Use of Electronic Data to Identify Risk Factors Associated with Clostridium difficile Infection (CDI) and to Develop CDI Risk Scores. (4th October 2017) - Aukes, Laurie; Fireman, Bruce; Lewis, Edwin; Timbol, Julius; Hansen, John; Yu, Holly; Cai, Bing; Gonzalez, Elisa; Lawrence, Jody; Klein, Nicola P
Abstract: Background: Clostridium difficile is a major cause of severe diarrhea in the U.S. We described characteristics of Kaiser Permanente Northern California (KPNC) members with C. difficile infection ( CDI ), identified risk factors associated with CDI, and developed risk scores to predict who may develop CDI. Methods: Retrospective cohort study with all KPNC members ≥18 years old from May 2011 to July 2014 comparing demographic and clinical characteristics for those with and without lab-confirmed incident CDI. We included CDI risk factors in logistic regression models to estimate the risk of developing future CDI after an Identification Recruitment Date (IRD), a time when an individual might be a good candidate for a C. difficile vaccine clinical trial. Two risk score models were created and cross validated (70% of the data used for development and 30% for testing). Results: During the study period, there were 9, 986 CDI cases and 2, 230, 354 members without CDI. CDI cases tended to be ≥65 years old (59% vs.. 21%), female (61% vs. 53%), and white race (70% vs. 53%), with more hospitalizations (42% vs. 3%), emergency room visits (51% vs. 14%), and skilled nursing facility stays (25% vs. 0.6%) in the year prior to CDI compared with members without CDI. At least 10 office visits within the prior year (53% vs. 16%), use of antibiotics in last 12 weeks (81% vs. 11%), proton pump inhibitors in the last year (36% vs. 7%), and multiple medical conditions within the prior year (e.g., chronic kidney disease, congestive heart failure, and pneumonia) were important risk factors for CDI. Using a hospital discharge event as the IRD, our risk score model yielded excellent performance in predicting the likelihood of developing CDI in the subsequent 31 – 365 days (C-statistic of 0.851). Using a random date as the IRD, our model also predicted CDI risk in the subsequent 1–30 days (C-statistic 0.658) and 31–365 days (C-statistic 0.722) reasonably well. Conclusion: CDI can be predicted by increasing age, medications, comorbidities and healthcare exposure, particularly ≥10 office visits, hospitalizations, and skilled nursing stays in the prior year and recent antibiotics. Such risk factors can be used to identify high-risk populations for C. difficile vaccine clinical studies. Disclosures: H. Yu, Pfizer, Inc.: Employee, Salary; B. Cai, Pfizer, Inc.: Employee, Salary; E. Gonzalez, Pfizer, Inc.: Employee, Salary; J. Lawrence, Pfizer, Inc.: Employee, Salary; N. P. Klein, GSK: Investigator, Grant recipient; sanofi pasteur: Investigator, Grant recipient; Merck & Co: Investigator, Grant recipient; MedImmune: Investigator, Grant recipient; Protein Sciences: Investigator, Grant recipient; Pfizer: Investigator, Grant recipient