Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 17, 2025
Date Accepted: Dec 22, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Analysis of risk factors and construction of risk prediction model for frailty in hospitalized elderly people living with HIV: a prospective observational study protocol
ABSTRACT
Abstract Background The aging trend of people living with HIV/AIDS (PLWHAA) in China is increasing day by day. Frailty is a common condition among elderly PLWHA and represents a significant cause of poor prognosis, including falls, decreased quality of life, increased mortality and potentially prolonged hospital stays. Consequently, early frailty screening in this population holds important clinical significance. Objective This study aims to describe the theoretical basis, research objectives and implementation plan of a prospective observational study. It will focus on investigating the epidemiological characteristics and risk factors of frailty syndrome in hospitalized elderly PLWHA, while simultaneously exploring the development of a clinically applicable risk prediction model. Method This study is an ongoing single center prospective observational study, with a plan to recruit at least 556 hospitalized elderly PLWHA (n=445 for development and n=111 for validation). According to the theory of unpleasant symptoms (TOUS), candidate predictors are categorized into physiological factors (including sociodemographic factors, disease-related influencing factors, sleep, nutrition and neurocognitive function), psychological factors (including anxiety and depression status) and environmental factors (including social support status). Potential predictors are screened using univariate analysis and LASSO regression to identify variables for final model inclusion. Model construction and validation employ three standard machine learning algorithms: logistic regression, random forest and support vector machine (SVM). Model performance will be evaluated by reporting accuracy, precision, sensitivity, specificity and the area under the curve. Moral and Communication This prospective study has been approved by the Medical Ethics Committee of Changsha First Hospital and the Medical Ethics Committee of University of South China (2024 HLSC025). Written informed consent from all participants is required before participating in the study. The research results will be reported and disseminated through open and transparent peer-reviewed journals and conference reports. Trial Registration Number China Clinical Trial Registration Center: ChiCTR2500103387
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