Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients

Aug 4, 2026

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Machine learning models were developed and validated to predict acute kidney disease (AKD), acute kidney injury (AKI), and hospital mortality in older patients. Older patients aged 65 and above were retrospectively reviewed and assigned to four trajectory groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. Among 22,005 patients, LGBM achieved the best performance for AKD, AKI, and mortality and 15-feature models showed high AUC values. SHAP interpretation identified key predictors and an online prediction tool supported clinical use.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Acute kidney injury, acute kidney disease, and prognosis in older patients

Methods

Study population and kidney dysfunction trajectory groups

Machine learning model development and evaluation

Model interpretation using SHAP

Results

Cohort inclusion and outcome incidence

Predictive performance of the best model

Top predictors identified by SHAP

Online prediction tool development

Discussion

Clinical value of LGBM-based early prediction

Future directions for external validation and clinical integration

OPEN ACCESS EDITED BY Tao-Hsin Tung, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, China REVIEWED BY Pranjal Sharma, Northeast Ohio Medical University, United States Bin Yi, Army Medical University, China *CORRESPONDENCE Chenyu Li [licnyu@163.com](licnyu@163.com)[ ](licnyu@163.com)Yan Xu [xuyan@qdu.edu.cn](xuyan@qdu.edu.cn) †These authors have contributed equally to this work and share first authorship RECEIVED 26 March 2024 ACCEPTED 29 July 2024 PUBLISHED 15 August 2024 CITATION Wang X, Xu L, Guan C, Xu D, Che L, Wang Y, Man X, Li C and Xu Y (2024) Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients. Front. Med. 11:1407354 . doi: 10.3389/fmed.2024.1407354 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) and the copyright owner(s) 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. TYPE Original Research PUBLISHED 15 August 2024 DOI 10.3389/fmed.2024.1407354 Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients Xinyuan Wang 1†, Lingyu Xu 1†, Chen Guan 1, Daojun Xu 2, Lin Che 1, Yanfei Wang 1, Xiaofei Man 1, Chenyu Li 1* and Yan Xu 1* 1 Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China, 2 Department of Nephrology, Linyi People's Hospital, Linyi, China 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 older adult 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 Frontiers in Medicine 01 [frontiersin.org](frontiersin.org) 1 Introduction Acute kidney injury (AKI), a complex public health concern, prevalent in about 12% of patients ( 1–3) and of

FAQs

What were the study’s main prediction targets for older patients?

The study aimed to predict the occurrence of acute kidney disease (AKD), acute kidney injury (AKI), and hospital mortality in older patients.

How were patients grouped to explore kidney dysfunction trajectories?

Patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI.

Which machine learning model performed best, and how were predictors interpreted?

Light gradient boosting machine (LGBM) outperformed other models for predicting AKD, AKI, and mortality. The Shapley additive explanations (SHAP) method was used to identify the top predictors, including AKI stage and laboratory/clinical factors.

Aug 4, 2026

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