{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:38:40Z","timestamp":1791819520553,"version":"4.3.4"},"reference-count":51,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Advanced Nursing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Frailty affects over 35% of maintenance haemodialysis (MHD) patients globally\u20142\u20133 times higher than the general elderly\u2014and is strongly linked to higher mortality, hospitalisation, and functional decline. Despite its clinical impact, frailty is often underdiagnosed in dialysis settings due to inconsistent assessments and limited resources. Existing prediction models vary widely in predictors and methods, requiring systematic review to guide clinical use and improve risk\u2010stratified care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To systematically identify, describe, and evaluate the existing risk prediction models for frailty in patients undergoing MHD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Systematic review and Methodological appraisal.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Sources<\/jats:title>\n                    <jats:p>A comprehensive search was conducted across multiple databases\u2014PubMed, Web of Science Core Collection, Embase, Cochrane Library, CINAHL, China Biomedical Literature Database (CBM), Wanfang Database, VIP Database\u2014covering studies up to November 1, 2024.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Review Methods<\/jats:title>\n                    <jats:p>Two researchers independently conducted literature searches, screening, and data extraction. They used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the risk of bias and the applicability of the included models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifteen studies (21 models) were analysed, with sample sizes 141\u2013786 and frailty incidence 11.00%\u201359.57%. Model AUCs ranged 0.720\u20130.998 (potential overfitting at extreme values). Key predictors included age, serum albumin, gender, Charlson comorbidity index, and activities of daily living scores. Methodological appraisal using PROBAST revealed moderate applicability but high bias risks: 53% of studies used retrospective designs, 95% lacked external validation, and limitations included small samples, non\u2010standard variable selection, and inadequate handling of missing data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While models demonstrate initial predictive utility, widespread bias and developmental\u2010stage limitations hinder clinical application. Future research must prioritise TRIPOD\u2010guided model development, emphasising large prospective cohorts, rigorous validation, and transparent reporting to enhance reliability and clinical utility in frailty risk stratification for MHD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jan.16915","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:05:09Z","timestamp":1743055509000},"page":"188-204","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Risk Prediction Models for Frailty in Adult Maintenance Haemodialysis Patients: A Systematic Review and Methodological Appraisal"],"prefix":"10.1111","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4522-3091","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Nursing Hubei University of Traditional Chinese Medicine  Wuhan Hubei People's 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