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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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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

  • 期刊论文

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  • 发表日期：

    2026年5月22日

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  • Yeong Jun Ju,
  • Soon Young Lee

摘要

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.