Liang Wang , Xiaobing Xian , Meiling Liu , Jie Li , Qi Shu , Siyi Guo , Ke Xu , Shiwei Cao , Wenjia Zhang , Wenyan Zhao , Mengliang Ye
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Predicting the decline of physical function among the older adults in China: A cohort study based on China longitudinal health and longevity survey (CLHLS)
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.
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.