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498. Molecular Epidemiology of C. difficile Within a Community Hospital: A Pilot for a Regional Survey

Abstract Background Clostridium difficile is the leading cause of healthcare-associated infection. As incidence rises, its epidemiology is also evolving. 20–50% of cases are now community-acquired; C. difficile cases arise from more diverse sources than previously thought. In this study, we investigated the diversity of C. difficile within a community hospital. Methods Stool samples were collected from symptomatic adults with a positive C. difficile PCR admitted to Duke Regional Hospital from July 2016 to July 2017. Healthcare-associated CDI was defined by any admission to a hospital, nursing or dialysis facility in the preceding 30 days. C. difficile was isolated by ethanol shock followed by plating on CDSA media. DNA was extracted using a chelex-based protocol. PCR ribotyping was conducted using the Bidet primers and agarose gel electrophoresis. A dendrogram was constructed in Bionumerics by the un-weighted pair-group method with the threshold for identical strains set at 95% similarity. Results C. difficile was successfully isolated from 85% of submitted specimens. For this pilot study, PCR ribotyping was performed on a convenience sample of 70 isolates. C. difficile exhibited substantial diversity: 47 distinct ribotypes were observed among 70 isolates (Figure 1). Fourteen clusters involving identical strain types were observed, totaling 35 isolates. Identical strain types suggestive of direct transmission were evenly split between hospital- (18 of 35, 51%) and community-acquired (17 of 35, 49%) cases. The median time between clustered cases was 50 days (range: 7 to 331 days). Thirty-five of 70 (50%) of all isolates exhibited entirely unique strain types. Conclusion C. difficile isolates in our community hospital exhibited tremendous genetic diversity. The high proportion of strains with entirely unique ribotypes suggests diverse sources of acquisition. These results are consistent with a growing body of literature in which 30–50% of C. difficile isolates are genetically distinct, even when direct transmission was suspected. We are currently expanding our survey to include a network of regional hospitals and clinics, with the goal of better characterizing C. difficile’s diverse and still poorly understood sources.Figure 1. Dendrogram of C. difficile PCR ribotypes. Disclosures All authors: No reported disclosures.

Background. Vaccine efficacy trials against C. difficile infection (CDI) require a study population with an observed event rate that supports feasibility and timely study completion. The goal of this study was to develop a predictive algorithm to identify patients at high risk of developing CDI over the next 2-12 months for enrollment in a vaccine clinical trial.
Methods. We conducted a two-stage retrospective study of patients within the US Department of Veterans Affairs Health system between January 1, 2009 and December 31, 2013. Included patients were age 50 years or older, had at least one visit in each of the 2 years (2007 and 2008) prior to the study, and had no CDI in the 12 months prior to and the first month of the study. In stage 1, we used a case-cohort design to identify factors (patient-month level) for predicting the risk of CDI in months 2-12. The stage 1 algorithm was then applied to the VA population and a calculated risk score generated at each patient visit (inpatient or outpatient). Patients were included in the stage 2 cohort at the first time their stage 1 risk score crossed the designated risk threshold. The stage 2 algorithm incorporated additional factors including medications and laboratory variables. Multivariable logistic regression with elastic net regularization was used to select final predictors in both stage 1 and stage 2 algorithms. The final algorithm consisted of sequential application of predictive algorithms in stage 1 and 2. Performance was measured using the positive predictive value (PPV) and the area under the curve (AUC).
Results. Risk factors in the final algorithm included baseline comorbidity score, acute renal failure, sepsis, UTI or pneumonia in the current month, UTI in the previous month, having an albumin or hematocrit test performed, high-risk antibiotics in the previous 3 months, hemodialysis in the last month, race, and the number and duration of acute and long-term care visits in the last month. The final algorithm resulted in an AUC of 0.69 and a PPV of 3.4%, largely enriching for the risk of CDI in months 2-12.
Conclusion. We developed a predictive algorithm to identify a patient population with increased risk of CDI over the next 2-12 months. Predictive models have the potential to improve enrollment and feasibility of clinical trials.
Disclosures Background. Clostridium difficile is the leading cause of healthcare-associated infection. As incidence rises, its epidemiology is also evolving. 20-50% of cases are now community-acquired; C. difficile cases arise from more diverse sources than previously thought. In this study, we investigated the diversity of C. difficile within a community hospital.
Methods. Stool samples were collected from symptomatic adults with a positive C. difficile PCR admitted to Duke Regional Hospital from July 2016 to July 2017. Healthcare-associated CDI was defined by any admission to a hospital, nursing or dialysis facility in the preceding 30 days. C. difficile was isolated by ethanol shock followed by plating on CDSA media. DNA was extracted using a chelex-based protocol. PCR ribotyping was conducted using the Bidet primers and agarose gel electrophoresis. A dendrogram was constructed in Bionumerics by the un-weighted pair-group method with the threshold for identical strains set at 95% similarity.
Results. C. difficile was successfully isolated from 85% of submitted specimens. For this pilot study, PCR ribotyping was performed on a convenience sample of 70 isolates. C. difficile exhibited substantial diversity: 47 distinct ribotypes were observed among 70 isolates (Figure 1). Fourteen clusters involving identical strain types were observed, totaling 35 isolates. Identical strain types suggestive of direct transmission were evenly split between hospital-(18 of 35, 51%) and community-acquired (17 of 35, 49%) cases. The median time between clustered cases was 50 days (range: 7 to 331 days). Thirty-five of 70 (50%) of all isolates exhibited entirely unique strain types.
Conclusion. C. difficile isolates in our community hospital exhibited tremendous genetic diversity. The high proportion of strains with entirely unique ribotypes suggests diverse sources of acquisition. These results are consistent with a growing body of literature in which 30-50% of C. difficile isolates are genetically distinct, even when direct transmission was suspected. We are currently expanding our survey to include a network of regional hospitals and clinics, with the goal of better characterizing C. difficile' s diverse and still poorly understood sources.

Determining Risk of Clostridium difficile Using Electronic Health Record (EHR) Data
Marci Drees, MD, MS 1,2 ; Edward F. Ewen, MD 2 ; Michael Winiarz, BS, MS 2 and Stephen Eppes, MD 1,2 ; 1 Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, 2 Christiana Care Health System, Newark, Delaware Session: 59. Healthcare Epidemiology: Updates in C. difficile Thursday, October 4, 2018: 12:30 PM Background. Hospitals may now be penalized for Clostridium difficile infection diagnosed after hospital day 3, which are classified as "hospital-onset" (HO) regardless of existence of true disease. Highly sensitive PCR-based testing has made this additionally problematic. As part of a C. difficile testing stewardship initiative, we sought to validate a C. difficile risk scoring tool using existing EHR data.
Methods. We conducted this study in a 2-hospital, >1,100-bed community-based academic healthcare system in northern Delaware. After piloting a paper-based Cdiff risk scoring tool, intended for use after hospital day 3 to discourage testing in low-risk patients, we created a C. difficile-specific analytic application using the Health Catalyst clinical analytics platform over the existing data warehouse (Cerner). The scoring tool was modified from those in the literature and included patient age, body mass index and albumin (if available); prior hospitalization or long-term care facility stay (within 90 days); and receipt of any fluoroquinolone, cephalosporin or piperacillin/tazobactam (within 30 days). Only antibiotics received within our system were included. Using data from September 2015-April 2018, we calculated a receiver operating characteristic (ROC) curve for the risk score's ability to predict a positive HO Cdiff PCR. To increase specificity, we defined "true positive" C. difficile as +PCR tests occurring in patients with ≥3 diarrheal episodes and no laxative use during the 48h prior to testing, and either WBC >12 or temperature >38C 24 hours before or after the +PCR.
Results. During the study period the health system had 150,554 inpatient encounters, of which 411 had positive PCR tests for HO C. difficile and 138 (33% of all PCR+) met our definition of "true positive. " The C. difficile risk stratification tool demonstrated an area under the ROC (AUC) of 0.77 (95% CI 0.75-0.79) to predict a +PCR test (Figure 1), with very similar results (AUC 0.76, 95% CI 0.73-0.80) if the outcome was "true positive" C. difficile (Figure 2).
Conclusion. Using readily available EHR data, we developed a C. difficile risk stratification tool that was able to predict C. difficile positivity with reasonable distinction, but did not differentiate colonization from true illness. The next step is to further refine the tool to better predict true C. difficile illness.