Evidence map›Paper›PMID 42860582›Full record

ArticleAnnals of medicine2026

Construction and validation of an identification model for frailty in maintenance hemodialysis patients using multiple machine learning algorithms: a cross-sectional study of retrospectively collected data.

Jiao Wang, Ping Yu, Wenjuan Sun, Yina Wang, Yijun Zhou, Yuxin Liu, Hong Bian, Yuqing Chen, Xuchu Duan, Xu Zhang

Abstract readValidation StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jiao WangJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.
Ping YuJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.
Wenjuan SunJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.
Yina WangHemodialysis Center, Jianhu County People's Hospital, Yancheng, China.
Yijun ZhouHemodialysis Center, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, China.
Yuxin LiuJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.
Hong BianJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.
Yuqing ChenDepartment of Pharmacy, Affiliated Hospital of Nantong University, school of Life Science, Affiliated Wuxi Clinical College of Nantong University Wuxi No.2 People's Hospital, Nantong University, Nantong, China.
Xuchu DuanDepartment of Pharmacy, Affiliated Hospital of Nantong University, school of Life Science, Affiliated Wuxi Clinical College of Nantong University Wuxi No.2 People's Hospital, Nantong University, Nantong, China.
Xu ZhangJiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi Medical College, Jiangnan university, Wuxi, China.ORCID 0009-0008-2672-6260

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFrailty adversely affects survival and quality of life in patients undergoing maintenance hemodialysis (MHD). This study aimed to develop a clinical multimodal model to identify prevalent frailty in MHD patients.

methodsClinical data from 626 MHD patients across four tertiary hospitals were retrospectively collected, including demographics, clinical characteristics, laboratory indicators, and physical function assessments. Frailty was assessed using the FRAIL scale. Patients from Wuxi (

resultsFrailty prevalence was 48.6%. Six predictors were identified: age, grip strength, comorbidities, Charlson comorbidity index (CCI), albumin, and pro-B-type natriuretic peptide (pro-BNP). The extreme gradient boosting (XGBoost) achieved the best external validation performance (AUC=0.917, 95% CI: 0.867-0.967; accuracy=0.847; sensitivity=0.850; specificity=0.844), with good calibration and clinical net benefit. The shapley additive explanations (SHAP) identified pro-BNP as the most influential predictor and revealed nonlinear associations and clinically relevant thresholds.

conclusionThe interpretable XGBoost model effectively identified prevalent frailty in MHD patients, demonstrating strong discrimination, calibration, and clinical applicability, and may support systematic frailty screening and individualized assessment.

Indexed as

FrailtyKidney Failure, ChronicMachine LearningRenal DialysisAgedBoosting Machine Learning AlgorithmsChinaClassification AlgorithmsComorbidityCross-Sectional StudiesFemaleHumansMaleMiddle AgedPredictive Learning ModelsPrevalencefrailtyfrailty classificationmachine learningMaintenance hemodialysis

Identifiers

PMID42860582
PMCPMC13656680

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