Evidence map›Paper›PMID 41926005›Full record

ArticleInternational urology and nephrology2026

The impact of frailty on the survival prognosis of maintenance hemodialysis patients and the construction and validation of a survival prediction model.

Yuanyuan Liu, Jing Liu, Shuqi Hou, Lingling Chang, Hanli Wu

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in International urology and nephrology, 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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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yuanyuan LiuClinical Medical College of Shandong Second Medical University, Weifang, 262500, Shandong, China.
Jing LiuClinical Medical College of Shandong Second Medical University, Weifang, 262500, Shandong, China.
Shuqi HouClinical Medical College of Shandong Second Medical University, Weifang, 262500, Shandong, China.
Lingling ChangDepartment of Nephrology, Yidu Central Hospital of Weifang City, 5168 Jiangjunshan Road, Qingzhou, Weifang, 262500, Shandong, China.
Hanli WuDepartment of Nephrology, Yidu Central Hospital of Weifang City, 5168 Jiangjunshan Road, Qingzhou, Weifang, 262500, Shandong, China. sdqzwhl@126.com.

Funding

Scientific Research and Development Fund Project of Affiliated Hospital of Shandong Second Medical University (Teaching Hospital) 2024FYM042
6 · The paper itself

Abstract

objectiveThis study aims to examine the impact of frailty on the survival outcomes of patients undergoing maintenance hemodialysis (HD) and to develop a predictive model for mortality risk.

methodsIn this prospective cohort study, 400 HD patients were enrolled and followed for 24 months. Frailty was assessed by the Fried phenotype. Depression and anxiety were evaluated using the PHQ-9 and GAD-7 scales, respectively. Patients were randomly split into a model development group (n = 280) and a validation group (n = 120). Kaplan-Meier curves and the log-rank test were used for survival analysis. Independent predictors were identified using LASSO-Cox regression to construct a nomogram. Model performance was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).

resultsThe prevalence of frailty was 45.75%. Multivariable analysis identified frailty (HR = 1.85, 95% CI 1.03-3.36), age (HR = 1.04, 95% CI 1.01-1.07), depression (HR = 4.91, 95% CI 2.00-12.04), anxiety (HR = 3.49, 95% CI 1.78-6.83), cardiovascular disease (HR = 2.06, 95% CI 1.13-3.78), serum creatinine (HR = 1.004, 95% CI 1.003-1.005), and total cholesterol (HR = 1.50, 95% CI 1.13-2.00) as independent risk factors (all P < 0.05). The model demonstrated a C-index of 0.903. In the validation cohort, the AUCs were 0.889 (6-month), 0.897 (1-year), and 0.941 (2-year). Calibration and DCA confirmed good accuracy and clinical utility.

conclusionFrailty is prevalent and independently associated with mortality in HD patients. The developed nomogram provides an accurate tool for individualized risk prediction. The particularly strong influence of depression and anxiety on survival underscores the critical need for integrating routine psychological screening into the clinical management of hemodialysis patients.

Indexed as

FrailtyKidney Failure, ChronicPredictive Learning ModelsRenal DialysisAgedAnxietyArea Under CurveDepressionFemaleFollow-Up StudiesHumansKaplan-Meier EstimateMaleMiddle AgedPrevalencePrognosisFrailtyFrailty prediction modelHemodialysisPrognosis

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.