Abstract P308: Machine Learning Based Models of the 30-Day Readmission for Stroke Patients Using Electronic Health Record Data

Abstract

Background: At a personalized level, identification of patients at higher risk of 30-day readmission and in need of special clinical attention could lower their chances of readmission. While at a system’s level, reducing hospital readmission improves the overall quality of care delivery and reduces the associated cost burden. Objective: To enhance understanding of the predictors of 30-day readmission after ischemic stroke and identify high-risk individuals. We aimed to compare the performance and the predictive power of machine learning-based methods and identify the best model. Method: The electronic health records (EHR) of acute ischemic stroke patients were extracted from two tertiary centers within the Geisinger Health System between January 1, 2015, and October 7, 2018. A total of 61 variables, including clinical variables, demographical characteristics, discharge status, and type of health insurance were used in this study. Patients were randomly split for model development (80%) and testing (20%). Random forest, gradient boosting machine, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, were developed to predict the 30-day readmission after stroke. The models were evaluated based on the area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). Results: A total of 3,184 patients with ischemic stroke (mean age: 71±13.90 years, men: 51.06%) were included in this study. From the 3,184, 301 (9.40%) were readmitted within 30-day. The best performance was obtained when XGBoost was used with ROSE-sampling. The AUC for the test set was 0.74 (95% CI: 0.64-0.78) with PPV of 0.43. The top four predictors of the 30-day readmission model were National Institutes of Health Stroke Scale score above 24, insert an indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy. Conclusions: Machine learning model can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the five algorithms analyzed, XGBoost had the best performance.