The SUPREME Index for 30-day all-cause readmission: development and internal validation of a machine learning-based risk stratification index in community-dwelling adults

BACKGROUND: Hospital readmissions are a key indicator of healthcare quality and have substantial implications for patient outcomes and system-level costs. Identifying individuals at high risk of experiencing 30-day all-cause readmission events is essential for implementing preventive strategies and improving resource allocation. This study aimed to develop and internally validate a simple, interpretable risk stratification index for identifying individuals at elevated risk of experiencing at least one 30-day all-cause readmission event in a community-dwelling general population using machine learning (ML)-based feature selection and logistic regression (LR)-based risk scoring. METHODS: We analyzed data on adults residing in Suwon City from the 2016-2019 South Korean National Health Insurance Service-National Sample Cohort. The outcome was defined at the person level as experiencing at least one all-cause readmission within 30 days of discharge from any hospitalization during the study period. Elastic net (EN) regularization was used to select candidate predictors, and LR was used to derive a point-based scoring index. Model performance was evaluated in an internal validation dataset using the area under the receiver operating characteristic curve (AUC), calibration curves, and Brier score. RESULTS: Among 3,357 adults, 357 (10.6%) experienced at least one 30-day readmission event during the study period. Four of seven features selected by EN (top-quartile number of hospital admissions, length of stay, total healthcare costs, and any malignancy) were retained to construct the SUwon Population-based Readmission risk Estimation ModEl (SUPREME) Index, with scores ranging from 0 to 14. Internal validation showed good performance (AUC = 0.861; Brier score = 0.067) with adequate calibration. A cutoff score of 8 provided a balanced operating point (sensitivity 0.742; specificity 0.867) for classifying individuals as high risk. CONCLUSIONS: The SUPREME Index, developed using EN and LR, provides a practical and interpretable tool for person-level risk stratification using administrative claims data. Because predictors were derived from routinely available claims and screening data and were not anchored to a single uniform baseline time point, the index should be interpreted as a stratification tool rather than an event-level prediction model anchored to a specific index hospitalization. External validation and evaluation in diverse populations are warranted.