OBJECTIVE: The purpose of this study was to develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. DESIGN: Retrospective cohort. SETTING: Tertiary care medical center. PATIENTS: Patients admitted to the hospital for ≥48 hours from 1-1-2003 through 12-31-2003. METHODS: Data were collected electronically from the hospital’s Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients’ risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic (ROC) curve was calculated to evaluate potential risk cut-offs. RESULTS: 35,350 admissions with 329 CDI cases were included. Variables in the risk prediction model were age, CDI pressure, admissions in previous 60 days, modified Acute Physiology Score, days on high risk antibiotics, low albumin, admission to an ICU, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index=0.88; Brier score 0.009). CONCLUSIONS: The CDI risk prediction model performed well. Further study is needed to determine if it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs
