Author Archives: cdifffoundation

Research Provides New Data; C. difficile Changes In Testing and Management







Changes in Testing

For example, new data published in The New England Journal of Medicine underscore the shortcomings of advances in testing technology, suggested Sahil Khanna, MBBS, an associate professor of medicine at Mayo Clinic College of Medicine and Science, in Rochester, Minn. (2020;382[14]:1320-1330).

At first glance, the study, which used data from 10 sites nationwide to derive a national estimate of the incidence of CDI, reported a relatively unchanged rate of the disease over a six-year period: 476,400 cases in 2011 and 462,100 cases in 2017. However, Dr. Khanna noted that after adjusting for the increasing use of nucleic acid amplification testing (NAAT), the researchers concluded that the incidence of CDI had actually decreased by 24% during the study period, including a 36% drop in healthcare-acquired CDI cases.

The study highlights a problem with NAAT, according to Khanna.

“NAAT is approximately 95% sensitive in detecting the C. difficile gene, but it cannot determine if the gene is active and toxin-producing, so it has the potential for overdiagnosis and for producing clinical false positives,” he explained. “Because of this, it’s important that we interpret NAAT results in the context of patient symptoms.”

Clinicians must be selective when deciding which patients should be tested, he said, only using it in patients who have acute diarrhea with no obvious alternative explanation, and who have the risk factors for CDI. These include older age, longer hospitalization, immunosuppression, use of antibiotics, gastric acid-suppressing agents, gastrointestinal surgery, manipulation of the gastrointestinal tract, and tube feeding.

“Patients not experiencing an active infection can be colonized with C. difficile, in which case there is a risk of a clinical false positives and unnecessary treatment,” Khanna emphasized.

An alternative testing approach now recommended by the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) is the use of a multistep algorithm including glutamate dehydrogenase (GDH) to identify pathogenic bacteria and enzyme immunoassay (EIA) to detect C. difficile toxin (Clin Infect Dis 2018;66[7]:e1-e48). NAAT should be reserved for instances in which results from GDH and EIA are inconclusive, the guidelines recommend.

“Unfortunately, NAAT remains the most commonly used test method,” Khanna said, adding that laboratories are increasingly adopting a two-step protocol of GDH and EIA.

Recurrent CDI mostly occurs in people:65 and older who take antibiotics and receive medical care
staying in hospitals and nursing homes for a long time with weakened immune systems

Treatment Changes

The treatment landscape for CDI also has changed over the past few years, noted Kim Ly, PharmD, a clinical pharmacy specialist in critical care and infectious diseases at Sunrise Hospital and Medical Center, in Las Vegas. Bezlotoxumab (Zinplava, Merck), a monoclonal antibody, is now approved for combination treatment of toxin B–producing CDI, along with an established antibiotic. Additionally, metronidazole, while still approved for the treatment of CDI, is no longer recommended by IDSA/SHEA as a first-line agent for primary CDI in adults.

“For severe initial episodes of CDI, oral vancomycin and fidaxomicin [Dificid, Merck] are now the preferred agents, and metronidazole is only recommended for nonsevere initial episodes when patients are unable to be treated with oral vancomycin or fidaxomicin,” Kim explained.

For a first recurrence of CDI, the IDSA/SHEA guidelines recommend administering oral vancomycin as a tapered and pulsed regimen or fidaxomicin, rather than a standard 10-day course of vancomycin. For subsequent recurrences, clinicians can use the same regimen, with the addition of a standard course of oral vancomycin followed by rifaximin or fecal microbiota transplantation (FMT).

Metronidazole comes into play again in the management of fulminant CDI, Ly noted.

“The IDSA/SHEA guidelines recommend treating this with oral or rectal vancomycin 500 mg four times daily along with intravenous metronidazole,” she explained.

Microbiota Disruption

Given that antibiotic-induced microbiota disruption “is far and away the number one precipitant for getting recurrent CDI,” selecting the CDI treatment with the least impact on the microbiota is important, said former IDSA president Cynthia Sears, MD, a professor in the Department of Medicine, Division of Infectious Diseases, at the Johns Hopkins University School of Medicine, in Baltimore.

“Vancomycin is the most commonly used therapy for CDI and its recurrences, but it decreases intestinal diversity and so impedes the recovery of the normal microbiota after CDI, setting the stage for CDI recurrence,” Sears said. “We have learned that vancomycin hits the colon with full force when taken orally because it is not absorbed, and it has off-target effects on lots of anaerobic bacteria that are essential to intestinal resistance of CDI.”

Fidaxomicin has less of an effect on the microbiota and has been shown to sometimes decrease the risk for CDI recurrence when compared with vancomycin (N Engl J Med 2011;364[5]:422-431), but it can be expensive, she said.

Fecal Microbiota Transplantation

FMT is a less expensive, highly effective treatment that has received increasingly widespread attention, specifically for the management of recurrent CDI. Despite the enthusiasm surrounding the treatment, Sears expressed significant reservations about employing it.

“While there’s no question that FMT benefits patients with recurrent CDI, I feel we don’t yet have a quality-controlled product that we know is safe as well as being effective,” she said.

Sears pointed to two recent FDA safety alerts that warned of the harm that FMT can cause. The first, from 2019, reported that stool from a single donor had not been thoroughly screened before FMT and contained extended-spectrum ss-lactamase–producing Escherichia coli. The specimen had been used in separate FMTs for two immunocompromised patients, leading to infection with the pathogen and death in one case.

In another FDA safety alert from earlier this year, the organization said a stool bank specimen that had undergone comprehensive screening nevertheless contained enteropathogenic E. coli and Shiga toxin-producing E. coli. Transfer of the stool for the treatment of recurrent CDI resulted in one nonfatal infection and one death.

“Stool banks try very hard to be sure their specimens are free of disease-causing microbes, but if you have very low-level colonization, molecular diagnostics can miss this,” Sears said. More recently, she noted, the FDA has also raised concerns about the possibility of transferring SARS-CoV-2 through FMT, given that the virus can be present in the stool of infected individuals.

What would a safer and equally effective microbiota-based treatment look like? According to Sears, while microbial diversity seems to be protective against recurrent CDI, there are suggestions that the administration of specific strains may be able to treat CDI and can be produced under the same strict quality control manufacturing processes as other FDA-approved drugs. One study published in 2015 using human and mouse samples found that colonization with Clostridium scindens, a strain of Firmicutes, increased resistance to CDI (Nature 2015;517[7533]:205-208). Many microbiota-based therapeutics are in the research pipeline as well.

“I am optimistic that we will see something emerge that’s safer and still as effective as FMT for patients, whether it’s an orally or rectally administered product,” Sears said.



Publication: Multicenter Prevalence Study Comparing Molecular and Toxin Assays for Clostridioides difficile Surveillance, Switzerland






Andreas F. Widmer, Reno Frei, Ed J. Kuijper, Mark H. Wilcox, Ruth Schindler, Violeta Spaniol, Daniel Goldenberger, Adrian Egli, Sarah Tschudin-Sutter , and Kuijper
Author affiliations: University Hospital Basel, Basel, Switzerland (A.F. Widmer, R. Frei, R. Schindler, V. Spaniol, D. Goldenberger, A. Egli, S. Tschudin-Sutter)Leiden University Medical Center, Leiden, the Netherlands (E.J. Kuijper)Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, and Leeds Teaching Hospitals, Leeds, UK (M.H. Wilcox)


Public health authorities in the United States and Europe recommend surveillance for Clostridioides difficile infections among hospitalized patients, but differing diagnostic algorithms can hamper comparisons between institutions and countries. We compared surveillance based on the detection of C. difficile by PCR or enzyme immunoassay (EIA) in a nationwide C. difficile prevalence study in Switzerland. We included all routinely collected stool samples from hospitalized patients with diarrhea in 76 hospitals in Switzerland on 2 days, 1 in winter and 1 in summer, in 2015. EIA C. difficile detection rates were 6.4 cases/10,000 patient bed-days in winter and 5.7 cases/10,000 patient bed-days in summer. PCR detection rates were 11.4 cases/10,000 patient bed-days in winter and 7.1 cases/10,000 patient bed-days in summer. We found PCR used alone increased reported C. difficile prevalence rates by <80% compared with a 2-stage EIA-based algorithm.


Since its identification as a cause of antibiotic-associated pseudomembraneous colitis in 1978 (1), Clostridioides difficile has emerged as a major healthcare-associated pathogen worldwide. In the United States, C. difficile infection (CDI) rates doubled during 1996–2003 (2), and rates of CDI were reported to be 76.9 cases/10,000 discharges in 2005 (3). In a more recent national point-prevalence study including US healthcare facility in-patients, 13/1,000 patients were found to be either infected or colonized (4), a higher rate than had been previously estimated. In a national point-prevalence study of nosocomial infections in the United States, C. difficile was the most common causative pathogen overall (5). The increase largely has been attributed to the emergence of the hypervirulent strain, PCR ribotype 027 (RT027), which was identified as causative strain in 82% of cases during CDI outbreaks in Quebec, Canada, during 2001–2003 and accounted for 31% of all cases of healthcare-associated infections in the United States in 2011 (69). In Europe, CDI incidence varies across hospitals and countries with a weighted mean of 4.1 cases/10,000 patient-days per hospital in 2008 (10). The most recent study on CDI prevalence in Europe suggests an increase in the number of cases, reporting a mean of 7.0 cases/10,000 patient-bed days and ranging among countries from 0.7 to 28.7 cases/10,000 patient-bed days (11). The most common ribotype identified was RT027, which was detected in 4 countries: Germany, Hungary, Poland, and Romania (11).

To estimate and compare the burden of CDI across the United States, the US Centers for Disease Control and Prevention (CDC) began population-based CDI surveillance in 10 locations in 2011 (12). The European Centre for Disease Prevention and Control (ECDC) began coordinating CDI surveillance in acute care hospitals in Europe in 2016 (13). Both authorities provide case definitions based on different diagnostic approaches, including detection of C. difficile toxin A and B by enzyme immunoassay (EIA) or detection of toxin-producing C. difficile organisms by PCR. However, the use of different diagnostic algorithms to detect C. difficile might hamper comparisons between institutions and countries. Therefore, in a nationwide C. difficile multicenter prevalence study in Switzerland, we systematically compared surveillance measures based on detection of C. difficile in stool by either PCR as a stand-alone test or by a 2-stage algorithm consisting of an EIA to detect glutamate dehydrogenase (GDH) and toxins A and B.


Study Design

We performed a nationwide multicenter prevalence study of toxigenic C. difficile detected in stool samples routinely collected from hospitalized patients with diarrhea. Our study followed the design of a previous point-prevalence study for maximal comparability between our results and data from Europe (11). University Hospital Basel, a tertiary care center in Switzerland, coordinated the study. All hospitals participating in Swissnoso (https://www.swissnoso.chExternal Link), a national infection prevention network, were asked to participate. The Swissnoso network consists of 85 acute care hospitals that account for a total of 26,341 beds.

The Ethics Committee Northwest and Central Switzerland (Ethikkommission Nordwest-und Zentralschweiz) issued a declaration of no objection for this study. We adhered to STROBE guidelines for reporting on observational studies (14).

Sample Collection

All stool samples collected from inpatients >1 year of age with diarrhea that were submitted to the microbiology laboratory on 2 specified sampling days, 1 in winter and 1 in summer, in 2015 were eligble for inclusion. Only 1 sample per patient was included. In addition, stool samples that tested positive for toxigenic C. difficile <1 week prior to each study day also were collected from all institutions to obtain a more detailed estimate of ribotype distribution in Switzerland.

We collected the following institutional data for all hospitals and their affiliated microbiology laboratories: contact information; detailed information regarding laboratory diagnostics in place; and detailed information on the total number of admissions, number of beds, and number of patients hospitalized on the 2 days of the study. We also collected information on the total number of diagnosed CDI cases at each institution during the study year. For each eligible stool sample, we collected the following data: date of sample collection, age and gender of patient, ward location and clinical specialty, institution, whether a C. difficile test was ordered by the treating physician, and result of the C. difficile test if testing was performed at the local laboratory.


We tested all stool samples at the Division of Clinical Microbiology of the University Hospital Basel by using a 2-stage algorithm consisting of EIA and PCR. We performed EIA to detect GDH and toxins A and B by using C. DIFF QUIK CHEK COMPLETE (Techlab, https://www.techlab.comExternal Link), following the manufacturer’s instructions. We then performed PCR to detect the toxin B gene by using the RealStar PCR Kit (Altona Diagnostics, https://www.altona-diagnostics.comExternal Link). For detected C. difficile, we performed strain typing by using high-resolution capillary gel-based PCR ribotyping according to the method previously described by Stubbs et al. (15).


We calculated reported and measured rates of detected toxigenic C. difficile per 10,000 patient bed-days across participating institutions. We compared differences in testing algorithms for detection of toxigenic C. difficile across institutions in Switzerland and performance characteristics of diagnostic algorithms. We considered the proportion of missed toxigenic C. difficile cases and ribotype distributions as additional outcomes. We further assessed the proportion of laboratories using optimized C. difficile diagnostic tests, which we defined as using an algorithm recommended by the European Society of Clinical Microbiology and Infectious Diseases (16).

Statistical Analyses

We separately calculated rates for each diagnostic algorithm performed in the coordinating center laboratory. In addition, we separately calculated rates for dedicated children’s hospitals. We defined missed C. difficile cases as those in which tests were negative at the participating hospital’s laboratory but positive at our institution. We used descriptive statistics to report ribotypes and differences in diagnostic algorithms across all participating institutions. All analyses were performed in Stata version 15.1 (StataCorp, https://www.stata.comExternal Link).


Figure 1. Distribution of centers participating in a prevalence study comparing molecular and toxin assays for nationwide surveillance of Clostridioides difficile, Switzerland. Red circles represent the location of participating centers.

Participating institutions included 76/85 (89.4%) institutions belonging to the Swissnoso network. Among participating institutions, 5 were academic teaching hospitals, 3 were dedicated children’s hospitals, and 36 were affiliated microbiology laboratories. Participating institutions were distributed across all geographic regions of Switzerland (Figure 1).

Participating institutions reported collecting a fecal sample for microbiological workup in »65% (SD +25%) of all patients with hospital-onset diarrhea. Among participating institutions, 15/76 (19.7%) did not begin CDI treatment before fecal sample collection. Among institutions that initiated treatment before collecting fecal samples, 23/76 (30.3%) began treatment in <2% of patients, 12/76 (15.8%) began treatment in 3%–5% of patients, 8/76 (10.5%) began treatment in 6%–10% of patients, and 1 (1.3%) began treatment in 11%–20% of patients. The other 17 (22%) institutions were not able to provide an estimate of these data.

Overall, 354 stool samples were submitted to the coordinating center, of which 338 were eligible for study inclusion; 16 samples were excluded because they were not liquid, their submitted data were incomplete, or they were duplicate samples from 1 patient. Among 338 samples included, 250 were collected as part of the point-prevalence study. We excluded 8 of these because the samples were collected from patients <1 year of age. In all, we included 242 samples in the point-prevalence study.

Diagnostic Algorithms

Figure 2. Testing algorithms at the 36 laboratories participating in a prevalence study comparing molecular and toxin assays for nationwide surveillance of Clostridioides difficile, Switzerland. EIA, enzyme immunoassay; GDH, glutamate dehydrogenase; NAAT, nucleic…

Among the 36 participating laboratories, 1 routinely tested all diarrheal stool samples for toxigenic C. difficile and 35 tested only if a specific test was requested. Optimized diagnostic tests for detection of toxigenic C. difficile were used by 58% (21/36) of laboratories in the winter sampling period and by 61% (22/36) in the summer sampling period. Among laboratories not following the recommendations of the European Society of Clinical Microbiology and Infectious Diseases (16), 9 in the winter sampling period and 10 in the summer sampling period used a nucleic acid amplification test (NAAT) alone, and 5 in the winter sampling period and 3 in the summer sampling period used EIAs for A and B toxins either as a standalone test or as an initial screening test. Only 1 laboratory reported having established PCR ribotyping methodologies (Figure 2).

Point-Prevalence Analyses

We collected demographic characteristics of patients whose stool samples tested positive by our testing algorithms (Table 1). C. difficile tests were required and performed for 68% (165/242) of stool samples; 6% (27/165) were reported as positive by the affiliated microbiology laboratory.

At the coordinating center, we detected C. difficile in 9% (21/242) of samples by EIA for GDH and A and B toxins and in 12% (30/242) of samples by PCR alone. Among all 27 samples reported as positive by the participating centers, we confirmed 18 (67%) by EIA and 24 (89%) by PCR. Among 138 samples reported as negative by the participating centers, 1 (1%) sample tested positive by EIA and 3 (2%) tested positive by PCR at the coordinating center. Among 77 samples not tested for C. difficile at the participating centers, we detected C. difficile in 2 (3%) by EIA and in 3 (4%) by PCR. Among 21 stool samples that tested positive by EIA at the coordinating center, a C. difficile test was not requested in 2 (10%) cases. Among 30 samples that tested positive by PCR at the coordinating center, a C. difficile test was not requested in 3 cases (10%; Table 2).

Measured detection and testing rates of toxigenic C. difficile were higher than the reported rates across all participating institutions (Table 3). Depending on the diagnostic algorithm applied, the largest difference in prevalence across all institutions was measured during the winter sampling period, which had a prevalence of 6.4 cases/10,000 patient bed-days by EIA and 11.4 cases/10,000 patient bed-days by PCR alone. Thus, across all institutions, rates of toxigenic C. difficile detection by PCR alone were <80% higher than detection rates by EIA for GDH and A and B toxins. In addition, detection rates by PCR alone were <100% higher in dedicated children’s hospitals (Table 3).

Ribotype Distribution

Figure 3. Distribution of PCR ribotypes among 107 samples collected in a prevalence study comparing molecular and toxin assays for nationwide surveillance of Clostridioides difficile, Switzerland. *Unknown ribotype.

We cultured and ribotyped 107 toxigenic C. difficile strains, 29 from the 2 point-prevalence days and 78 collected <1 week before each prevalence day. We identified a large diversity of C. difficile ribotypes, 23 (22%) had not been referenced before. The ribotypes most commonly identified included RT014 (12/107; 11%), presumably hypervirulent RT078 (9/107; 8%), RT001 (7/107; 7%), and RT002 (7/107; 7%) (Figure 3).



In this nationwide multicenter study, we found that PCR as a stand-alone test increased reported C. difficile prevalence rates <80% compared with a 2-stage EIA-based algorithm. At first glance, this finding was not surprising given the higher sensitivity of EIA (16). However, the fact that our results and conclusions are based on a nationwide cohort representing all geographic regions of Switzerland adds to the study’s credibility. In addition, our results strengthen the advice of the European Society of Clinical Microbiology and Infectious Diseases study group for C. difficile against use of a single commercial test for diagnosing CDI because of the low positive predictive values when CDI prevalence is low, 46% at a CDI prevalence of 5% (16). However, CDC and ECDC protocols for CDI surveillance define a case of CDI as the combination of diarrheal stool and a positive PCR (12,13). In addition, the clinical practice guidelines for CDI in adults and children published by the Infectious Diseases Society of America and Society for Healthcare Epidemiology of America recommend testing by different approaches, such as multistep algorithms or NAAT, depending on the degree of clinical suspicion (17). Based on a systematic review and meta-analysis, the American Society of Microbiology also recommends different approaches, including NAAT-only testing, and algorithms that include GDH and NAAT or GDH, toxins, and NAAT (18). Although these recommendations stand to reason for detection of CDI in individual patients, our results challenge their utility for meaningful comparisons in surveillance studies and suggest that uniform definitions should be provided.

On both point-prevalence days, we noted a higher nationwide rate of toxigenic C. difficile than previously reported in incidence studies performed at different institutions in Switzerland (1921). Our findings suggest that CDI rates have increased during the last decade in Switzerland, a finding that is in line with reports from other countries in Europe (11). Using the same diagnostic algorithm, diagnostic test, and a similar study design to the multicenter point-prevalence study of CDI in hospitalized patients with diarrhea in Europe, the nationwide mean prevalence rates are comparable in Switzerland (mean 6.1 cases/10,000 patient bed-days) and Europe (7.0 cases/10,000 patient bed-days) (11). Because we only included liquid stools in our study, our mean prevalence rate of 9.3 cases/10,000 patient bed-days measured by PCR fulfills the ECDC case definition and further shows that CDI is increasing substantially nationwide.

We found a lower proportion of missed detection of toxigenic C. difficile in Switzerland (9.5%), driven by the absence of clinical suspicion, compared with Europe (23%), which equates to 1 missed case of C. difficile per day among the included institutions in Switzerland. False-negative testing accounted for 1 additional missed diagnosis during both point-prevalence days, which extrapolates to »550 missed cases of C. difficile per year among hospitals across the nation.

We detected a variety of different RTs during our study period, 21% of which had not been referenced before. Of note, we did not recover hypervirulent RT027, but RT078 was the third most common strain circulating in Switzerland during our study. In contrast, a point-prevalence study in Europe identified RT027 as the most commonly circulating strain during its study period but did not detect RT078. RT078 has been associated with similarly severe disease manifestations as RT027, but RT078 has been reported to affect younger patients and to be linked more commonly with community-associated disease in the Netherlands (22). RT078 has been isolated from piglets with diarrhea, possibly suggesting ongoing transmission by introduction to the food chain because isolates from humans and pigs were found to be highly genetically related (22). A component of RT078 infections also was reported in Northern Ireland, which has a large pig population and »1:1 ratio of cattle to humans (23). In Switzerland, RT078 has been isolated previously from 6 wastewater treatment plants, suggesting its dissemination in the community (24). Except for both hypervirulent RT027 and RT078, we identified other similarities in RT distribution between Switzerland and the rest of Europe; RT014, RT001, RT002, and RT020 were among the 10 most commonly identified ribotypes in both settings.

Our study has some limitations, most of which are intrinsic to point-prevalence studies. First, our study only reflects frequency of toxigenic C. difficile detected on 2 days in 2015; therefore, we cannot draw solid conclusions regarding incidence. We expanded the timeframe for assessing the distribution of ribotypes circulating in Switzerland by an additional week for each prevalence day, but this still represents a limited collection of the true incidence. Second, we cannot rule out introduction of bias to testing policies at the participating hospitals, which might have increased testing on the 2 point-prevalence days. However, we did not provide any promotional information regarding our study, so alterations in daily clinical practice among treating physicians is unlikely on these 2 days. Third, we only included liquid stool samples for analyses, but we did not consider any other preanalytical factors, such as the use of laxatives, for testing eligibility. Finally, we applied surveillance definitions recommended by CDC and ECDC rather than defintions used for the clinical diagnosis of CDI in individual patients, such as detection of C. difficile in the context of symptoms related to CDI. Therefore, we cannot rule out detection of toxigenic C. difficile from colonization rather than infection in some cases.

In conclusion, this nationwide multicenter study reveals that PCR as a stand-alone test results in an increase of C. difficile prevalence rates of <80% compared with a 2-stage EIA-based algorithm. Our findings underscore the need for consistent testing algorithms for meaningful interinstitutional and nationwide comparisons. Our results also challenge the utility of the current CDC and ECDC case definitions and highlight the need for uniform recommendations on diagnostic approaches.


Dr. Widmer is head of the infection control program at University Hospital Basel, University of Basel, Switzerland. His research interests include all aspects of Clostridioides difficile and the epidemiology and prevention of hospital-acquired infections.



We acknowledge and thank the ESCMID (European Society of Clinical Microbiology and Infectious Diseases) Study group for C. difficile (ESGCD) for professional support. We also thank all participating centers and laboratories (Appendix).

Astellas Pharmaceuticals Europe provided financial support for this study. The funder did not influence the study design and did not contribute to data collection, data analysis, data interpretation, or writing of the report. Astellas Pharma Europe reviewed the report for factual accuracy before submission, in line with the terms of the funding agreement. The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication. Alere provided C. DIFF QUIK CHEK COMPLETE test kits for conducting EIAs to detect GDH and toxins A and B.

The authors declare the following possible conflicts of interest: A.W. is a member of the Astellas and Merck Sharp & Dohme Corp. advisory boards for C. difficile and reports grants from the Swiss National Science Foundation. S.T.-S. is a member of the Astellas and Merck Sharp & Dohme Corp. advisory boards for C. difficile and reports grants from the Swiss National Science Foundation (grant nos. NRP72 and 407240_167060), the Gottfried und Julia Bangerter-Rhyner Stiftung, and the Fund for the Promotion of Teaching and Research of the Voluntary Academic Society, Base


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  23. Patterson  LWilcox  MHFawley  WNVerlander  NQGeoghegan  LPatel  BCet al. Morbidity and mortality associated with Clostridium difficile ribotype 078: a case–case study. J Hosp Infect2012;82:1258DOIExternal LinkPubMedExternal Link
  24. Romano  VPasquale  VKrovacek  KMauri  FDemarta  ADumontet  SToxigenic Clostridium difficile PCR ribotypes from wastewater treatment plants in southern Switzerland. Appl Environ Microbiol2012;78:66436DOIExternal LinkPubMedExternal Link

Cite This Article

DOI: 10.3201/eid2610.190804

Original Publication Date: September 09, 2020



U.S. Burden of CDI and Outcomes – Trends in the US – Publication

Trends in U.S. Burden of Clostridioides difficile Infection and Outcomes

List of authors.
Alice Y. Guh, M.D., M.P.H., Yi Mu, Ph.D., Lisa G. Winston, M.D., Helen Johnston, M.P.H., Danyel Olson, M.S., M.P.H., Monica M. Farley, M.D., Lucy E. Wilson, M.D., Stacy M. Holzbauer, D.V.M., M.P.H., Erin C. Phipps, D.V.M., M.P.H., Ghinwa K. Dumyati, M.D., Zintars G. Beldavs, M.S., Marion A. Kainer, M.B., B.S., M.P.H., et al., for the Emerging Infections Program Clostridioides difficile Infection Working Group*



Efforts to prevent Clostridioides difficile infection continue to expand across the health care spectrum in the United States. Whether these efforts are reducing the national burden of C. difficile infection is unclear.


The Emerging Infections Program identified cases of C. difficile infection (stool specimens positive for C. difficile in a person ≥1 year of age with no positive test in the previous 8 weeks) in 10 U.S. sites. We used case and census sampling weights to estimate the national burden of C. difficile infection, first recurrences, hospitalizations, and in-hospital deaths from 2011 through 2017. Healthcare-associated infections were defined as those with onset in a health care facility or associated with recent admission to a health care facility; all others were classified as community-associated infections. For trend analyses, we used weighted random-intercept models with a negative binomial distribution and logistic regression models to adjust for the higher sensitivity of nucleic acid amplification tests (NAATs) as compared with other test types.


The number of cases of C. difficile infection in the 10 U.S. sites was 15,461 in 2011 (10,177 healthcare-associated and 5284 community-associated cases) and 15,512 in 2017 (7973 healthcare-associated and 7539 community-associated cases). The estimated national burden of C. difficile infection was 476,400 cases (95% confidence interval [CI], 419,900 to 532,900) in 2011 and 462,100 cases (95% CI, 428,600 to 495,600) in 2017. With accounting for NAAT use, the adjusted estimate of the total burden of C. difficile infection decreased by 24% (95% CI, 6 to 36) from 2011 through 2017; the adjusted estimate of the national burden of healthcare-associated C. difficile infection decreased by 36% (95% CI, 24 to 54), whereas the adjusted estimate of the national burden of community-associated C. difficile infection was unchanged. The adjusted estimate of the burden of hospitalizations for C. difficile infection decreased by 24% (95% CI, 0 to 48), whereas the adjusted estimates of the burden of first recurrences and in-hospital deaths did not change significantly.


The estimated national burden of C. difficile infection and associated hospitalizations decreased from 2011 through 2017, owing to a decline in healthcare-associated infections. (Funded by the Centers for Disease Control and Prevention.)

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Study Shows the Burden of CDI During the COVID-19 Pandemic: A Retrospective Case-Control Study in Italian Hospitals (CloVid)


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The Burden of Clostridioides Difficile Infection
during the COVID-19 Pandemic: A Retrospective
Case-Control Study in Italian Hospitals (CloVid)

Guido Granata 1,* , Alessandro Bartoloni 2, , Mauro Codeluppi 3, , Ilaria Contadini 4, Francesco Criistini 4, , Massimo Fantoni 5, , Alice Ferraresi 6, , Chiara Fornabaio 6, , Sara Grasselli 3,
Filippo Lagi 2, , Luca Masucci 5,, Massimo Puoti 7, , Alessandro Raimondi 7, , Eleonora Taddei 8
,Filippo Fabio Trapani 9, , Pierluigi Viale 9, , Stuart Johnson 10, Nicola Petrosillo 1, and on behalf of the CloVid Study Group †

1 Clinical and Research Department for Infectious Diseases, Severe and Immunedepression-Associated
Infections Unit, National Institute for Infectious Diseases L. Spallanzani IRCCS, 00149 Rome, Italy;
2 Department of Infectious Diseases, Careggi Hospital, University of Florence, 50121 Florence, Italy; (A.B.); (F.L.)
3Infectious Diseases Unit, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (M.C.); (S.G.)
4Infectious Diseases Unit, Rimini-Forlì-Cesena Hospitals, 48121 Rimini, Italy; (I.C.); (F.C.)
5 Dipartimento di Scienze di Laboratorio e Infettivologiche —Fondazione Policlinico A. Gemelli IRCCS,Via della Pineta Sacchetti, 00168 Rome, Italy; (M.F.); (L.M.)
6Infectious Diseases Unit, ASST Cremona, 26100 Cremona, Italy; (A.F.); (C.F.)
7Infectious Diseases Unit, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (A.R.)
8 Dipartimento di Sicurezza e Bioetica—Sezione di Malattie Infettive—Fondazione Policlinico A.
Gemelli IRCCS, Via della Pineta Sacchetti, 00168 Rome, Italy;
9 Department of Medical and Surgical Sciences, Infectious Diseases Unit,
Alma Mater Studiorum–University of Bologna, 40126 Bologna, Italy; (F.F.T.); (P.V.)
10 Research Service, Hines VA Hospital and Infectious Disease Section, Loyola University Medical Center,
Maywood, IL 60153, USA;
* Correspondence:; Tel.: +39-065-517-0241
† CloVid (Clostridioides difficile infection during the COVID-19) Study Group.
Received: 28 October 2020; Accepted: 25 November 2020; Published: 27 November 2020

Abstract: Data on the burden of Clostridioides difficile infection (CDI) in Coronavirus Disease
2019 (COVID-19) patients are scant. We conducted an observational, retrospective, multicenter,
1:3 case (COVID-19 patients with CDI)-control (COVID-19 patients without CDI) study in Italy
to assess incidence and outcomes, and to identify risk factors for CDI in COVID-19 patients.
From February through July 2020, 8402 COVID-19 patients were admitted to eight Italian hospitals;

38 CDI cases were identified, including 32 hospital-onset-CDI (HO-CDI) and 6 community-onset,
healthcare-associated-CDI (CO-HCA-CDI). HO-CDI incidence was 4.4 × 10,000 patient-days.
The percentage of cases recovering without complications at discharge (i.e., pressure ulcers, chronic heart decompensation) was lower than among controls (p = 0.01); in-hospital stays were longer among cases, 35.0 versus 19.4 days (p = 0.0007). The presence of a previous hospitalization (p = 0.001), previous
steroid administration (p = 0.008) and the administration of antibiotics during the stay (p = 0.004) were risk factors associated with CDI. In conclusions, CDI complicates COVID-19, mainly in patients with J. Clin. Med. 2020, 9, 3855; doi:10.3390/jcm9123855 J. Clin. Med. 2020, 9, 3855 2 of 11 co-morbidities and previous healthcare exposures. Its association with antibiotic usage and hospital-acquired bacterial infections should lead to strengthen antimicrobial stewardship programmes and infection prevention and control activities

1. Introduction
Since 31 December 2019, when the World Health Organization (WHO) was informed of an
outbreak of a respiratory disease affecting the city of Wuhan, the world has been shaken by the
most profound health crisis of the last several decades [1,2]. Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly worldwide with the consequence of causing a serious health threat to humans on every continent.  At present, more than thirty million people are known to have been infected, which has placed a great burden on health care systems and heightened anxiety and psychological stress of medical staff [3].

The lack of high-level evidence, inherent to the novelty and rapid spread of COVID-19, has led to
the adoption of heterogeneous therapeutic management approaches, often without a clear distinction between evidence-based data and expert opinion in informing treatment choices. The high number shortage of beds, especially in critical areas, and the need for healthcare worker protection have challenged compliance with infection control and antibiotic stewardship programs in most health-care facilities facing this emergent threat of COVID-19 [4]. During the pandemic many health-care facilities gave priority to the protection of their healthcare workers from COVID-19, reducing attention to the prevention of other bacterial infections transmitted by interpersonal contact. Moreover, most of the early recommendations for the management of COVID-19 patients considered the use of empirical antibiotic treatment, resulting in large usage of antimicrobials in COVID-19 patients. Up to 94% of COVID-19 patients have been reported to receive empirical antibiotic therapy during their hospital stay [4–9]. Bacterial superinfections have been described in the course of COVID-19 disease
and early reports of Clostridioides difficile infection (CDI) co-infection have been published [10,11]. CDI is commonly associated with the use of broad-spectrum antibiotics, absence of antimicrobial stewardship, inadequate infection control, and hospital overcrowding [12]. Currently, we do not have a clear picture of the burden of CDI in COVID-19 patients and there is a lack of data on the prevalence and clinical manifestations of CDI in COVID-19 patients.
The aim of this study was to assess the incidence of CDI in hospitalized COVID-19 patients,
to describe the clinical characteristics and outcomes of COVID-19 patients with CDI and to identify risk factors for the onset of CDI in COVID-19 patients.

2. Materials and Methods
We conducted an observational, retrospective, national multicenter, case-control study with 1:3
matching to assess the incidence, clinical characteristics, and outcomes of COVID-19 patients with CDI. In addition, we evaluated risk factors associated with the occurrence of CDI in COVID-19 patients. The study was performed in 8 acute-care Italian hospitals admitting COVID-19 patients, between February 2020 and July 2020 (Figure 1 and Table S1). All the hospitals have an Infectious Disease Unit. The study was approved by the Ethics Committees of the participant hospitals.J. Clin. Med. 2020, 9, 3855 3 of 11 J. Clin. Med. 2020, 9, x FOR PEER REVIEW 3 of 11

Figure 1. Geographical distribution of participating centers. The detailed list of the eight participating centers is available as supplementary material (Table S1).
2.1. Study Design Hospitalized adult (>18 years old) patients with COVID-19 and CDI were identified from the databases of the participant centers. Cases were defined as COVID-19 patients with CDI; controls were COVID-19 patients without CDI. Cases were matched 1:3 with controls. Demographic, epidemiological, and clinical data (COVID-19 onset and clinical characteristics, medications given for COVID-19, antimicrobial treatments before and after the diagnosis of COVID-19, laboratory data, CDI onset and characteristic, and patient’s outcome) were collected in clinical record forms (CRF)

(Table S2).
Controls were matched to cases according to the following criteria:
1. Same gender
2. Hospitalization in the same hospital and in the same unit
3. Same date of hospital admission ± 7 days
4. Same age ± 3 years
All cases and controls were followed up to 30 days from their hospital discharge to assess for
new onset of diarrhea, recurrence of CDI, and mortality at 30 days from the hospital discharge.
The definitions of CDI, microbiological evidence of C. difficile, CDI recurrence, mild CDI, severe
CDI and complicated CDI and the definitions of the clinical syndromes associated with COVID-19
adopted in the study are described in Table S3.

2.2. Data Analysis
The incidence of CDI among all COVID-19 patients admitted to the participating hospitals was
calculated using as the numerator the number of CDI cases and as the denominator the number of days of hospitalization of the COVID-19 patients (× 10,000). The characteristics of the study population and Figure 1. Geographical distribution of participating centers. The detailed list of the eight participating centers is available as supplementary material (Table S1).

2.1. Study Design
Hospitalized adult (>18 years old) patients with COVID-19 and CDI were identified from the
databases of the participant centers. Cases were defined as COVID-19 patients with CDI; controls were COVID-19 patients without CDI. Cases were matched 1:3 with controls. Demographic, epidemiological, and clinical data (COVID-19 onset and clinical characteristics, medications given for COVID-19, antimicrobial treatments before and after the diagnosis of COVID-19, laboratory data, CDI onset and characteristic, and patient’s outcome) were collected in clinical record forms (CRF) (Table S2).

Controls were matched to cases according to the following criteria:
1. Same gender
2. Hospitalization in the same hospital and in the same unit
3. Same date of hospital admission ±7 days
4. Same age ±3 years
All cases and controls were followed up to 30 days from their hospital discharge to assess for new onset of diarrhea, recurrence of CDI, and mortality at 30 days from the hospital discharge.
The definitions of CDI, microbiological evidence of C. difficile, CDI recurrence, mild CDI, severe
CDI and complicated CDI and the definitions of the clinical syndromes associated with COVID-19
adopted in the study are described in Table S3.

2.2. Data Analysis
The incidence of CDI among all COVID-19 patients admitted to the participating hospitals was
calculated using as the numerator the number of CDI cases and as the denominator the number of days of hospitalization of the COVID-19 patients (× 10,000). The characteristics of the study population and the patient outcome were evaluated by means of descriptive statistics. The potential correlations J. Clin. Med. 2020, 9, 3855 4 of 11
between CDI and clinical variables of COVID-19 (infection onset, severity) and laboratory findings were analyzed by univariate and multivariate analysis. To identify risk factors for onset of CDI in COVID-19 patients and any determinants of delayed diagnosis of CDI, the characteristics of the CDI group were compared to the control group by means of univariate and multivariate analysis.

2.3. Statistical Analysis
Quantitative variables were tested for normal distribution and compared by means of a paired
t-test. Qualitative differences between groups were assessed by use of Fisher’s exact test. The precision of odd ratio (OR) was determined by calculating a 95% confidence interval (CI). A p value less than 0.05 was considered statistically significant. Variables from the univariate analysis were considered for inclusion in multivariate logistic regression analysis if p-value was less than 0.05. Backward stepwise logistic regression was performed, and the model that was considered biologically plausible and had the lowest −2 log-likelihood ratio was chosen as the final model. Statistical analysis was performed using the software program IBM SPSS version 24.

3. Results
3.1. CDI Incidence among COVID-19 Patients
Overall, during the study period, a total of 40,315 patients were admitted to the eight participant hospitals; of these, 8402 were COVID-19 patients. The mean hospital stay for COVID-19 patients was 13.8 days (range 1–59 days). Thirty-eight CDI cases were identified, including 32 hospital-onset CDI (HO-CDI) and 6 community-onset, healthcare-associated CDI (CO-HCA-CDI) cases. Therefore, during the study period, 32 COVID-19 patients developed HO-CDI, corresponding to an HO-CDI prevalence of 0.38%, and an HO-CDI incidence of 4.4 × 10,000 patient days ranging in the hospitals from 0.7 to 12.3 × 10,000 patient days (Table S4).
3.2. Clinical Features of Clostridioides Difficile Infection in COVID-19 Patients
The demographic and epidemiological data, the comorbidities, the clinical characteristics, and
the outcome of the 38 COVID-19 patients with CDI and of the 114 controls included in the study are described in Table 1. The mean laboratory findings at the admission of the 38 COVID-19 patients with CDI and of the 114 controls are shown in Table 2. The CDI characteristics, severity, management, and follow-up of the 38 COVID-19 patients with CDI included in the study are shown in Table 3.

Table 1. Demographic and epidemiological data, comorbidities, clinical characteristics of the Coronavirus Disease 2019 (COVID-19), and outcome of the 38 COVID-19 patients with CDI and of the 114 COVID-19 controls included in the study. CCI: Charlson Co-morbidity Index. LTHCF: long-term health care facility. ARDS: Acute Respiratory Distress Syndrome. LMWH: Low Molecular Weight



Cleveland Clinic Shares 9 Places Germs Are Lurking In the Kitchen

Did you know that about 9% of foodborne illness outbreaks occur in the home and it’s almost impossible to tell where the bacteria may live with the naked eye?

Research has identified the top kitchen items that are commonly cross-contaminated during the preparation of a meal (yuck!). Infectious disease specialist Susan Rehm, MD, outlines these top kitchen contaminators and how to make sure you don’t get sick.

1. Cloth towel

Like sponges, cloth towels were the most frequently contaminated article in the kitchen. How many times have you used a towel to wipe off the counter after cooking, washed your hands, and then wiped your clean hands with that same towel? It happens more often than you think.

“One of the best ways to prevent cross-contamination in the kitchen is to use paper towels,” says Dr. Rehm.

Research also shows that salmonella grows on cloths stored overnight, even after they were washed and rinsed in the sink. To minimize the risk of contamination, either strictly use paper towels or use a new, clean cloth for each surface in your kitchen. Be sure to wash your towels with bleach or other disinfectants before using them again.

2. Smartphone or tablet

Just like if you take your phone to the bathroom with you, anything you touch in the kitchen following contact with raw meat can become contaminated. That includes your smartphone or tablet you use to follow a recipe or answer a call.

“Either don’t use it or clean it as frequently as you would wash your hands,” she says.

Consider covering your device with clear plastic or printing out the recipe so you don’t have to touch your device. If you don’t want to print it out, make sure to disinfect your phone afterward.

To disinfect your phone, Dr. Rehm recommends following these steps:

  1. Take the case off and turn your phone or tablet off completely.
  2. Mist a gentle cleaning cloth with 70% isopropyl alcohol.
  3. Gently wipe down each corner of your phone or tablet.
  4. Wipe down your case and any phone accessories with the same solution.
  5. Let dry completely before turning your device on.

Never use harsh chemicals on your devices. Double-check with your phone brand on the proper way to disinfect their products so you don’t end up ruining your expensive tech.

3. Sink faucet, refrigerator, oven handle, trash container

When was the last time you disinfected your sink faucet, refrigerator, oven, or trash can?

“During food prep, be aware that there are bacteria in food, and touching it can spread it to other surfaces and potentially cause illness,” says Dr. Rehm. “Common bacteria found in the kitchen include E.coli, salmonella, shigella, campylobacter, norovirus, and hepatitis A.”

E.coli can survive for hours on a surface, salmonella can survive for about four hours and hepatitis A can survive for months. If those numbers make you nervous, lessen your chances of getting those germs by disinfecting each surface that bacteria could have come into contact with. And yes, that means wiping down or spraying each surface in your kitchen that you worked at just to be sure.

4. Cooking utensils

With so many different kitchen utensils, it’s important to be aware of how you use them, too.

“When you use tongs or a fork to put raw poultry on the grill, you should wash it immediately afterward if you plan to use the same tools to serve the meal,” says Dr. Rehm.

Sanitize your utensils by hand-washing in hot, soapy water and sanitizing solution. Make sure to air-dry them completely before putting them away into the cupboard.

5. Hands

Believe it or not, it’s common for people to not wash their hands with the frequency or quality needed to reduce bacterial contamination.

“When preparing food, it’s wise to wash hands beforehand, frequently throughout, and afterward,” says Dr. Rehm.

Each time you handle raw meat, wash your hands. Lather your hands with soap (don’t forget your nails, between your fingers and the back of your hands!) Scrub your hands for at least 20 seconds, and then use a paper towel to dry them and turn off the water faucets and don’t reuse it. Throw the used paper towel away immediately after use.

6. Fruit and vegetables

Bacteria can be found on your favorite fruit and veggies.

If you’re not careful, that bacteria could cause nausea, vomiting, and diarrhea. Cleaning up is less effective than not contaminating it in the first place, so make it a habit to keep surfaces as clean as possible the first time to avoid cross-contamination. ​