Development and Validation of a web-based Postoperative Clostridioides difficile infection risk prediction model

BackgroundClostridioides difficile infection is associated with significant morbidity, mortality and increased costs. Assessment of the postoperative C. difficile infection risk is necessary to improve the outcome of surgical patients. ObjectiveTo develop and validate a risk prediction tool for C. difficile infection after surgery. MethodsIn this retrospective cohort study, 2,451,169 surgical patients from the American College of Surgeons National Surgical Quality Improvement Program Database (ACS-NSQIP) over 2015-2017 were included. Nine predictors were selected for the model: age, preoperative leukocytosis (>12 x109/L), hematocrit ([≤]30%), chronic dialysis, insulin dependent diabetes, weight loss, steroid use, presence of preoperative sepsis, and surgery type. A second model included hospital length of stay as a predictor. A predictive model was developed using ACS-NSQIP 2015-2016 training cohort (n=1,435,157) and tested using 2017 validation cohort (n=1,016,012). Multivariate logistic regression was used for the model. Main outcomeThe primary outcome was postoperative 30-day C. difficile infection (CDI). Results0.39% of the patients (n=9,675) developed CDI and 42.3% (n=4,091) of CDI occurred post-discharge. The Clostridioides difficile risk prediction model had excellent AUC (area under the receiver operating characteristic curve) for postoperative C. difficile infection (training cohort=0.804, test cohort= 0.803). The model that includes hospital length of stay has a high AUC (training cohort=0.841, test cohort=0.838). ConclusionThe C. difficile prediction model provides a robust predictive tool for postoperative C. difficile infection.