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<p>Acute kidney injury (AKI) is a common and serious complication in patients with acute myocardial infarction (AMI) and diabetes mellitus (DM), often leading to poor outcomes. Timely and accurate risk stratification of AKI severity is essential for early intervention and improved prognosis. This study aimed to develop and externally validate machine learning (ML) models for early risk stratification of AKI severity in AMI patients with DM. In this multicenter retrospective study, data were collected from the MIMIC-III/IV, eICU-CRD, and Zhongda Hospital databases. Feature selection was performed via Boruta and LASSO algorithms. A total of 30 predictive models were built using 10 ML algorithms. Model performance was assessed via metrics of discrimination, calibration, and clinical utility. The top-performing models were interpreted using SHapley Additive exPlanations (SHAP) and deployed as an interactive web application. Among the 4,908 AMI patients with DM, 1,930 (39.3%) developed AKI. The LightGBM model achieved the highest area under the curve (AUC) in predicting stage I or higher AKI (AUC = 0.851, 95% CI: 0.822–0.879) and stage II or higher AKI (AUC = 0.874, 95% CI: 0.849–0.897) in the external Zhongda Hospital cohort. For stage III AKI, the XGBoost model performed best, with an AUC of 0.875 (95% CI: 0.846–0.901). We developed and validated ML models for early risk stratification of AKI severity in AMI patients with DM. These models demonstrated robust performance and clinical utility and were integrated into a user-friendly web tool to facilitate individualized risk stratification and early intervention, potentially improving clinical outcomes.</p>

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Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Liang Ruan,
  • Yong Qiao,
  • Xudong Li,
  • Yun Sun,
  • Shuailei Xu,
  • Gaoliang Yan,
  • Dong Wang,
  • Chengchun Tang,
  • Yuhan Qin

摘要

Acute kidney injury (AKI) is a common and serious complication in patients with acute myocardial infarction (AMI) and diabetes mellitus (DM), often leading to poor outcomes. Timely and accurate risk stratification of AKI severity is essential for early intervention and improved prognosis. This study aimed to develop and externally validate machine learning (ML) models for early risk stratification of AKI severity in AMI patients with DM. In this multicenter retrospective study, data were collected from the MIMIC-III/IV, eICU-CRD, and Zhongda Hospital databases. Feature selection was performed via Boruta and LASSO algorithms. A total of 30 predictive models were built using 10 ML algorithms. Model performance was assessed via metrics of discrimination, calibration, and clinical utility. The top-performing models were interpreted using SHapley Additive exPlanations (SHAP) and deployed as an interactive web application. Among the 4,908 AMI patients with DM, 1,930 (39.3%) developed AKI. The LightGBM model achieved the highest area under the curve (AUC) in predicting stage I or higher AKI (AUC = 0.851, 95% CI: 0.822–0.879) and stage II or higher AKI (AUC = 0.874, 95% CI: 0.849–0.897) in the external Zhongda Hospital cohort. For stage III AKI, the XGBoost model performed best, with an AUC of 0.875 (95% CI: 0.846–0.901). We developed and validated ML models for early risk stratification of AKI severity in AMI patients with DM. These models demonstrated robust performance and clinical utility and were integrated into a user-friendly web tool to facilitate individualized risk stratification and early intervention, potentially improving clinical outcomes.