atmosphere
of 82% N2, 15% CO2, and 3% H2.

2.2. Isolation and Identification of B. amyloliquefaciens C-1
Extracellular Lipopeptide. Lipopeptide isolation was per-
formed by acid precipitation according to Zhang et al. [14].
Briefly, the crude cell-free culture was adjusted to pH 2.0 with
6M HCl and placed overnight at 4°C. After centrifugation,
the precipitate was extracted twice with methanol. The
solution was dried in a vacuum freeze dryer, and the dry
residue was dissolved in 50mM Tris-HCl (pH 7.5) and
passed through a 0.22μm filter. The extracted lipopeptide
sample was analyzed with a UV-VIS spectrophotometer
(UV5, Mettler Toledo).

2.3. Thin-Layer Chromatography. The purified lipopeptide
was examined by thin-layer chromatography (TLC) on a sil-
ica gel G plate [15]. TLC assay was performed on a silica gel G
plate (10 × 20 cm, Silica gel 60, Germany). A chloroform-
methanol mixture (10 : 1, v/v) was used as the mobile phase.
A sample was spotted onto the TLC plate and hydrolyzed
with 6M HCl for 2 h in a sealed container. Once dried, the
plate was developed in the mobile phase. After development,
the plate was sprayed with 0.5% ninhydrin and placed in an
oven at 110°C for 10min to detect the peptides as red spots.

2.4. Semipreparative HPLC Analysis. The putative lipopep-
tides were identified by HPLC analysis. Briefly, crude extract
spots were removed from the TLC plates and dissolved in
10% methanol; the supernatants were analyzed by semipre-
parative high-pressure liquid chromatography using an Agi-
lent LC 1200 system. The chromatographic separation was
performed with a C-18 Column (4:6 × 250mm). The column
outlet was coupled to an Agilent MSD Ion Trap XCT mass
spectrometer equipped with an ESI ion source. The lipopep-
tide fragments were selectively desorbed with methanol
gradients from 35% to 65% within 140min. All elution pro-
grams used a flow rate of 0.5ml/min at 214 nm and detection
occurred using the negative ion mode at m/z ranging from
400 to 2000. The isolated fragments were collected for the fol-
lowing experiments.

2.5. Detection of Lipopeptide Synthesis-Related Genes. Lipo-
peptide biosynthesis genes (sfr, ituD, and fenB) were identi-
fied by PCR (sfr-F: 5′ ATGAAGATTTACGGAATTTA 3′,
sfr-R: 5′ TTATAAAAGCTCTTCGTACG 3′; ituD-F: 5′
ATGAACAATCTTGCCTTTTTA 3′, ituD-R: 5′ TTATTT
TAAAATCCGCAATT 3′; fenB-F: 5′ CTATAGTTTGT
TGACGGCTC 3′, fenB-R: 5′ CAGCACTGGTTCTTGT
CGCA 3′) [9]. PCR conditions consisted of an initial dena-
turation step at 94°C for 5min followed by 30 cycles of
denaturation at 94°C for 1min, 54°C annealing for 45 sec
(sfr, ituD) or 1min (fenB), and 72°C extension for 1min
followed by a final extension step at 72°C for 7min. The
amplified PCR product was purified and sequenced by an
automated sequencer (3730 DNA Analyzer). PCR product
sequences were identified using GenBank nucleotide data
and BLAST from the National Center for Biotechnology
Information, Bethesda, MD, USA (http://www.ncbi.nlm
.nih.gov/blast/).

2.6. The Inhibitory Activity of Lipopeptide against C. difficile.
Antimicrobial activities of the lipopeptides were detected by
disc diffusion assay. 0.5McF (106CFU/ml) of C. difficile cells
was inoculated onto the surface of blood agar plates. An
Oxford cup (6mm diameter), containing 100μl lipopeptide
with concentrations of 5, 10, and 15μg/ml dissolved in 10%
methanol, was placed on test C. difficile-seeded plates. A
cup containing 100μl 10% methanol was used as negative
control. Each C. difficile strain was plated in triplicate. The
plates were incubated anaerobically overnight at 35°C, and
antimicrobial activity was evaluated by measuring inhibition
zones against the tested C. difficile cells. The minimal inhibi-
tory concentrations (MIC) against C. difficile strains were

2 BioMed Research International

http://www.ncbi.nlm.nih.gov/blast/

http://www.ncbi.nlm.nih.gov/blast/

determined by the broth microdilution method in 96-well
microplates with a final concentration of 105CFU/ml, and
the final concentration of the added lipopeptide ranged
from 10μg/ml to 0.0095μg/ml; bacterial growth without
lipopeptide was set as control [16]. The MIC was defined
as the lowest lipopeptide concentration at which growth
was completely inhibited after overnight anaerobic incuba-
tion of the plates at 35°C.

2.7. Growth of C. difficile Incubated with Lipopeptides. For
time-kill analyses, 0.5McF (106CFU/ml) of C. difficile cells
(strains ATCC 9689, ATCC 700057, and ATCC BAA-1870)
was prepared and inoculated into fresh RCM containing 0,
0.25, 0.5, and 0.75 MIC of lipopeptide separately, and incu-
bated in the anaerobic chamber at 35°C. Cell viability was
determined every 2 h for 24 h [17]. Each treatment was
performed with three biological replicates.

2.8. Scanning Electron Microscope Analysis of C. difficile Cells
Treated with Lipopeptides. An overnight culture of C. difficile
was transferred into fresh RCM medium with 0.5McF and
0.25MIC of lipopeptide, and incubated anaerobically at
35°C for 1 h. The C. difficile cells were collected and washed
three times with sterile PBS solution. The cells were fixed
with 2% glutaraldehyde overnight at 4°C and then dehy-
drated by a graded series of ethanol (50%, 70%, 80%, 90%,
95%, and 100%) for 20min at each step. After the critical
point of drying and gold coating, the surface structure of
treated C. difficile cells was observed with a scanning electron
microscope (Hitachi S-2460N, Hitachi, Ltd., Tokyo, Japan) at
an acceleration voltage of 20 kV [18].

2.9. Fluorescence Microscope Analysis of C. difficile Cells
Treated with Lipopeptides. Propidium iodide (PI) penetrates
only damaged cell membranes, whereupon it binds to
double-stranded DNA and fluoresces red with 488nm illu-
mination. To clearly detect an effect on the plasma mem-
brane, 0.5MIC of the purified lipopeptide was incubated
with C. difficile cells (107 cells/ml) in RCM liquid medium.
The mixture was incubated anaerobically at 35°C for 1 h.
Then, 10μl of 100μg/ml PI solution was added to the cell
suspension, and the mixture was incubated for 30min in
the dark. Finally, the treated C. difficile cells were observed
with a Nikon TI-S fluorescence microscope with the filters
set at an excitation wavelength of 488nm and an emission
wavelength of 538nm. Cells treated with the same amount
of sterile water were used as a negative control [19]. All
experiments were repeated three times.

2.10. Determination of Extracellular Alkaline Phosphatase
Activity of C. difficile Cells Treated with Lipopeptide. An over-
night culture of C. difficile was subcultured into fresh RCM
liquid medium. Lipopeptide was added separately into the
C. difficile culture at final concentrations of 0.25 and
0.5MIC. Bacterial supernatant (0.5ml) was collected every
12 h for the measurement of extracellular alkaline phospha-
tase (AKPase) activity using an AKP assay kit (Nanjing Jian-
cheng Technology Co., Ltd., Nanjing, China) as described in
[20]. The AKPase unit was defined as 1mg of phenol pro-
duced by 100ml of bacterial culture supernatant reacted with

the substrate at 37°C for 15 minutes. Cells treated with the
same amount of sterile water were used as negative control.
Each test was performed in three biological replicates.

2.11. Lipopeptide Binding to C. difficile Genomic DNA. Gel
retardation experiment assays were performed to identify
the DNA binding activity of the lipopeptide as described in
[21]. Briefly, 50ng of C. difficile genomic DNA was mixed
with 1μl of 1, 2, and 5μg/ml lipopeptide in 20μl of binding
buffer (10mM Tris-HCl, 1mM EDTA buffer, pH 8.0). One
μl of sterilized water mixed with C. difficile genomic DNA
was used as negative control. Mixtures were incubated at
35°C for 1 h. All samples were subjected to 1.0% agarose gel
electrophoresis and stained with ethidium bromide.

2.12. Whole Genome Sequencing of B. amyloliquefaciens C-1.
The genomic DNA of strain C-1 was isolated and purified by
a kit (Applied Biosystems® 4413021) and sequenced on the
Pacific Bioscience (PacBio) RS II system at Genefund, Shang-
hai, China. The genome was assembled with SMRT analysis
v.2.3 and RS_HGAP_Assembly.3, and the genome assembly
was improved by using the software Pilon. Identification of
protein-coding open reading frames (ORFs) and annotation
of ORFs were performed by using the NCBI Prokaryotic
Genome Annotation Pipeline. Genes were functionally
annotated by BLAST search in COG (Gene Ontology Con-
sortium), Nr (NCBI RefSeq), and Pfam Databases [22, 23].
KEGG (Kyoto Encyclopedia of Genes and Genomes) data-
base was used in the analysis of metabolic pathways of
lipopeptide-producing Bacillus species. All amino acid
sequences derived from the Bacillus genomes were submitted
to the KEGG database, and the metabolic functions of these
sequences were annotated by KASS. The KO (KEGG Orthol-
ogy) term and corresponding KEGG pathway for each ORF
were automatically generated. Secondary metabolite clusters
present in the genome of the B. amyloliquefaciens collection
have been evaluated using antiSMASH 5.0 [24].

The 16S rRNA gene sequences of Bacillus species were
extracted from genome sequences and aligned using the
CLUSTALX [25]. Phylogenetic trees were constructed using
the neighbor-joining method implemented in the software
package MEGA version 7.0.26 [26]. Evolutionary distances
were calculated using Kimura’s two-parameter model.

The C-1 genome sequence data were deposited into the
Sequence Read Archive (SRA) of NCBI and can be accessed
via accession number SRP127533.

2.13. Statistical Analysis. All experimental data are expressed
as the average with standard deviation of at least three inde-
pendent replicates. Statistical analyses were performed using
the t-test and analysis of variance (ANOVA), JMP pro (SAS
Institute Inc., NC, US), STAMP10, and SPSS V20.0 (IBM Inc.,
IL, US). Significant differences were considered at P < 0:05.

3. Results and Discussion

3.1. Production, Purification, and Identification of C-1
Lipopeptide. Bacillus spp. produces abundant secondary
metabolites, such as proteinase, amylase, bacteriocin, and
exopolysaccharide. In this study, we focused on the

3BioMed Research International

lipopeptide-producing B. amyloliquefaciens strain C-1. The
growth profile of C-1 is shown in Figure 1. The maximum
growth and cell dry weight were reached at 48 h; the maxi-
mum lipopeptide production (3:49 ± 0:26mg/ml) was
reached at 72 h. Lipopeptide production was conducted in a
fermentation medium. The active compound from the cul-
ture supernatant was scanned from 190 to 900nm, and the
maximum absorption occurred at 213 nm, which is the typi-
cal absorption wavelength of peptides. Three spots were
observed by thin-layer chromatography, and three compo-
nent peaks were detected by RP-HPLC. Mass spectroscopy
showed that the molecular masses of the three components
at m/z were 1067Da, 1477Da, and 1506Da, which corre-
sponded to surfactin, fengycin, and iurin, respectively. PCR
products of 675 bp, 1400 bp, and 482 bp corresponded to
Srf, FenB, and ItuD genes, and the PCR fragments were
sequenced and showed 99% identity with surfactin, fengycin,
and iturin biosynthesis gene clusters, individually (S1).

3.2. Antimicrobial Activity of C-1 Lipopeptide against C.
difficile. Bacillus spp. lipopeptides have an inhibitory activity
against plant pathogenic fungi and pathogenic bacteria and
have been developed as biocontrol agents [27]. Although, in
our previous report, C-1 did not show any inhibitory activity
against fungi, it did have antibacterial activity toward several
human pathogens, and this antibacterial activity in the C-1
supernatant was verified to be the contribution of lipopep-
tide, not exopolysaccharide [11, 12].

In plate tests, the C-1 lipopeptide displayed antagonistic
activities against three C. difficile strains. Inhibition zone
diameters ranged between 7.05mm and 22.00mm, and the
largest inhibition zone was from 15μg/ml lipopeptide against
strain C. difficile ATCC 9689 (Table 1). The MICs against C.
difficile strains ATCC 9689, ATCC 700057, and ATCC BAA-
1870 were 0.75, 2.5, and 2.5μg/ml, separately. Within a cer-
tain concentration range (0.0095μg/ml-10μg/ml), the inhib-
itory effect was positively correlated with the concentration
of the C-1 lipopeptide. To analyze the inhibitory effect, we
determined the growth curves of the three C. difficile strains.
At 24 h of continuous measurement, the maximum OD600 of
C. difficileATCC 9689, ATCC 700057, and ATCC BAA-1870
treated with 1/4 MIC lipopeptide reached 51.57%, 51.54%,
and 56.12% of the control. For the 1/2 MIC treatment,
growth was reduced to 43.15%, 46.39%, and 46.12% of the
control. For the 3/4 MIC treatment, growth was further
reduced to 38.95%, 40.21%, and 41.84% of the control. The
C. difficile ATCC9689 was significantly the most sensitive
to the treatment (P < 0:01), and the inhibitory effect was
stronger with an increased concentration of the C-1 lipopep-
tide (Figure 2).

Because C. difficile ATCC9689 was more sensitive to the
C-1 lipopeptide, and it was also a tcdA and tcdB positive
strain, we assessed the following antibacterial mechanism
toward this strain.

3.3. Effect of C-1 Lipopeptide on C. difficile Morphology, Cell
Wall Permeability. Scanning electron microscopy showed
that the surface of C. difficile ATCC 9689 was damaged after
treatment with the C-1 lipopeptide. Exudates surrounded the

bacteria, and the cell wall and cell membrane were inter-
rupted and indistinct, whereas untreated cells were smooth
and uninterrupted (Figures 3(a) and 3(b)). In addition, with
increasing concentrations of lipopeptide, the bacteria were
surrounded by exudate that may have been cytoplasm
extruded from the cells. Propidium iodide (PI) penetrates
only a damaged cell membrane, after which it binds to
double-stranded DNA and fluoresces red with illumination
at 488nm. We stained the C-1 lipopeptide-treated and C-1
lipopeptide-untreated C. difficile cells with PI and observed
the cells with a fluorescence microscope. C. difficile ATCC
9689 cells treated with lipopeptide were stained with PI as
shown by red fluorescence, which indicated a damaged cell
membrane (Figures 3(d) and 3(f)), whereas the untreated
cells did not show any fluorescence (Figures 3(c) and 3(e)).

The destroyed cell membrane increased the permeability
of C. difficile ATCC 9689 after C-1 lipopeptide treatment. To
verify the effect of lipopeptide on cell wall permeability, we
measured alkaline phosphatase (AKPase) activity. A dam-
aged cell wall and cell membrane increased cell permeability
and caused an increase in extracellular AKPase. After lipo-
peptide treatment, extracellular AKPase activity increased
continuously and was significantly higher than that of the
untreated cells (Fig S2). With 36 h of incubation, the extracel-
lular AKPase content of C. difficile ATCC 9689 treated with
1/4 MIC and 1/2 MIC of lipopeptide increased 4.7-fold and
7.7-fold.

The antibacterial activity of Bacillus spp. lipopeptide was
observed with other pathogens, such as S. aureus [28], Vibrio
anguillarum [18], and E. clocae [8]. However, our report is
the first to document the effects of lipopeptide on toxin-
producing C. difficile. The inhibitory mechanism of the C-1
lipopeptide against C. difficile could be explained by destroy-
ing the bacterial cell by forming ion-conducting channels in
the cell membrane as described by Etchegaray et al. [29]. This
mode of action drastically reduces the chance of the

7

2

0.0 0

1

2

3

4

0.5

1.0

1.5

C
ell

d
ry

w
ei

gh
t (

m
g)

Li
po

pe
pt

id
e (

m
g/

m
l)

2.0

2.5

3.0

3.5

6048
Time (h)

3624120
0.0

0.5

1.0

1.5

O
D

60
0

2.0

2.5

Lipopeptied
OD600

Cell dry weight

Figure 1: The fermentation and lipopeptide production in B.
amyloliquefaciens C-1 for 72 hours. The solid line indicates the
growth curve of OD600, the dotted line indicates the growth curve
of cell dry weight, and the shaded columns represent the
lipopeptide production.

4 BioMed Research International

development of resistance by microbes, offering a promising
alternative for the treatment of CDI.

3.4. Genome Sequencing of B. amyloliquefaciens C-1. The cir-
cular chromosome of C-1 contains 3,934,216 bp, 46.5% GC
content, 27 rRNA and 86 tRNA genes (Table 2,

Figure 4(a)). Genome annotation at the RAST server showed
that the C-1 genome encodes 4013 proteins, and the corre-
sponding functional categorization by COG annotation is
in Figure 4(b). The sequence data of the B. amyloliquefaciens
C-1 genome were deposited into NCBI and can be accessed
via accession number SRP127533.

Table 1: The inhibition of the C-1 lipopeptide against Clostridium difficile in a plate test (the inhibition diameter showed in mm).

Clostridium difficile
Concentration (μg/ml)

0 5 10 15

ATCC 9689 0.00 7:05 ± 0:71a,b 14:50 ± 0:71a,b 22:00 ± 1:41a,b

ATCC 700057 0.00 0.00 8:40 ± 0:71a 11:50 ± 0:71a

ATCC BAA-1870 0.00 0.00 8:50 ± 0:71a 10:50 ± 0:71a
aSignificant difference of C-1 lipopeptide treatments vs. negative control (P < 0:01). bSignificant difference of lipopeptide treatments between C. difficile strains
ATCC 9689 and ATCC 70057, ATCC 9689, and ATCC BAA-1870 (P < 0:01).

0
0.0
0.1
0.2
0.3
0.4
0.5
0.6

O
D

60
0

0.7
0.8
0.9
1.0
1.1

2 4 6 8 10 12 14 16
Time (h)

18 20 22 24 26

0
0.25 MIC

0.5 MIC
0.75 MIC

(a)

0
0.0
0.1
0.2
0.3
0.4
0.5
0.6

O
D

60
0

0.7
0.8
0.9
1.0
1.1

2 4 6 8 10 12 14 16
Time (h)

18 20 22 24 26

0
0.25 MIC

0.5 MIC
0.75 MIC

(b)

0
0.0
0.1
0.2
0.3
0.4
0.5
0.6

O
D

60
0

0.7
0.8
0.9
1.0
1.1

2 4 6 8 10 12 14 16
Time (h)

18 20 22 24 26

0
0.25 MIC

0.5 MIC
0.75 MIC

(c)

Figure 2: The growth curve of C. difficile ATCC 9689 (a), ATCC 700057 (b), and BAA-1870 (c) treated with different concentrations of
the C-1 lipopeptide.

5BioMed Research International

(a) (b)

(c) (d)

(e) (f)

Figure 3: Morphological changes of C. difficile ATCC 9689 treated with 0.5 MIC of the C-1 lipopeptide. Observed with a scanning electron
microscope (×25000; (a) untreated cells and (b) treated cells). Observed with a light microscope (×400; (c) untreated cells and (e) treated
cells). Observed with a fluorescent microscope (PI staining, ×400; (d) untreated cells and (f) treated cells).

Table 2: Genome project information summary of B. amyloliquefaciens C-1.

Property/attributes C-1 Property/attributes C-1

Finishing quality High-quality draft Total predicted CDS 3805

Sequencing platform PacBio Sequel rRNA operons 27

Total bases (Mb) 757.4 tRNA 86

NCBI taxonomy ID 1386 tmRNA 1

BioProject ID PRJNA427474 Noncoding RNA 81

Genome size (bp) 3934216 Miscellaneous RNA 81

GC content (%) 46.5

6 BioMed Research International

XJTU

BASys

2800 kbp

2

400 kbp

2

200 kbp

2000 kbp1800 kbp

1

600 kbp

1400 kbp

3800 kbp
3600 kbp

3400 kbp

3200 kbp
800 kbp

1000 kbp

1200 kbp

3000 kbp

2600 kbp

200 kbp

400 kbp

600 kbp

Genes encoding proteins

Genes encoding functional RNA

COG functional categories

Cellularprocesses

Metabolism

Fonvard strand

Fonvard strand

Reverse strand

Information storage and processing

Reverse strand

Translation, ribosomal structure and biogenesis

DNA replication, recombination and repair

Cell division and chromosome partitioning
Posttranslational modification, protein turnover, chaperones
Cell envelope biogenesis, outer membrane
Cell motility and secretion
Inorganic ion transport and metabolism

Energy production and conversion
Carbohydrate transport and metabolism
Amino acid transport and metabolism
Nucleotide transport and metabolism
Coenzyme metabolism
Lipid metabolism
Secondary metabolites biosynthesis, transport and catabolism

General function prediction only
Function unknown

Signal transduction mechanisms

Transcription

Poorly characterized

Length: 3,934,217 bp; Genes; 4,223BASYS: Sunday March 05 01:31:32 2017

(a)

Figure 4: Continued.

7BioMed Research International

Blast searches of the 16S rRNA gene sequence of C-1
showed that it was most similar to other B. amyloliquefaciens
isolates. B. amyloliquefaciens isolates appear to group into
two clades indicated by phylogenetic tree analysis (Fig S3).
Although C-1 is in the same clade with known strains such
as DSM7, ATCC19217, and ATCC 14580, it appears to be
phylogenetically distant from most other isolates.

3.5. Secondary Metabolites from B. amyloliquefaciens Strains.
As much as 8.5% of the B. amyloliquefaciens C-1 genome
CDS was assigned to categories related to the secondary
metabolites responsible for the control of pathogens
(“Motility and Chemotaxis” [85 CDS], “Membrane Trans-
port” [71 CDS], “Virulence, Disease and Defense” [70
CDS], “Secondary Metabolism” [6 CDS], and “Stress
Responses” [108 CDS]). For the functional categories of
genes, a possible role in bacteria inhibition may be impor-
tant. In the carbohydrate transport and metabolism cate-

gory, 437 genes (10.9% of total genes) were predicted in
the C-1 genome. This finding suggests that C-1 possesses
a broad battery of genes coding for enzymes required to
release a variety of environmental carbon sources.

As a Bacillus. spp., B. amyloliquefaciens possesses an
enormous potential to synthesize bioactive secondary metab-
olites, especially nonribosomal-synthesized peptides and
polyketides. For the nonribosomal peptide synthetases
(NRPSs) and polyketide synthases (PKS), we used anti-
SMASH to identify related giant gene clusters (Table 3,
Table S1). C-1 was found to harbor genes encoding six
nonribosomal peptides (surfactins (srfABCD), iturins
(ituABCD), fengycins (fenABCDE), bacillibactin
(bmyABC), teichuronic, and bacilysin) and three polyketides
(bacillaene (baeEDLMNJRS), difficidin (difABCDEFGHIJ),
and macrolactin (mlnABCDEFGHI)). Compared with other
sequenced B. amyloliquefaciens strains, fengycin, difficidin,
bacillibactin, bacilysin, macrolactin, and bacillanen showed

Environmental information processing(535)

Unclassified (417)
Genetic information processing (509)

Carbohydrate metabolism (258)
Amino acid metabolism (235)
Cellular processes (214)
Metabolism of cofactors and vitamins (159)
Enzyme families (120)
Energy metabolism (116)

Lipid metabolism (90)
Nucleotide metabolism(85)
Organismal systems (30)
Xenobiotics biodegradation and metabolism(33)
Biosynthesis of other secondary metabolites(41)
Metabolism of other amino acids (45)
Glycan biosynthesis and metabolism(66)
Metabolism of terpenoids and polyketides (66)
Human diseases (84)

(b)

Figure 4: The genome map (a) of B. amyloliquefaciens C-1 and overview of the subsystem category coverage of the C-1 genome based on
RAST serve (b). The red circle is the CDS of the forward strand, and the blue circle is the CDS of the reverse strand. The outer circle
represents the categorization of predicted protein coding sequences in the C-1 genome in COG annotation, and the inner circle represents
the genes encoding function RNA.

8 BioMed Research International

100% identity, whereas surfactins had 82% identity (Table S1).
Surfactin can inhibit awide range ofmicroorganismsdue to its
ability to insert into the cell wall and create ion pores.
Bacillomycin D, iturin, and fengycin have antifungal
properties primarily based on their ability to disrupt the cell
wall [30, 31]. Macrolactins, important 24-membered
macrolactones produced by Bacillus spp., exhibit
antimicrobial activities, where macrolactin A and E and
succinyl macrolactin are the representative compounds. And
it is assembled by a modular PKS system like macrolides,
which could inhibit the H+-transporting ATPase of the
bacterial cells [32]. Polyketide compounds inhibit a wide
range of microorganisms by preventing protein synthesis
[31]. The identity of surfactin among different strains varied
from 82% to 96%, including subunit genes of SrfAA, SrfAB,
SrfAC, and SrfAD (Fig S4). C-1 had all the subunit genes,
SrfAA, SrfAB, SrfAC, and SrfAD, and the mutation of
subunit genes in other strains may indicate loss of the ability
to synthesize secondary metabolites [33].

PCR experiments detected C-1 surfactin, iturin, and
fengycin genes, an intact Bac operon that included Bacilysin
biosynthesis proteins BacA, BacB, BacC, BacD, and BacE
(ORF3909-3913), and the oligopeptide permease operon
(ORF1362-1366). Bac proteins, nonribosomally synthesized
dipeptides active against a range of bacteria and some fungi,
are involved in the biosynthesis of bacilysin. The proteolysis
of this dipeptide releases the nonproteinogenic amino acid
L-anticapsin, which functions as a competitive inhibitor of
glucosamine synthase and can cause lysis of fungal cells
[34]. Because there was 100% sequence identity of bacilysin,

whereas there was no antifungal activity of the C-1 lipopep-
tide and exopolysaccharide, the regulation and expression
of the encoded Bac operon, and modification of produced
bacilysin, deserve more analysis.

Other antimicrobial gene clusters were predicted by anti-
SMASH, such as the lantibiotic amylolysin [35], the bacteri-
ocin amylocyclicin [36], and the aminoglycoside antibiotic
butirosin [37]. These antibacterials have not been detected
by chemical analysis from C-1 supernatants, possibly because
of the fermentation medium or condition that had been used
[38]. Potential gene clusters may explain the broad activity of
C-1 against pathogens.

We also detected in the C-1 genome other beneficial
genes, such as phospholipase (ORF767) and siderophore
production genes. There were seven gene clusters responsible
for siderophore production and iron acquisition, including
an ABC-type Fe3+-siderophore transport system (ORF1-2,
ORF3417-3418, 4007-4008), an Fe-bacillibactin uptake sys-
tem (ORF413-415), an iron compound ABC uptake trans-
porter (ORF624-627), and a siderophore biosynthesis
protein (ORF1221-1222, 3262-3267). These proteins enable
bacteria to sequester iron complexes produced by other path-
ogens and antagonize certain pathogens [38, 39].

Moreover, we also detected genes for amphiphilic
membrane-active biosurfactants and peptide antibiotics that
have powerful antibacterial and mosquito larvicidal activity.
The giant gene clusters add to the capacity of the C-1 bac-
terium to contribute to the antimicrobial activity against
C. difficile. And we also checked the biosafety of the C-1
strain by using Galleria mellonella and intestinal epithelial

Table 3: Identification of gene clusters potentially involved in the synthesis of secondary metabolites by B. amyloliquefaciens C-1.

Clustera Typeb Fromc Toc Secondary metabolited

1 Saccharide 165858 190731 Unknown

2 NRPS 556597 622004 Surfactin

3 Fatty acid 785309 810347 Unknown

4 NRPS 938299 967890 Iturins

5 Other KS 1158436 1199680 Butirosin

6 Fatty acid 1227747 1248724 Unknown

7 Terpene 1281720 1302460 Unknown

8 Fatty acid 1314519 1339344 Citrulline

9 Putative 1378928 1396630 Molybdenum cofactor

10 Lantipeptide 1406687 1451837 Unknown

11 Transatpks 1624433 1706630 Macrolactin

12 Transatpks-NRPS 1932737 2035409 Bacillaene

13 Transatpks-NRPS 2100037 2237835 Fengycin

14 Terpene 2263057 2284940 Unknown

15 T3PKS 2348257 2389357 Unknown

16 Transatpks 2504342 2604794 Difficidin

17 Bacteriocin-NRPS 3235204 3301995 Bacillibactin

18 Saccharide 3504667 3530078 Unknown

19 Saccharide 3624018 3678829 Teichuronic

20 Saccharide 3823278 3895655 Bacilysin
aClusters identified using default settings of antiSMASH 5.0. bClass of gene cluster according to antiSMASH 5.0. cLocation of gene clusters in the B.
amyloliquefaciens C-1 genome. dSecondary metabolites potentially produced based on the gene clusters.

9BioMed Research International

cells, which all indicated the safety of B. amyloliquefaciens
C-1. A future study of how these gene clusters are
expressed and regulated will help explain the synthesis of
antimicrobial lipopeptides and augment our knowledge
for the control of CDI.

4. Conclusions

B. amyloliquefaciens C-1 fermentation supernatant contains
a mixture of lipopeptides, namely, surfactin and fengycin,
which had a strong inhibitory effect on C. difficile growth
and viability. Systematic research of the antibacterial mecha-
nism showed that the C-1 lipopeptide damages the integrity
and permeability barrier of the cell wall and cell membrane,
then leads to C. difficile cell death. The ~3.93Mbp genome
of C-1 reveals the genetic basis of its antimicrobial activity,
and the antimicrobial compound-encoding gene clusters
provide better understanding of the antibacterial mecha-
nisms of this strain. Furthermore, the genome analysis will
facilitate the production of effective probiotics that inhibit
multidrug resistant pathogens in the host intestinal ecosys-
tem, especially the phospholipase- and siderophore-
producing clusters. Until now, the anti-C. difficile activities
of the bacteriocins were known predominantly from
in vitro studies; thus, the in vivo efficacies of the majority of
these bacteriocins deserve further investigation.

Data Availability

The data used to support the findings of this study are
available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Authors’ Contributions

Jia Lv and Rong Da contributed equally to this work.

Acknowledgments

This work was financially supported by the National Natural
Science Foundation of China (81673199) and the National
Science Basic Research Plan in Shaanxi Province of China
(2018JM7054).

Supplementary Materials

Fig S1: the isolation, purification, and verification of the B.
amyloliquefaciens C-1 lipopeptide. (A) The acid precipitated
crude lipopeptide; (B) the UV-VIS spectrophotometer scan-
ning analysis of crude lipopeptide; (C) the purified lipopep-
tide isolated by a TLC plate; (D) the PCR detection of
lipopeptide synthesis-related genes fenB, srf, and ituD in
the C-1 genome; phylogenetic tree of PCR fragments fenB
(E), srf (F), and ituD (G). Fig S2: effect of the C-1 lipopeptide
on AKPase in C. difficile ATCC 9689 (∗P < 0:05, ∗∗P < 0:01
indicated statistically significant differences of C-1 lipopep-
tide treatments vs. negative control). Fig S3: neighbor-

joining phylogenetic tree based on 16S rRNA gene sequences
of B. amyloliquefaciens strains. 16S rRNA gene sequences
were from 16S ribosomal RNA gene partial sequence or
directed from the genome annotation in NCBI with accession
numbers of C-1 (JX028840), LL3 (CP002634.1), XH7
(CP002927.1), ATCC 13952 (CP009748.1), DSM7 (NC_
014551), SRCM101267 (CP021505.1), Y2 (HE774679.1),
UMAF6614 (NZ_CP006960), 19217 (CP009749.1), TA208
(CP002627.1), ATCC 14580 (CP000002.3), RD7-7
(CP016913.1), S499 (CP014700.1), LFB112 (NC_023073),
CC178 (CP006845.1), L-H15 (CP010556.1), L-S60
(CP011278.1), B15 (KT923051.1), KHG19 (NZ_CP007242),
ATCC 7050 (DQ297928.1), Y14 (NZ_CP017953), 168 (NC_
000964), IT45 (NC_020272), ATCC 14581 (JQ579621.1),
WS-8 (CP018200.1), LM2303 (MN640968.1), UMAF6639
(GCA_001593765), and DSM 319 (KM051080.1). Fig S4:
phylogenetic tree of genome-sequenced B. amyloliquefaciens
strains based on the amino acid sequences of surfactin synthe-
tases SrfAA, SrfAB, SrfAC, and SrfAD, and the comparison of
the gene loci strain C-1 (SRP127533), KHG19 (NZ_
CP007242), UMAF6639 (GCA_001593765), UMAF6614
(NZ_CP006960), LFB112 (NC_023073), IT45 (NC_
020272), DSM7 (NC_014551), Y14 (NZ_CP017953), and
168 (NC_000964). Table S1: comparison of gene clusters
potentially involved in the synthesis of secondary metabolites
in B. amyloliquefaciens-sequenced strains. (Supplementary
Materials)

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11BioMed Research International

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articles for individual use.

• Mechanism of Antibacterial Activity of Bacillus amyloliquefaciens C-1 Lipopeptide toward Anaerobic Clostridium difficile

1. Introduction

2. Material and Methods

2.1. Bacterial Strains and Culture

2.2. Isolation and Identification of B. amyloliquefaciens C-1 Extracellular Lipopeptide

2.3. Thin-Layer Chromatography

2.4. Semipreparative HPLC Analysis

2.5. Detection of Lipopeptide Synthesis-Related Genes

2.6. The Inhibitory Activity of Lipopeptide against C. difficile

2.7. Growth of C. difficile Incubated with Lipopeptides

2.8. Scanning Electron Microscope Analysis of C. difficile Cells Treated with Lipopeptides

2.9. Fluorescence Microscope Analysis of C. difficile Cells Treated with Lipopeptides

2.10. Determination of Extracellular Alkaline Phosphatase Activity of C. difficile Cells Treated with Lipopeptide

2.11. Lipopeptide Binding to C. difficile Genomic DNA

2.12. Whole Genome Sequencing of B. amyloliquefaciens C-1

2.13. Statistical Analysis

3. Results and Discussion

3.1. Production, Purification, and Identification of C-1 Lipopeptide

3.2. Antimicrobial Activity of C-1 Lipopeptide against C. difficile

3.3. Effect of C-1 Lipopeptide on C. difficile Morphology, Cell Wall Permeability

3.4. Genome Sequencing of B. amyloliquefaciens C-1

3.5. Secondary Metabolites from B. amyloliquefaciens Strains

4. Conclusions

Data Availability

Conflicts of Interest

Authors’ Contributions

Acknowledgments

Supplementary Materials

52

October & December 2019. Vol 22. Issue 4

* Corresponding Author:
Pegah Mohaghegh, MD.
Address: Department of Community Medicine, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
Tel: +98 (86) 34173521
E-mail: pmohaghegh@arakmu.ac.ir

1. Department of Infectious Diseases, Infectious Diseases Research Center (IDRC), Arak University of Medical Sciences, Arak, Iran.
2. Department of Microbiology and Immunology, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
3. Department of Clinical Research, Pasteur Institute of Iran, Tehran, Iran.
4. Department of Community Medicine, School of Medicine, Arak University of Medical Sciences, Arak, Iran.

Masoomeh Sofian1 , Elahe Eghbal1, Ehsanollah Ghaznavi-Rad2, Amitis Ramezani3, *Pegah Mohaghegh4

Citation: Sofian M, Eghbal E, Ghaznavi-Rad E, Ramezani A, Mohaghegh P. [The Effect of Probiotic Yogurt on the Frequency
of Clostridium Difficile in Old Hospitalized Patients (Persian)]. Journal of Arak University of Medical Sciences (JAMS). 2019;
22(4):52-65. https://doi.org/10.32598/JAMS.22.4.50

: https://doi.org/10.32598/JAMS.22.4.50

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and read the article online

Background and Aim Clostridium difficile is the main cause of Antibiotic-Associated Diarrhea (AAD) in
the hospital setting. Today, the use of probiotics for the prevention and treatment of AAD and colitis is
increasing. In this study, we investigated the effect of probiotic yogurt on the frequency of Clostridium
difficile.
Methods and Materials In this randomized clinical trial study, 132 elderly patients admitted to the infec-
tious ward of Vali-e-Asr Hospital in Arak, who were under antibiotic treatment, were randomly divided
into two groups, case (yogurt probiotic, 200 mg/d for 8 days) and control group (common yogurt). All
patients were trained about the signs of colitis. We evaluated the colitis signs and the presence of Clostrid-
ium difficile by Polymerase Chain Reaction (PCR) and compared them between the groups. The obtained
data were analyzed with appropriate statistical tests in SPSS V. 16.
Ethical Considerations The Research Ethics Committee of Arak University of Medical Sciences approved
this study (Code: 10-165-93). Also, it was registered at the Iranian Registry of Clinical Trials (Code:
IRCT2016092229915N1).
Results Clostridium difficile was detected in 4 (6.1%) patients of the case, and 1 (1.5%) patient of the con-
trol group, at the beginning of the study. There was no significant difference between the frequency of
Clostridium difficile and colitis syndrome between two groups at the end of the study (P>0.05).
Conclusion Probiotic yogurt has no significant effect in reducing the frequency of Clostridium difficile and
colitis syndrome in our study.

Key words:
Clostridium difficile,
Probiotic yogurt

Article Info:
Received: 18 Jun 2019

Accepted: 07 Sep 2019

Available Online: 01 Oct 2019

Research Paper
The Effect of Probiotic Yogurt on the Frequency of Clostridium Difficile in Old
Hospitalized Patients

A B S T R A C T

Extended Abstract

1. Introduction

lostridium difficile is a Gram-positive,
spore-forming, toxin-producing anaerobic
bacillus and is a component of the intes-
tinal flora in healthy infants. It is also a

C
known cause of pseudomembranous colitis and diarrhea in
patients under antibiotic therapy [1, 2]. The causative agent
of Clostridium Difficile Infection (CDI) is exotoxins A and
B produced by this bacterium [3, 4].

Probiotics are micronutrients (bacteria and yeast) that set-
tle in different parts of the body, especially in the intestine,
modify the microbial flora and exert beneficial effects on

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October & December 2019. Vol 22. Issue 4

Sofian M, et al. Probiotic Yogurt and Clostridium Difficile. JAMS. 2019; 22(4):52-65.

the health of the host [6]. A significant percentage of pa-
tients admitted to infectious disease wards are older people
who need long-term antibiotic therapy, and increased colo-
nization of Clostridium difficile in them is associated with
a higher risk of CDI [8-12]. The use of probiotic yogurt
can prevent the growth of Clostridium difficile and reduce
morbidity and even mortality in these patients [7]. Of the
studies on the effect of probiotics on CDI, few studies have
investigated the impact of probiotics on the frequency of C.
difficile in older people. In this regard, this study investi-
gates the effect of probiotic yogurt use on the rate of Clos-
tridium difficile in hospitalized elderly patients.

2. Materials and Methods

In this double-blind, randomized clinical trial, 132 patients
over 60 years old who were admitted to the infectious Ward
of Vali-e-Asr Hospital in Arak, Iran were selected through
convenience sampling method. They were diagnosed with
acquired pneumonia and under antibiotic therapy using
ceftriaxone and azithromycin. After obtaining informed
consent from them, they were randomly divided into two
groups of intervention (n=66) and control (n=66).

The patients in the intervention group received 200 mL
probiotic yogurt from day 1 to day 8 of hospitalization twice
per day (lunch and dinner). The control group received 200

mL of regular yogurt for the same period, both at lunch and
dinner. In the first and eighth days of the study, all stool
samples were taken, and their DNA extracted, and the ex-
tract was used in a Polymerase Chain Reaction (PCR) with
16S RNA and srRNA Universal primers. The PCR product
was sent for nucleotide sequencing, and the results under-
went BCAST analysis. Mean and Standard Deviation (SD)
were used to describe the quantitative data and the number
and percentage were used to describe the qualitative data.

3. Results

At the beginning of the study, the prevalence of Clostrid-
ium difficile in the intervention and control groups was 4
(6.1%) and 1 (1.5%), respectively. After the intervention,
the number of stool samples with CDI and the number of
cases with antibiotic-associated colitis symptoms were not
significantly different between the intervention and con-
trol groups (P <0.05). The comparison of symptoms be-
tween the two groups showed that the prevalence of fever
(P=0.039) and constipation (P=0.046) was higher in the
intervention group at the beginning of the study, and the
difference was statistically significant (Table 1). Further-
more, the study showed that the likelihood of developing
CDI in people with a history of cerebrovascular diseases
was 1.69 times higher than those with no such history. The

Table 1. Frequency of clinical symptoms and C. difficile on the first and eighth day of study in the two study groups

Variable
No. (%)

P
Intervention Control

Day 1 (baseline)

Stomachache 5 (7.6) 1 (1.5) 0.095*

Fever 26 (4.39) 15 (7.22) 0.039*

Anorexia 20 (3.30) 13 (7.19) 0.159*

Constipation 17 (8.25) 8 (1.12) 0.046*

Diarrhea 0 (0) 0 (0) –

Nausea 6 (1.9) 7 (6.10) 0.770*

Vomiting 1 (5.1) 3 (5.4) 0.310*

Test positive for C. difficile on the first day 4 (1.6) 1 (5.1) 0.171*

Day 8

Stomachache 0 (0) 0 (0) –

Fever 0 (0) 0 (0) –

Anorexia 5 (6.7) 7 (6.10) 0.545*

Constipation 11 (7.16) 11 (7.16) 1*

Diarrhea 0 (0) 0 (0) –

Nausea 0 (0) 0 (0) –

Vomiting 0 (0) 0 (0) –

Test positive for C. difficile on the eighth day 0 (0) 0 (0) –

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October & December 2019. Vol 22. Issue 4

history of antibiotic use in the past 6 months also increased
the risk of developing CDI by 1.97 times.

4. Discussion

This randomized clinical trial study was performed to
determine the effect of probiotic yogurt on the incidence
of C. difficile in 132 patients (mean age: 72 y) admitted to
the Infectious Disease Ward of Vali-e-Asr Hospital. At the
beginning of the study, the prevalence of C. difficile was
3.8%. Also, in positive cases, the relative frequency of fe-
ver was higher. Based on the results of this study, a history
of corticosteroid use, antibiotic use in the past 6 months,
proton pump inhibitors, history of contact with a child at
home, history of hospitalization, and history of brain injury
in patients with positive C. difficile were higher. This dif-
ference was statistically significant According to the Fisher
exact test. By considering positive C. difficile as the depen-
dent variable, history of cerebrovascular disease, antibiotic
use in the past 2 to 6 months, using proton pump inhibitors,
history of contact with child at home, history of hospitaliza-
tion in the past 6 months, history of corticosteroid use, and
fever were risk factors of CDI. In our study, there was a
decrease in the incidence of positive C. difficile on the 8th
day of the study compared to the first day, but it was not
statistically significant between the two groups.

Thus, the use of probiotic yogurt has no significant effect
on reducing the prevalence of C. difficile and colitis symp-
toms in comparison with regular yogurt. For more accurate
results, it is recommended to conduct studies with a larger
sample size in several different health centers.

Ethical Considerations

Compliance with ethical guidelines

This is a registered clinical trial (Code: IRCT20160
92229915N1) approved by the Ethics Commit-
tee of Arak University of Medical Sciences (Code:
10.165.93).

Funding

The present paper was extracted from the PhD. thesis of
the second author, Elahe Eqbal, Department of Infectious
Diseases, Infectious Diseases Research Center (IDRC),
Arak University of Medical Sciences.

Authors’ contributions

Conceptualization: Masoumeh Sufian, Elaheh Eghbal,
Pegah Mohaghegh; Research: Elaheh Eghbal, Ehsanollah

Ghaznavi Rad, Amitis Ramezani, Masoumeh Sufian; Edit-
ing and Finalization: Masoum Sufian, Pegah Mohaghegh.
Also all authors met standard writing standards based on
the recommendations of the International Committee of
Medical Journal Publishers (ICMJE).

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgements

The authors would like to thank the Infectious Disease
Ward and Laboratory staff of Vali-e-Asr Hospital for their
valuable cooperation.

Sofian M, et al. Probiotic Yogurt and Clostridium Difficile. JAMS. 2019; 22(4):52-65.

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55

مهر و آبان 1398. دوره 22. شماره 4

* نویسنده مسئول:
دکتر پگاه محقق

نشانی: اراک، دانشگاه علوم پزشکی اراک، دانشکده پزشکی، گروه پزشکی اجتماعی.
تلفن: 34173521 )86( 98+

pmohaghegh@arakmu.ac.ir :پست الکترونیکی

زمینه و هدف کلستریدیوم دیفیسیل عامل اصلی اسهال ناشی از آنتی بیوتیک در بیمارستان هاست و امروزه استفاده از پروبیوتیک برای
پیشگیری و درمان اسهال و کولیت ناشی از آنتی بیوتیک در حال افزایش است. در این مطالعه تأثیر ماست پروبیوتیک در فراوانی

کلستریدیوم دیفیسیل بررسی شده است.

مواد و روش ها در این کارآزمایی بالینی، 132 بیمار سالمند بستری در بخش عفونی بیمارستان ولی عصر اراک که تحت درمان آنتی بیوتیکی
بودند، به صورت تصادفی به دو گروه آزمایش )ماست پروبیوتیک، 200میلی گرم در روز به مدت هشت روز( و گروه کنترل )ماست معمولی(
تقسیم شدند. به همه بیماران اطالعاتی از عالئم کولیت داده شد و عالئم کولیت و نتایج تست بررسی کلستریدیوم در مدفوع بیماران
با PCR در دو گروه آزمایش و کنترل ثبت و مقایسه شد. بعد از جمع آوری داده ها، اطالعات توسط نسخه 1۶ نرم افزار آماری SPSS و با

استفاده از آزمون های آماری مناسب تجزیه و تحلیل شد.

کد با و تأیید اراک پزشکی علوم دانشگاه پژوهش اخالق کمیته در اخالق 93-1۶5-10 کد با پژوهش این اخالقی مالحظات
IRCT2016092229915N1 در مرکز ثبت کارآزمایی

بالینی ثبت شده است.

یافته ها در بدو بستری، کلستریدیوم دیفیسیل درگروه آزمایش و کنترل به ترتیب در چهار نفر)۶/1 درصد( و 1 نفر )1/5 درصد(
مثبت بود و بعد از مداخله تعداد نمونه های مدفوع آلوده به کلستریدیوم دیفیسیل و تعداد موارد ابتال به عالئم کولیت ناشی از

.)P>0/05( آنتی بیوتیک بین دو گروه آزمایش و کنترل تفاوت معنی داری نداشت

نتیجه گیری استفاده از ماست پروبیوتیک در مقایسه با ماست معمولی تأثیر معنی داری در کاهش شیوع کلستریدیوم دیفیسیل ندارد.

کلیدواژه ها:
کلستریدیوم دیفیسیل،

پروبیوتیک، ماست

اطالعات مقاله:
تاریخ دریافت: 28 خرداد 1398

تاریخ پذیرش: 16 شهریور 1398
تاریخ انتشار: 09 مهر 1398

مقدمه

مثبت، گرم غیرهوازی باسیل یک دیفیسیل کلستریدیوم
اسپوردار، تولیدکننده توکسین است که اولین بار در سال 1935
کشف شد. این باکتری می تواند به شکل اسپور و شکل وژتاتیو
)رویشی( وجود داشته باشد. اسپورها در مقابل حرارت، اسید و
آنتی بیوتیک ها مقاوم اند و در کولون به فرم وژتاتیو تبدیل و به
شکل تولیدکننده توکسین در می آیند. کلستریدیوم دیفیسیل
جزء فلور روده در نوزادان سالم به حساب می آیند[2 ،1] . نقش
آسیب شناختی کلستریدیوم اولین بار در سال 1970 مشخص
مدفوع در دیفیسیل کلستریدیوم توکسین که هنگامی شد،

بیماران با کولیت سودوممبران1 )با غشای کاذب( در ارتباط با
آنتی بیوتیک کشف شد. این ارگانیسم تازه در آن زمان به عنوان
عامل کولیت پسودوممبران و اسهال و کولیت در بیمارانی که در

معرض آنتی بیوتیک بودند شناخته شد [2 ،1].

کلستریدیوم دیفیسیل با ایجاد دو اگزوتوکسین موجب کولیت
B توکسین و )انتروتوکسین( A توکسین می شود: اسهال و
)سایتوتوکسین( [3]. توکسین A موجب التهاب می شود که این
التهاب به ترشح مایعات روده ای، آسیب مخاطی و التهاب منجر
می شود. توکسین B تقریبًا 10 برابر بیشتر از توکسین نوع A در

1. Pseudomembranous Colitis

1- گروه بیماری های عفونی، مرکز تحقیقات بیماری های عفونی، دانشگاه علوم پزشکی اراک، اراک، ایران.
2- گروه میکروبیولوژی، دانشکدهپزشکی، دانشگاه علوم پزشکی اراک، اراک، ایران.

3- گروه تحقیقات بالینی، انستیتو پاستور ،تهران، ایران.
4- گروه پزشکی اجتماعی، دانشکده پزشکی، دانشگاه علوم پزشکی اراک، اراک، ایران.

تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

،‌الهه‌اقبال1،‌احسان‌اهلل‌غزنوی‌راد2،‌آمیتیس‌رمضانی3،‌*پگاه‌محقق‌4 معصومه‌صوفیان1

https://orcid.org/0000-0001-5679-9796

https://orcid.org/0000-0002-8564-3659

56

مهر و آبان 1398. دوره 22. شماره 4

A آسیب مخاط کولونی نقش دارد. گونه هایی که توکسین نوع
را ندارند، می توانند از نظر ویروالنس مشابه گونه هایی باشند که
هر دو نوع توکسین را دارند. تعداد کمی از گونه های کلستریدیوم
در می توانند گونه ها این نمی کنند. تولید توکسین دیفیسیل
دستگاه گوارش رشد و تکثیر شده و جزء فلور نرمال شوند و

بیماری زا نیستند [5 ،4 ،1].

باسیل کلستریدیوم از ناشی عفونت کلستریدیوم دیفیسیل
دیفیسیل اسپوردار گرم مثبت و بی هوازی است که توکسین های
Aو B و Binary toxin تولید می کند و اسپور آن در محیط،
.[1] است موجود نگهداری مراکز و بیمارستان ها به خصوص
این توکسین ها فرایندهایی را ایجاد می کنند که موجب اختالل
می شود. کاذب غشای تشکیل و اسهال اپی تلیال، عملکرد
مرگ و میر نسبت داده شده به عفونت کلستریدیوم دیفیسیل2 قباًل
معادل 0/۶-3/5 بوده و در همه گیری های اخیر به ۶/9 رسیده
بیشتر می شود پیشرونده ای به صورت افزایش سن با و است
[3]. به طور معمول کمتر از 5 درصد بزرگ ساالن با کلستریدیوم
دیفیسیل کولینیزه هستند که این رقم در بیماران پس از بستری
در بیمارستان به 20 تا 30 درصد می رسد، حتی می تواند کولیت
فولمینانت بدهد که نیاز به مداخله جراحی دارد و در سال های
اروپا و اخیر شیوع و شدت عفونت کلستریدیوم دیفیسیل در
آمریکا افزایش یافته است که در سال های اخیر شیوع و شدت
عفونت کلستریدیوم دیفیسیل در اروپا و آمریکا افزایش یافته است
که موجب افزایش ناخوشی و مرگ و میر و افزایش ضریب اشغال

تخت های بیمارستانی شده است [1].

کلیندامایسین، آمپی سیلین، آموکسی سیلین و سفالوسپورین ها
داشته اند. همراهی CDI با که بودند آنتی بیوتیک هایی اولین
سفتریاکسون، به خصوص سه و دو نسل سفالوسپورین ها
که هستند داروهایی سفتازیدیم، سفوروکسیم، سفوتاکسیم،
فلوروکینولون ها و هستند وضعیت این مسئول همه از بیش
فلوکساسین( موکسی لووفلوکساسین، )سیپروفلوکساسین،
جدیدترین دسته داروهای مسبب این بیماری در همه گیری های
اخیر هستند، با وجود این، تمام آنتی بیوتیک ها حتی وانکومایسین
و مترونیدازول که برای درمان CDI به کار می روند، خطر CDI را

دربر دارند [3].

که هستند زنده ای ارگانیسم های پروبیوتیک ها طرفی از
با تعدیل فلور میکروبی روده، اثرات مفیدی بر سالمت میزبان
اعمال می کنند [۶]. سازوکار اثر پروبیوتیک ها کاماًل شناخته شده
اثرات پیشگیری کننده نیست، ولی مکانیسم هایی برای توجیه
است که پیشنهاد شده انسان بیماری های در آن ها درمانی و
باکتری ها، مهارکننده ترکیبات تولید به می توان آن جمله از
تعدیل PH روده، بلوک جایگاه های اتصال باکتری ها، رقابت برای

2. Clostridium Difficile Infection (CDI)

جذب مواد غذایی و تقویت سیستم ایمنی اشاره کرد [7 ،۶].
پروبیوتیک ها، ریززنده هایی )باکتری ها و مخمرهایی( هستند که
با استقرار در بخش های مختلف بدن )اساسًا روده به عنوان فلور
طبیعی(، از طریق دریافت خوراکی یا کارگذاری موضعی، با عمل
زیستی خود، از طریق حفظ و بهبود توازن فلور میکروبی روده
)میان ریززنده های سودمند و زیان بخش(، سبب ایجاد خواص
سالمت بخش برای میزبان می شوند [7 ،۶]. گوناگونی ریززنده های
بدین معنا که بوده است، افزایش پروبیوتیک همواره در حال
پژوهش های نوین به شناخت نژادها، گونه ها و جنس های جدیدی
از ریززنده ها که خواص پروبیوتیکی دارند، منجر می شود [7 ،۶].

درصد قابل توجهی از بیماران بستری در بخش های عفونی،
افراد سالمندی هستند که نیاز به درمان آنتی بیوتیکی به صورت
طوالنی مدت دارند [9 ،8]. افزایش کلونیزاسیون کلستریدیوم
دیفیسیل در این بیماران و درمان عفونت کلستریدیوم دیفیسیل
با آنتی بیوتیک هایی مثل مترونیدازول و وانکومایسین خود با خطر
مصرف .[10-12] است همراه دیفیسیل کلستریدیوم عفونت
ماست پروبیوتیک با خواص ذکرشده می تواند از رشد کلستریدیوم
و کولیت بیمارستانی، اسهال موارد عمده عامل که دیفسیل
باعث کاهش کولیت سودوممبرانوس است، جلوگیری کرده و
موربیتی و حتی مورتالیتی این بیماران شود [7]. در میان مطالعات
انجام شده در خصوص اثر پروبیوتیک ها بر عفونت کلستریدیوم
دیفیسیل، تعداد مطالعاتی که به بررسی اثر پروبیوتیک بر فراوانی
کلستریدیوم دیفیسیل در گروه سنی سالمندان پرداخته اند، ناچیز
اثر مصرف ماست بررسی با هدف این پژوهش بنابراین است.
پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند

بستری در بخش عفونی بیمارستان ولی عصر اراک انجام شد.

مواد و روش ها

در این مطالعه کارآزمایی بالینی تصادفی شده دوسوکور ، تعداد
132 بیمار باالی۶0 سال که با تشخیص پنومونی اکتسابی در
تحت و بستری اراک شهر ولی عصر بیمارستان عفونی بخش
درمان آنتی بیوتیکی با سفتریاکسون و آزیترومایسین بودند، به
بیماران برای انتخاب شدند. پروتکل مطالعه شیوه دردسترس
توضیح داده شد و بیماران فرم های رضایت نامه آگاهانه را تکمیل
کردند. بیماران به صورت تصادفی به دو گروه ۶۶نفره آزمایش و

کنترل تقسیم شدند.

معیارهای ورود به مطالعه شامل سن بیشتر از ۶0 سال، درمان
دریافت نکردن ، آزیترومایسین و سفتریاکسون آنتی بیوتیک با
آنتی بیوتیک در دو ماه گذشته، کسب رضایت شرکت در مطالعه
هنگام در اسهال نبود و HIV به مبتالنبودن بیمار، توسط
بستری شدن بود. معیارهای خروج از مطالعه شامل تمایل نداشتن
بیمار برای شرکت در مطالعه یا ناتوانی در اجرای دستورها، ترخیص
بیمار قبل از هفت روز، ابتال به HIV و نیاز به بستری در ICU بود.

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

57

مهر و آبان 1398. دوره 22. شماره 4

بیماران گروه آزمایش از روز اول بستری 200 سی سی ماست
پروبیوتیک در دو نوبت نهار و شام دریافت کرده و بیماران گروه
کنترل نیز از روز اول بستری 200 سی سی ماست معمولی در
دو نوبت نهار و شام دریافت می کردند. با هماهنگی کارشناس
تغذیه بیمارستان، ماست پروبیوتیک کاله کم چرب با حفظ زنجیره
سرما و توضیح چگونگی سرو ماست در پیاله های ماست خوری
معمول بیمارستان که از لحاظ ظاهری، تفاوتی با سرو ماست
معمولی نداشت و فقط با نام ماست یک و ماست دو تهیه و توزیع
شد و تا تکمیل حجم نمونه ادامه یافت. با هماهنگی با واحد
آزمایشگاه در روز اول و هشتم بستری، نمونه مدفوع یا سوآپ
رکتال توسط دستیار اخذ و با حفظ شرایط به آزمایشگاه منتقل
می شد. از نمونه مدفوع تمامی افرادی که وارد طرح می شدند،
به میزان دو گرم به طور مستقیم در محیط PBS برای استخراج
DNA برده شده و در فریزرC °20 نگهداری می شدند. برای انجام
ابتدا در مطالعه شده برای ژن های پلیمراز3 زنجیرهای واکنش
DNA توسط کیت یکتاتجهیز مستقیمًا از نمونه مدفوع استخراج
شد. محصوالت PCR برای تعیین توالی نوکلئوتیدی به شرکت
ژن فناوران ارسال شد و نتایج تحلیل BCAST حاکی از ناحیه
توکسین های A و B کلستریدیوم دیفیسیل بود. برای اینکه ثابت
کنیم اسید نوکلئیک باکتری در نمونه استخراج شده ما هست،
یک PCR برای 16SRNA انجام دادیم. برای اطمینان از استخراج
و اراک رازی و سرم سازی واکسن مؤسسه نانودراپ از ،DNA

پرایمرهای 16srRNAUniversal باکتریایی استفاده شد.

SPSS پس از جمع آوری داده ها و ورود به محیط نرم افزار آماری
)نسخه 1۶( با استفاده از تست های آماری مناسب کای اسکویر،
تحلیل و تجزیه داده ها تی تست مستقل و فیشر دقیق تست
شدند. برای توصیف داده های کمی از میانگین و انحراف معیار و
برای توصیف داده های کیفی از تعداد و درصد استفاده شد. سطح

خطای نوع اول 0/05 لحاظ شد.

یافته ها

در این مطالعه کارآزمایی بالینی تصادفی شده که با هدف تعیین
در دیفیسیل کلستریدیوم فراوانی بر پروبیوتیک ماست تاثیر
بیماران بستری در بخش عفونی بیمارستان ولی عصر انجام شد، دو
گروه ۶۶ نفره از بیماران واجد شرایط ورود به مطالعه، به مدت 8
روز متوالی، 200 گرم ماست پروبیوتیک )گروه آزمایش( یا ماست
معمولی )گروه کنترل( در دو نوبت دریافت کردند. هر دو گروه
به شکل 100 درصد تحت درمان با سفتریاکسون وآزیترومایسین
بودند و هیچ یک وانکومایسین یا مروپنم یا سیپروفلوکساسین

دریافت نمی کردند.

در کل نمونه ها، میانگین و انحراف معیار سن در افراد مورد
بررسی شده ۶/30±72/43 سال بود. از کل بیماران موردمطالعه

3. Polymerase Chain Reaction (PCR)

سکونت محل بررسی در بودند. مرد درصد( 50/8( نفر ۶7
بیماران، 41 نفر )31/1 درصد( ساکن شهر بودند. از 132 بیمار
بررسی شده، عفونت کلستریدیوم در پنج بیمار )3/8 درصد( مثبت
بود. شیوع عالئم اولیه و سوابق پزشکی و دارویی در کل نمونه ها

در جدول شماره 1 بیان شده است.

بررسی شده، 43/2 بیمار از 132 جدول شماره 1، اساس بر
درصد مبتال به فشار خون، 28 درصد مبتال به دیابت و 2۶/5

درصد مبتال به بیماری مزمن تنفسی بودند.

هیچ یک از دو گروه سابقه کلونوسکوپی و بستری در بخش
مراقبت ویژه در سه ماه اخیر نداشتند. در بررسی عالئم اولیه
بیماران، از 132 بیمار بررسی شده 31/1 درصد دارای تب، 25
درصد بی اشتهایی و 18/9 درصد یبوست داشتند. هیچ یک از

بیماران مبتال به اسهال نبودند.

مشخصات پزشکی، سوابق توزیع ،2 شماره جدول در
جمعیت شناختی و تاریخچه دارویی بیمار به تفکیک دو گروه
آماری تست های اساس بر است. ذکر شده کنترل و آزمایش
ایسکمیک بیماری سابقه ،2 شماره جدول ذیل در ذکرشده
تا ماه بیمارستان در سه قلبی )P=0/033(، سابقه بستری در
یک سال قبل )P=0/029(، سابقه مصرف PPI )مهارکننده های
پمپ پروتون( )P=0/032( و سابقه مصرف آنتی بیوتیک در شش
ماه گذشته )P=0/052( در ابتدای مطالعه در دو گروه، تفاوت

معنی داری داشت.

بر اساس آزمون آماری خی دو طبق جدول شماره 3، شیوع تب
)P=0/039( و یبوست )P=0/04۶( در ابتدای مطالعه در گروه
آزمایش بیشتر بوده است و این تفاوت از نظر آماری معنی دار
کلستریدیوم بروز بین فیشر، آماری آزمون اساس بر است.
دیفیسیل مثبت و سن، جنس، محل سکونت، سابقه جراحی،
سابقه اندوسکوپی، زخم پوستی و مصرف مسهل تفاوت آماری
معنی دار دیده نشد )P≥0/05( . در مقایسه موارد کلستریدیوم
دیفیسیل مثبت و منفی در شروع مطالعه بر اساس آزمون دقیق
فیشر، عالئمی نظیر درد شکم، یبوست، استفراغ و بی اشتهایی در
دو گروه تفاوت معنی دار نداشتند، ولی در موارد مثبت، فراوانی
نسبی تب بیشتر بود و بر اساس آزمون دقیق فیشر این تفاوت

.)P=0/002( شیوع از نظر آماری معنی دار است

مصرف سابقه بیمار، دارویی و پزشکی سوابق بررسی در
کورتون )P=0/001(، مصرف آنتی بیوتیک در شش ماه گذشته
)P=0/00۶(، مهارکننده پمپ پروتون )P=0/002(، سابقه تماس
)P=0/001( سابقه بستری در بیمارستان ،)P=0/004( با کودک
کلستریدیوم بیماران در )P=0/001( مغزی حوادث سابقه و
دیفیسیل مثبت بیشتر است و بر اساس آزمون دقیق فیشر، این

تفاوت از نظر آماری معنی دار است )جدول شماره 4(.

بر اساس رگرسیون لجستیک دوحالته مطابق جدول شماره 5

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

58

مهر و آبان 1398. دوره 22. شماره 4

با )سطح معنی داری: 0/05(، سابقه حوادث مغزی عروقی، سابقه
بستری قبلی در بیمارستان در دو ماه گذشته ، تماس با کودکان
در منزل، مصرف آنتی بیوتیک در دو ماه گذشته ، سابقه مصرف
کورتون و مهارکننده های پمپ پروتون و وجود تب ، ریسک عامل
مساعدکننده ابتال به عفونت کلستریدیوم دیفیسیل بود. نسبت
شانس4 ابتال به کلستریدیوم دیفیسیل در افراد با سابقه حوادث
حوادث به ابتال سابقه بدون افراد برابر 1/۶9 مغزی عروق
قلبی عروقی است. همچنین سابقه مصرف آنتی بیوتیک در شش
ماه گذشته، شانس ابتال به عفونت کلستریدیوم دیفیسیل را 1/97

4. Odd Ratio

کلستریدیوم عفونت بروز شانس نسبت می دهد. افزایش برابر
دیفیسیل در افراد با سابقه بستری در بیمارستان، سابقه تماس با
کودکان در منزل، مصرف دارو در جدول شماره 5 ذکر شده است.
شانس بروز تب در افراد مبتال به عفونت کلستریدیوم دیفیسیل

1/78 برابر افراد غیرمبتال به عفونت بود.

بحث

این مطالعه کارآزمایی بالینی تصادفی شده با هدف تعیین تأثیر
ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل بر 132 نفر
بیمار با میانگین سنی72 سال بستری در بخش عفونی بیمارستان

جدول 1. توزیع فراوانی خصوصیات جمعیت شناختی و تاریخچه در کل نمونه ها

تعداد )درصد(شاخصمتغیر

متغیر‌جمعیت‌شناختی
)50/8(‌67جنس‌)مرد(‌

)31/1(‌41محل‌سکونت‌)شهر(‌

بیماری‌زمینه‌ای

)28(‌37دیابت
)43/2(‌57فشار‌خون

)1/5(‌2حوادث‌مغزی‌عروقی
)12/1(‌16بیماری‌ایسکمیک‌قلبی
)26/5(‌35بیماری‌مزمن‌تنفسی

تاریخچه‌پزشکی

)4/5(‌6سابقه‌زخم‌پوستی
)1/5(‌2سابقه‌سابقه‌جراحی
)3/8(‌5سابقه‌اندوسکوپی
)0(‌0سابقه‌کولونوسکوپی

)6/1(‌8بستری‌در‌بیمارستان‌در‌سه‌ماه‌تا‌یک‌سال‌قبل
)0(‌0بستری‌در‌بخش‌مراقبت‌ویژه‌در‌سه‌ماه‌تا‌یک‌سال‌قبل

)8/3(‌11تماس‌با‌کودک‌در‌منزل

مصرف‌دارو‌در‌دو‌ماه‌اخیر

)20/5(‌27آنتی‌بیوتیک‌)دو‌تا‌شش‌ماه‌گذشته(
)5/3(‌7کورتون

)11/4(‌15مصرف‌مسهل‌
‌)PPI(21مهار‌کننده‌پمپ‌پورتون‌‌)15/9(

عالئم‌اولیه

)4/5(‌6درد‌شکم
)18/9(‌25یبوست
)9/8(‌13تهوع
)3(‌4استفراغ

)25(‌33بی‌اشتهایی

)31/1(‌‌41تب
)0(‌0اسهال

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

59

مهر و آبان 1398. دوره 22. شماره 4

ولی عصر انجام شد. در کل نمونه ها، سه بیماری شایع به ترتیب
فشار خون باال، دیابت و بیماری مزمن تنفسی بود. حدود 20/5
درصد بیماران سابقه مصرف آنتی بیوتیک در شش ماه گذشته را

داشتند.

شیوع کلستریدیوم دیفیسیل در این مطالعه 3/8 درصد بود و
عالئمی نظیر درد شکم، یبوست، تهوع و استفراغ و بی اشتهایی
همراهی با عفونت مثبت نداشت. ولی در موارد مثبت، فراوانی
توکسین شیوع مختلف مطالعات در بود. بیشتر تب نسبی
کلستریدیوم دیفیسیل و اسهال ناشی از کلستریدیوم در بیماران
بستری در بیمارستان در طیف گسترده ای از 0/1 درصد تا 2
درصد گزارش شده است [17-13]. در مطالعه کوهورت اولسن
و همکاران نیز در یک پی گیری ده ساله ، میزان بروز ساالنه 1
تا 4 درصد به دست آمد [15]. در مطالعه زرین فر و همکاران
در همین بیمارستان بر 195 نفر بیمار بررسی شده 14/4 درصد
توکسین کلستریدیوم مثبت بودند و تنها 4/1 درصد اسهال ناشی
از کلستریدیوم داشتند [14]. در مطالعه پپن و همکاران در فاصله
سال های 1991 تا 2003 شیوع کلستریدیوم دیفیسیل کاهش
چشمگیری پیدا کرده است و عمومًا جمعیت سالمندی در معرض

خطر بیشتر همراه با مرگ ومیر بیشتر قرار گرفته است [18].

بر اساس نتایج مطالعه حاضر، سابقه مصرف کورتون، مصرف
آنتی بیوتیک در شش ماه گذشته، مهارکننده پمپ پروتون، سابقه
تماس با کودک، سابقه بستری در بیمارستان و سابقه حوادث
مغزی در بیماران کلستریدیوم دیفیسیل مثبت بیشتر است و بر
اساس تست دقیق فیشر این تفاوت از نظر آماری معنی دار است.
همچنین بر اساس آزمون آماری رگرسیون لجستیک دومتغیره،
با احتساب کلستریدیوم دیفیسیل مثبت به عنوان متغیر وابسته،
سابقه حوادث مغزی عروقی، بستری در بیمارستان در شش ماه
گذشته، تماس با کودکان در منزل، سابقه مصرف آنتی بیوتیک در
دو تا شش ماه گذشته، مصرف کورتون، مصرف مهارکننده های
پمپ پروتون و وجود تب، ریسک عامل بیماری بودند. اما سن،
جنس، محل سکونت، سابقه جراحی، سابقه اندوسکوپی، زخم
پوستی و نوع مصرف آنتی بیوتیک، مصرف مسهل شانس ابتال به

بیماری را افزایش نداد.

در مطالعه ویلکاکس و همکاران، شیوع کلستریدیوم دیفیسیل
در بیماران باالی 70 سال بستری در بیمارستان 2/1 درصد ذکر
شده است و مصرف آنتی بیوتیک در چهار هفته گذشته، به ویژه
استفاده همزمان از چند دارو، مصرف سفالوسپورین ها و بستری
در بیمارستان در شش ماه گذشته ریسک عامل بیماری معرفی

جدول 2. توزیع متغیرهای جمعیت شناختی و سوابق پزشکی و عوامل خطر در دو گروه آزمایش و کنترل

Pماست معمولیماست پروبیوتیکمتغیر

متغیرهای‌جمعیت‌شناختی

+‌6/620/784±‌‌6/0172/28±‌72/59سن‌)میانگین‌±‌انحراف‌معیار(

*‌0/384)54/5(‌36)47(‌31جنسیت‌)مرد(‌تعداد‌)درصد(

*‌0/573)33/3(‌22)28/8(‌‌19محل‌سکونت‌)شهر(‌تعداد‌)درصد(

سوابق‌پزشکی‌و‌عوامل‌خطر‌تعداد‌
)درصد(

*‌0/081)21/2(‌14)34/8(‌23دیابت

*‌0/219)37/9(‌25)48/5(‌32فشارخون

**‌0/496)0(‌0)0/3(‌2حوادث‌عروق‌مغزی

*‌0/033)6/1(‌4)18/2(‌12بیماری‌ایسکمیک‌قلبی

*‌0/844)27/3(‌18)25/8(‌17بیماری‌مزمن‌تنفسی

*‌1)4/5(‌3)4/5(‌3سابقه‌زخم‌پوستی

**‌0/496)0(‌0)3(‌2سابقه‌جراحی

*‌0/648)3(‌2)4/5(‌3سابقه‌اندوسکوپی

*‌0/029)1/5(‌1)10/6(‌7سابقه‌بستری‌در‌بیمارستان‌در‌سه‌ماه‌تا‌یک‌سال‌قبل

*‌0/345)6/1(‌4)10/6(‌7تماس‌با‌کودک‌در‌منزل

سابقه‌مصرف‌دارو‌تعداد‌)درصد(

*‌0/698)4/5(‌3)6/1(‌4سابقه‌مصرف‌کورتون‌

*‌0/411)9/1(‌6)13/6(‌9سابقه‌مصرف‌مسهل‌

*‌0/032)9/1(‌6)22/7(‌15سابقه‌مصرف‌‌PPI)مهارکننده‌های‌پمپ‌پروتون(

*‌0/052)13/6(‌9)27/3(‌18سابقه‌مصرف‌آنتی‌بیوتیک‌در‌شش‌ماه‌گذشته

+ :Fishers’ Exact tes؛ *: Chi-Square :** Independent Samples Test؛ سطح معنی داری: 0/05

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

60

مهر و آبان 1398. دوره 22. شماره 4

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

شده است، در حالی که تماس با کودکان کوچک تر از دو سال،
مصرف مهارکننده های پمپ پروتون یا مصرف داروهای ضدالتهابی

غیراستروئیدی شانس ابتال را افزایش نداده است [19].

بر اساس مطالعه دیال و همکاران اسهال ناشی از کلستریدیوم
در ۶.8 درصد بیماران بستری گزارش شد و بر اساس رگرسیون
چندمتغیره، مصرف مهارکننده های پمپ پروتون ، نارسایی کلیوی،
جنس زن و سابقه بستری در بیمارستان در سه ماه گذشته از

عوامل بیماری بودند [20].

بر اساس یافته های مطالعه مک دونالد و همکاران در 2005،
و است شده بیان درصد هفت دیفیسیل کلستریدیوم شیوع
شناخته شده خطر عامل عمده ترین آنتی بیوتیک از استفاده
برای اسهال مرتبط با کلستریدیوم دیفیسیل در نظر گرفته شد.
سایر عوامل خطر مطرح شده شامل بستری شدن در بیمارستان،
سن باال و بیماری شدید بود. عوامل خطر احتمالی مطرح شده
در مطالعه شامل مهارکردن اسید معده، تغذیه روده ای، جراحی
گوارشی، شیمی درمانی و پیوند سلول های بنیادی بود [5]. در
مطالعه مک دونالد و همکاران آمده است که دو نقش عمده برای
آنتی بیوتیک ها در پاتوژنز کلستریدیوم د یفیسیل مطرح است اول
اینکه آنتی بیوتیک ها فلور نرمال روده را از بین می برند و شرایط

را برای مخفی شدن کلستریدیوم دیفیسیل و ایجاد توکسین های آن
فراهم می کنند، ثانیًا به نظر می رسد افزایش مقاومت آنتی بیوتیکی
کلستریدیوم دیفیسیل به انواع آنتی بیوتیک ها کلیندامایسین و
فلوروکینولون ها نقش مهمی در افزایش ویروالنس آن داشته باشد
[5]. آنتی بیوتیک هایی که نقش عمده ای در مستعد کردن میزبان به
اسهال مرتبط به کلستریدیوم دیفیسیل دارند، شامل فلوروکینولون ها،
کلیندامایسین، طیف وسیعی از پنی سیلین ها و سفالوسپورین ها
هستند. هر آنتی بیوتیکی حتی مترونیدازول و وانکومایسین که در
درمان کلستریدیوم دیفیسیل نقش دارند می توانند کولیت وابسته به
آنتی بیوتیک ایجاد کنند. استفاده گسترده از آنتی بیوتیک ها، استفاده
از چندین آنتی بیوتیک و افزایش دوره درمان با آنتی بیوتیک، از
عواملی هستند که شیوع اسهال ناشی از کلستریدیوم دیفیسیل را

افزایش می دهند [21 ،5].

طبق نتایج مطالعه فریمن و همکاران در بررسی اپیدمیولوژیک
عفونت کلستریدیوم به این نتیجه رسیدند که استفاده روزافزون از
تکنیک تشخیصی ریبوتایپ PCR منجر به افزایش آمار تشخیصی
شده و هم زمان تغییرات شگرفی در درمان ، عوارض و پیامدهای

بیماری شده است [22].

در این مطالعه گروه سنی سالمندان بررسی شده بود. بر اساس

جدول 3. توزیع تظاهرات بالینی و موارد کلستریدیوم دیفیسیل در روز اول و هشتم مطالعه در دو گروه

متغیرتظاهرات بالینی
تعداد )درصد(

P ماست
ماست معمولیپروبیوتیک

توزیع‌تظاهرات‌بالینی‌در‌زمان‌ورود‌بیمار‌به‌
بیمارستان‌)شروع‌مطالعه(‌در‌دو‌گروه

*0/095)1/5(‌1)7/6(‌5درد‌شکم

*0/039)22/7(‌15)39/4(‌26تب

*0/159)19/7(‌13)30/3(‌20بی‌اشتهایی

*‌0/046)12/1(‌8)25/8(‌17یبوست

-)0(‌0)0(‌0اسهال
*0/770)10/6(‌7)9/1(‌‌6تهوع

*0/310)4/5(‌3)1/5(‌1استفراغ

*‌0/171)1/5(‌1)6/1(‌4مثبت‌بودن‌کلستریدیوم‌دیفیسیل‌در‌شروع‌مطالعه

توزیع‌تظاهرات‌بالینی‌در‌روز‌هشتم‌بستری‌در‌دو‌
گروه

-)0(‌0)0(‌0درد‌شکم
-)0(‌0)0(‌0تب

*‌0/545)10/6(‌7)7/6(‌5بی‌اشتهایی

*‌1)16/7(‌11)16/7(‌11یبوست

-)0(‌0)0(‌0اسهال
-)0(‌0)0(‌0تهوع
-)0(‌0)0(‌0استفراغ

-)0(‌0)0(‌0مثبت‌بودن‌کلستریدیوم‌دیفیسیل‌در‌روز‌هشتم

* Chi-Square ؛ سطح معنی داری: 0/05

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

61

مهر و آبان 1398. دوره 22. شماره 4

مطالعه ریگ و همکاران در سال 2007 ، سن باال با افزایش شدت
اسهال مرتبط با کلستریدیوم دیفیسیل رابطه داشت [23]. علت
این ارتباط نامشخص و شاید چندعاملی باشد. عوامل مربوط به
میزبان شامل پاسخ ایمنی به عفونت کلستریدیوم دیفیسیل و
بیماری های همراه سالمندی از جمله این علل است [23 ،17 ،8].

و سفتریاکسون آنتی بیوتیک بیماران همه ما مطالعه در
بررسی امکان بنابراین و می کردند مصرف آزیترومایسین

بر نبود. دفیسیل کلستریدیوم بروز بر آنتی بیوتیک نوع نقش
اساس مطالعه آرونسون و همکاران، سفالوسپورین ها قوی ترین
معرفی دیفیسیل کلستریدیوم با همراه آنتی میکروبیال عامل
شده اند [24]. نتایج مطالعه هاول و همکاران نشان داد مصرف
مهارکننده های پمپ پروتون خطر ابتال به عفونت کلستریدیوم
دیفیسیل را 0/9 درصد تا 1/4 درصد افزایش می دهد. در حالی
که مصرف آنتی اسید این خطر را به میزان 0/۶ درصد باال می برد

جدول 4. ارتباط تظاهرات بالینی و سوابق پزشکی با مثبت بودن کلستریدیم دیفیسیل در ابتدای مطالعه

تظاهرات بالینی و سوابق پزشکی
تعداد )درصد(

P
کلستریدیوم منفی در شروع مطالعهکلستریدیوم مثبت در شروع مطالعه

‌*‌0/002)28/3(‌36)100(‌5تب
*1)4/7(‌6)0(‌0درد‌شکم

**‌0/437)9/4(‌‌12)20(‌1حالت‌تهوع

*‌1)3/1(‌4)0(‌0استفراغ

-)0(‌0)0(‌0اسهال
*‌1)18/9(‌24)20(‌1یبوست

*‌0/099)23/6(‌30)60(‌3بی‌اشتهایی

PPI‌0/002)13/4(‌17)80(‌4سابقه‌مصرف‌*

*‌0/001)3/1(‌4)60(‌3سابقه‌مصرف‌کورتون

*‌0/006)18/1(‌23)80(‌4سابقه‌مصرف‌آنتی‌بیوتیک‌در‌شش‌ماه‌گذشته

*‌0/004)6/3(‌‌8)60(‌3سابقه‌تماس‌با‌کودک‌در‌منزل

*‌0/619)27/6(‌35)40(‌2دیابت

*‌0/001)3/9(‌5)60(‌3سابقه‌بستری‌در‌بیمارستان‌در‌سه‌ماه‌تا‌یک‌سال‌گذشته

*‌0/111)11(‌14)40(‌2سابقه‌بیماری‌ایسکمیک‌قلبی

*‌0/001)0/8(‌1)20(‌1سابقه‌حوادث‌مغزی

*:Fishers’ Exact test؛**: Chi-Square؛ سطح معنی داری: 0/05

جدول 5. نسبت شانس ابتال به کلستریدیوم دیفیسیل

B1SE2Wald3Df4Sig.5Exp (B)=odd ratio6متغیر

0/6660/027603/2310/0001/69حوادث‌مغزی‌عروقی
0/3480/012450/5510/0001/45سابقه‌بستری‌در‌بیمارستان
0/6780/034701/3410/0001/89تماس‌با‌کودکان‌در‌منزل

0/3480/041347/9810/0001/97مصرف‌آنتی‌بیوتیک‌در‌شش‌ماه‌گذشته
0/4580/033569/0310/0001/53مصرف‌داروهای‌استروئیدی

0/3880/041459/9010/0001/39مصرف‌داروهای‌مهارکننده‌پمپ‌پروتون
0/4580/034346/7810/0001/78تب

1. ضریب ثابت رگرسیون؛ 2. خطای معیار؛ 3. آماره والد؛ 4. درجه آزادی؛ 5. مقدارP value ؛6. نسبت شانس

معصومه صوفیان و همکاران.تأثیر ماست پروبیوتیک بر فراوانی کلستریدیوم دیفیسیل در بیماران سالمند بستری در بیمارستان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

62

مهر و آبان 1398. دوره 22. شماره 4

[25]. در مطالعه حاضر هم، مصرف مهارکنند ه های پمپ پروتون
از ریسک عوامل بیماری بود، ولی مصرف منیزیوم هیدروکساید،
شانس ابتال را افزایش نداد. ساندرا و همکاران در مطالعه خود
به این نتیجه رسیدند اسیدیته معده مهم ترین سازوکار دفاعی
مصرف و است بلع شده پاتوژن های برابرکلونیزاسیون در بدن
ساپرس کننده های اسید معده نظیر مهار کننده های پمپ پروتون
با افزایش PH معده و تأثیر بر عملکرد لوکوسیت ها هم زمان با
افزایش شانس ابتال به عفونت های تنفسی، باعث افزایش شانس
عفونت های روده ای نیز می شوند [20]. کانینگهام و همکاران نیز،
استفاده وسیع از آنتی بیوتیک ها و مصرف بیش از هشت هفته ای
مهارکننده های پمپ پروتون را عامل خطر کلستریدیوم دیفیسیل

بیان کرده اند [2۶].

در مطالعه ما هیچ یک از بیماران کولونوسکوپی نشده بودند و
آندوسکوپی نیز ریسک عامل بیماری نبود. در مطالعه رودمان و
همکاران شیوع اسهال ناشی از کلستریدیوم در بیماران مبتال به
بیماری های التهابی روده بیش از دو برابر افراد سالم بود و به همین
نسبت، شانس بستری در بیمارستان و انجام مداخالت تشخیصی
و درمانی، نظیر آندوسکوپی و کولونوسکوپی بیشتر بود [27]. در
مطالعه لویس و همکاران با عنوان تأثیر پری بیوتیک اولیگوفروکتوز
بر عود اسهال ناشی از کلستریدیوم،کاهش چشمگیر عود اسهال و
مدت بستری کمتر در بیمارستان گزارش شده است [28]. عوامل
خطری نظیر سن بیشتر از ۶5 سال، بیماری وخیم زمینه ای نظیر
حوادث قلبی عروقی یا مزمن تنفسی، اقدامات تهاجمی دستگاه
گوارش نظیر آندوسکوپی و لوله معده ای، مسهل ها و دارودرمانی
زخم معده، سابقه بستری در بیمارستان و یا بخش مراقبت های
دستگاه دفاعی مخاط به تهاجم با عمومًا باریم تنقیه و ویژه
به ابتال افزایش خطر به فلور طبیعی آن تغییر در گوارشی و
عفونت کلستریدیوم منجر شده و شانس مواجهه با عامل بیماری

را افزایش می دهد [4].

در مطالعه ما نسبت کاهش بروز کلستریدیوم دیفیسیل مثبت
در روز هشتم مطالعه به نسبت زمان ورود به مطالعه وجود داشت،
ولی از نظر آماری این کاهش بین دو گروه معنی دار نبود. همچنین
مصرف عوارض و عالئم بر بود نتوانسته پروبیوتیک ماست
آنتی بیوتیک نسبت به گروه شاهد تغییری ایجاد کند. طبق نتایج
مطالعه زرین فر و همکاران نیز استفاده از پروبیوتیک الکتوباسیلوس
تأثیر معنی داری بر کاهش اسهال ناشی از آنتی بیوتیک ها نداشت،
اگرچه عالئم را تخفیف داده بود. کارآزمایی های بالینی محدودی
در خصوص بررسی تأثیر پروبیوتیک ها بر کلستریدیوم دیفیسیل
وجود دارد و این موضوع همچنان نیاز به تحقیق گسترده دارد
[13]. در مطالعه سانگ و همکاران که از پروبیوتیک باسیلوس
کواگوالنس در بیماران تحت درمان با آنتی بیوتیک استفاده کردند،
نتایجی مشابه مطالعه حاضر به دست آمد. در مطالعه کارآزمایی
بالینی که توماس و همکاران انجام دادند، از الکتوباسیلوس کازئی،
الکتو باسیلوس بولگاریکوس و ساکارومایسیز ترموفیلوس استفاده

شده بود که کولیت و اسهال ناشی از آنتی بیوتیک ها کاهش یافت
[29، 30]. بنابراین به نظر می رسد نوع پروبیوتیک به کار رفته در

این موضوع تأثیر داشته باشد.

نتیجه گیری

در پروبیوتیک ماست از استفاده مطالعه حاضر نتایج طبق
شیوع کاهش در معنی داری تأثیر معمولی ماست با مقایسه
کلستریدیوم دیفیسیل و عالئم کولیت بیماران ندارد. مطالعات
این بیشتر در بررسی برای بیشتر نمونه با حجم چند مرکزی

خصوص الزم است.

خصوص در متناقض نتایج با اندکی مطالعات درمجموع
همین که دارد وجود دیفیسیل کلستریدیوم و پروبیوتیک ها
موضوع منجر به دشواری مقایسه شد. از محدودیت های مطالعه
حاضر، تعداد کم بیماران کلستریدیوم دیفسیل مثبت در طول
مطالعه بود که امکان بررسی اثر پروبیوتیک را محدود می کرد.
بنابراین مطالعات با حجم نمونه بیشتر توصیه می شود. همچنین
تهیه ماست پروبیوتیک با توجه به امکانات و شرایط موجود در
بیمارستان مقدور نبود که پس از بررسی بین ماست های تولیدی از
ماست پروبیوتیک کم چرب کاله با حفظ زنجیره سرما استفاده شد.
مدت نسبتًا کوتاه بستری بیماران در بیمارستان ، به نوعی بررسی
اثر دراز مدت ماست پروبیوتیک و اثر آنتی بیوتیک بر کلستریدیوم
دیفیسیل را محدود می کرد. وجود تنها یک بیمارستان مرجع
در شهر اراک امکان بررسی و نمونه گیری چندمرکزی را محدود
می کرد. از این رو پیشنهاد می شود مطالعات وسیع تر با حجم
نمونه بیشتر و مدت زمان پی گیری طوالنی تر در زمینه تأثیرات
پروبیوتیک ها بر پیشگیری و کنترل عفونت های گوارشی ناشی از

کلستریدیوم دیفیسیل انجام شود.

مالحظات اخالقی

پیروی از اصول اخالق پژوهش

اخالق کمیته در اخالق 93-1۶5-10 کد با پژوهش این
IRCTID کد با و تأیید اراک پزشکی علوم دانشگاه پژوهش
IRCT2016092229915N1 در مرکز ثبت مطالعات کارآزمایی

بالینی ثبت شده است.

حامی مالی

ایــن مقالــه برگرفتــه از پایان نامــه دکتــرای تخصصــی
ــال، ــه اقب ــوم پزشــکی اراک، اله ــی دســتیار دانشــگاه عل عفون
ــگاه ــاوری دانش ــات و فن ــت تحقیق ــن معاون ــت. همچنی اس
علــوم پزشــکی اراک، تأمیــن مالــی ایــن پژوهــش را بــر عهــده

ــتند.. داش

مشارکت نویسندگان

http://jams.arakmu.ac.ir/index.php?slc_lang=fa&sid=1

63

October & December 2019. Vol 22. Issue 4

مفهوم سازی: معصومه صوفیان، الهه اقبال، پگاه محقق؛ تحقیق
و بررسی :الهه اقبال، احسان الله غزنوی راد، آمیتیس رمضانی،
معصوم نوشته: نهایی سازی و ویراستاری صوفیان؛ معصومه
صوفیان، پگاه محقق. همچنین نویسندگان معیارهای استاندارد
ناشران بین المللی کمیته پیشنهادهای اساس بر را نویسندگی

مجالت پزشکی )ICMJE( داشتند.

تعارض منافع

بنابر اظهار نویسندگان، این مقاله تعارض منافع ندارد.

تشکر و قدردانی

نویسندگان مقاله بر خود الزم می دانند از کارکنان بخش عفونی
و آزمایشگاه بیمارستان ولی عصر تشکر و قدردانی کنند که در

انجام این پژوهش همکاری کردند.

http://jams.arakmu.ac.ir/index.php?slc_lang=en&sid=1

http://www.icmje.org/recommendations/translations/persian

http://www.icmje.org/recommendations/translations/persian

http://www.icmje.org/recommendations/translations/persian

64

October & December 2019. Vol 22. Issue 4

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