499. Determining Risk of Clostridium difficile Using Electronic Health Record (EHR) Data
Session: Poster Abstract Session: Healthcare Epidemiology: Updates in C. difficile
Thursday, October 4, 2018
Room: S Poster Hall
  • Drees_IDWeek2018_Cdiff Scoring Tool_FINAL.pdf (524.6 kB)
  • Background:  Hospitals may now be penalized for Clostridium difficile (Cdiff) 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 Cdiff testing stewardship initiative, we sought to validate a Cdiff risk scoring tool using existing EHR data.

    Methods:  We conducted this study in a 2-hospital, >1100-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 Cdiff-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 90d); and receipt of any fluoroquinolone, cephalosporin or piperacillin/tazobactam (within 30d).  Only antibiotics received within our system were included.  Using data from 9/15-4/18, 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” Cdiff 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 24h 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 Cdiff and 138 (33% of all PCR+) met our definition of “true positive”.  The Cdiff risk stratification tool demonstrated an area under the ROC (AUC) of 0.77 (95% CI, 0.75-0.79) to predict a +PCR test (Fig 1), with very similar results (AUC 0.76, 95% CI 0.73-0.80) if the outcome was “true positive” Cdiff (Fig 2).

    Conclusion:  Using readily available EHR data, we developed a Cdiff risk stratification tool that was able to predict Cdiff 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 Cdiff illness.

    Fig 1:

    Fig 2:

    Marci Drees, MD, MS1,2, Edward F. Ewen, MD2, Michael Winiarz, BS, MS2 and Stephen Eppes, MD1,2, (1)Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, (2)Christiana Care Health System, Newark, DE


    M. Drees, None

    E. F. Ewen, None

    M. Winiarz, None

    S. Eppes, None

    Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 3rd with the exception of research findings presented at the IDWeek press conferences.