文献类型：期刊文献

英文题名：Temporal Validation of a Machine Learning Readmission Model in Patients With Heart Failure With Preserved Ejection Fraction and Chronic Kidney Disease

作者：Deng, Yaoting[1];Lu, Weijie[1];Zhong, Yang[1];Liu, JiaJia[2];Sheng, Pengcheng[3];Liu, Mengyang[1];Yang, Kang[1];Hu, Yujie[2];Ma, Nan[4];Xie, Ping[1,2,4]

第一作者：Deng, Yaoting

通信作者：Xie, P[1];Xie, P[2];Xie, P[3]

机构：[1]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Gansu, Peoples R China;[2]Lanzhou Univ, Clin Med Coll 1, Lanzhou, Gansu, Peoples R China;[3]Bengbu Med Univ, Clin Med Coll 1, Bengbu, Anhui, Peoples R China;[4]Gansu Prov Hosp, Branch Ctr Construct Unit, Natl Clin Res Ctr Cardiovasc Dis, Dept Cardiovasc Med, Lanzhou, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Gansu, Peoples R China;[2]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou, Gansu, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Branch Ctr Construct Unit, Natl Clin Res Ctr Cardiovasc Dis, Dept Cardiovasc Med, Lanzhou, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：42

期号：9

起止页码：2145

外文期刊名：CANADIAN JOURNAL OF CARDIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001874061600001)】；

基金：Funding Sources This work was supported by grants from the National Natural Science Foundation of China (number 82460051) , and the Major Project of Gansu Province Joint Research Fund (number 24JRRA886) .

语种：英文

外文关键词：Heart failure with preserved ejection fraction; chronic kidney disease; machine learning; patient readmission; risk stratification; prognosis

摘要：Background: The coexistence of heart failure with preserved ejection fraction and chronic kidney disease is associated with high readmission risks. Existing tools, such as the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, rely on linear modelling, which limits their ability to capture nonlinear interactions. We developed a machine learning model to predict readmission and assessed its performance against temporal data drift. Methods: In this retrospective study, patients were temporally stratified into derivation (n = 750) and independent validation (n = 130) cohorts. Feature selection used the least absolute shrinkage and selection operator and recursive feature elimination with cross-validation. Twelve algorithms were trained using stratified nested cross-validation. Model stability was evaluated using the Population Stability Index (PSI) and rolling metrics. Results: Ten features were identified, with estimated glomerular filtration rate as the primary predictor. An interaction was observed between high-sensitivity C-reactive protein and N-terminal pro-B-type natriuretic peptide. The random forest model achieved an area under the receiver operating characteristic curve of 0.837 (95% confidence interval, 0.761-0.905) in the validation cohort, compared with 0.551 for the MAGGIC score. Longitudinal analysis indicated temporal stability, with feature PSI scores < 0.25. Conclusions: A machine learning model that incorporated renal, inflammatory, and hemodynamic parameters demonstrated greater predictive accuracy for 1-year readmission compared with the MAG-GIC score. The model maintained temporal stability, in support of its potential utility for risk stratification.