Development and Validation of a Risk Prediction Model for Frailty in Patients with Chronic Diseases.

IF 2.1 Q3 GERIATRICS & GERONTOLOGY Gerontology and Geriatric Medicine Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.1177/23337214241282895

Yuanchun Xu, Wei Cao, Zongsheng He, Nuoyi Wu, Mingyu Cai, Li Yang, Shuying Liu, Wangping Jia, Haiyan He, Yaling Wang

{"title":"Development and Validation of a Risk Prediction Model for Frailty in Patients with Chronic Diseases.","authors":"Yuanchun Xu, Wei Cao, Zongsheng He, Nuoyi Wu, Mingyu Cai, Li Yang, Shuying Liu, Wangping Jia, Haiyan He, Yaling Wang","doi":"10.1177/23337214241282895","DOIUrl":null,"url":null,"abstract":"<p><p>The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations.</p>","PeriodicalId":52146,"journal":{"name":"Gerontology and Geriatric Medicine","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497504/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gerontology and Geriatric Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/23337214241282895","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

慢性病患者虚弱风险预测模型的开发与验证。

在慢性病患者中，体弱的发生率很高。然而，对这些患者的虚弱程度进行评估还远未成为临床实践的常规部分。本研究旨在开发一个有效的预测模型，用于评估慢性病患者的虚弱风险。本研究共招募了 543 名慢性病患者，其中 237 人参与了预测模型的开发和验证。共分析了 57 项与虚弱相关的指标，包括社会人口学变量、健康状况、体格测量、营养评估、体力活动水平和血液生物标志物。有 100 个病例（42.2%）出现虚弱症状。多变量逻辑回归分析表明，性别、年龄、慢性病、迷你营养评估评分和临床虚弱量表评分是慢性病患者虚弱的预测因素。利用这些因素建立的提名图模型具有良好的一致性和准确性。预测模型和验证集的 AUC 值分别为 0.946 和 0.945。校准曲线、ROC 和 DCA 表明，提名图具有良好的预测性能。总之，本文开发的综合提名图是评估慢性病患者虚弱风险的一种有前途的便捷工具，有助于临床医师筛查高危人群。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Gerontology and Geriatric Medicine Medicine-Geriatrics and Gerontology

CiteScore

2.90

自引率

3.70%

发文量

119

审稿时长

12 weeks

期刊介绍： Gerontology and Geriatric Medicine (GGM) is an interdisciplinary, peer-reviewed open access journal where scholars from a variety of disciplines present their work focusing on the psychological, behavioral, social, and biological aspects of aging, and public health services and research related to aging. The journal addresses a wide variety of topics related to health services research in gerontology and geriatrics. GGM seeks to be one of the world’s premier Open Access outlets for gerontological academic research. As such, GGM does not limit content due to page budgets or thematic significance. Papers will be subjected to rigorous peer review but will be selected solely on the basis of whether the research is sound and deserves publication. By virtue of not restricting papers to a narrow discipline, GGM facilitates the discovery of the connections between papers.