Predictors of bleeding event among elderly patients with mechanical valve replacement using random forest model

Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable in Asian populations. Thus, we aimed to identify an accurate model for predicting bleeding in elderly patients receiving warfarin after mechanical valve replacement in a Korean population. In this retrospective cohort study, a random forest model was used to determine factors predicting bleeding events among 598 participants. Twenty-two descriptors were selected as predictors for bleeding. Steroid use was the most important predictor of bleeding events, followed by labile international normalized ratio, history of stroke, history of myocardial infarction, and cancer. The random forest model was sensitive (80.77%), specific (87.67%), and accurate (85.86%), with an area under the curve of 0.87, suggesting fair prediction. In the elderly, drug interactions with steroids and overall physical condition had a significant effect on bleeding. Elderly patients taking warfarin for life require lifelong management.