A novel risk score predicting 30‐day hospital re‐admission of patients with acute stroke by machine learning model

Abstract

AbstractBackgroundThe 30‐day hospital re‐admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re‐admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30‐day hospital re‐admissions after discharge of AS patients and define an early re‐admission risk score (RRS).MethodsThis observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re‐admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis.ResultsOf 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re‐admitted within 30 days from discharge. After identifying the predictors of early re‐admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0–1), medium (RRS = 2–3) and high (RRS >3) with re‐admission rates of 5%, 8% and 14%, respectively.ConclusionsThe identification of risk factors for early re‐admission after AS and the elaboration of a score to stratify at discharge time the risk of re‐admission can provide a tool for clinicians to plan a personalized follow‐up and contain healthcare costs.