Predicting the decline of physical function among the older adults in China: A cohort study based on China longitudinal health and longevity survey (CLHLS)

IF 2.5 3区医学 Q3 GERIATRICS & GERONTOLOGY Geriatric Nursing Pub Date : 2024-11-28 DOI:10.1016/j.gerinurse.2024.11.019

Liang Wang , Xiaobing Xian , Meiling Liu , Jie Li , Qi Shu , Siyi Guo , Ke Xu , Shiwei Cao , Wenjia Zhang , Wenyan Zhao , Mengliang Ye

{"title":"Predicting the decline of physical function among the older adults in China: A cohort study based on China longitudinal health and longevity survey (CLHLS)","authors":"Liang Wang , Xiaobing Xian , Meiling Liu , Jie Li , Qi Shu , Siyi Guo , Ke Xu , Shiwei Cao , Wenjia Zhang , Wenyan Zhao , Mengliang Ye","doi":"10.1016/j.gerinurse.2024.11.019","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>As the arrival of healthy aging, maintaining physical function (PF) in older adults is crucial for their health, so it is necessary to detect the decline of PF among them and take intervention measures.</div></div><div><h3>Methods</h3><div>We construct eight machine learning models to predict declines of PF in this study. The performance of the models was tested by Area Under Curve (AUC), sensitivity, specificity, accuracy, precision-recall (PR) curve and calibration degree. Decision Curve Analysis (DCA) curve was used to evaluate their discrimination ability and clinical practicability.</div></div><div><h3>Results</h3><div>There were 2,017 participants in this study. We found that logistic regression models performed the best, with AUC, sensitivity, specificity and accuracy of 0.803, 0.698, 0.761 and 0.744 respectively, and its DCA curve, calibration degree and PR curve also performed well.</div></div><div><h3>Conclusion</h3><div>Logistic regression can be used as optimal model to identify the risk of PF decline among older adults in China.</div></div>","PeriodicalId":56258,"journal":{"name":"Geriatric Nursing","volume":"61 ","pages":"Pages 378-389"},"PeriodicalIF":2.5000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geriatric Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0197457224004154","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background

As the arrival of healthy aging, maintaining physical function (PF) in older adults is crucial for their health, so it is necessary to detect the decline of PF among them and take intervention measures.

Methods

We construct eight machine learning models to predict declines of PF in this study. The performance of the models was tested by Area Under Curve (AUC), sensitivity, specificity, accuracy, precision-recall (PR) curve and calibration degree. Decision Curve Analysis (DCA) curve was used to evaluate their discrimination ability and clinical practicability.

Results

There were 2,017 participants in this study. We found that logistic regression models performed the best, with AUC, sensitivity, specificity and accuracy of 0.803, 0.698, 0.761 and 0.744 respectively, and its DCA curve, calibration degree and PR curve also performed well.

Conclusion

Logistic regression can be used as optimal model to identify the risk of PF decline among older adults in China.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

中国老年人身体机能下降的预测：基于中国健康与长寿纵向调查（CLHLS）的队列研究

背景随着健康老龄化的到来，老年人保持身体功能（PF）对其健康至关重要，因此有必要检测老年人身体功能的衰退并采取干预措施。通过曲线下面积（AUC）、灵敏度、特异性、准确度、精确度-召回（PR）曲线和校准度来检验模型的性能。采用决策曲线分析（DCA）曲线来评估其辨别能力和临床实用性。我们发现逻辑回归模型表现最佳，其AUC、灵敏度、特异性和准确性分别为0.803、0.698、0.761和0.744，其DCA曲线、校准度和PR曲线也表现良好。

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

Geriatric Nursing 医学-护理

CiteScore

3.80

自引率

7.40%

发文量

257

审稿时长

>12 weeks

期刊介绍： Geriatric Nursing is a comprehensive source for clinical information and management advice relating to the care of older adults. The journal''s peer-reviewed articles report the latest developments in the management of acute and chronic disorders and provide practical advice on care of older adults across the long term continuum. Geriatric Nursing addresses current issues related to drugs, advance directives, staff development and management, legal issues, client and caregiver education, infection control, and other topics. The journal is written specifically for nurses and nurse practitioners who work with older adults in any care setting.