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ORIGINAL RESEARCH article

Front. Med.
Sec. Geriatric Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1407354

Machine Learning-Based Risk Prediction of Acute Kidney Disease and Hospital Mortality in Older Patients

Provisionally accepted
Xinyuan Wang Xinyuan Wang 1Lingyu Xu Lingyu Xu 1Chen Guan Chen Guan 1Daojun Xu Daojun Xu 2Lin Che Lin Che 1Yanfei Wang Yanfei Wang 1Xiaofei Man Xiaofei Man 1Chenyu Li Chenyu Li 1*Yan Xu Yan Xu 1*
  • 1 The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
  • 2 Linyi People's Hospital, Linyi, Shandong Province, China

The final, formatted version of the article will be published soon.

    Introduction: Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients. Methods: We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method. Results: A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models. Discussion: The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving elderly care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

    Keywords: Acute kidney disease, Hospital Mortality, risk prediction, machine learning, older people

    Received: 26 Mar 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Wang, Xu, Guan, Xu, Che, Wang, Man, Li and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Chenyu Li, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong Province, China
    Yan Xu, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.