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Updated: Jun 7, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Disability status investigation and risk prediction model for middle-aged and older adults in Anhui Province: A
Jing Li1,2, Shu Wang3, Yan-Bei Zhang1
1Department of Geriatric Respiratory and Critical Care, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Abstract:
This study investigated the disability status of middle-aged and older adults in Anhui Province, China, with a primary focus on physical disability, and constructed a nomogram to predict disability risk. Data was collected through a province-wide questionnaire survey conducted in 16 cities and counties from January to December 2021, involving 3386 participants aged 60 years and above. The abilities of daily living (ADL) scale assessed participants' comprehensive ability. Risk factors for disability were identified using least absolute shrinkage and selection operator and logistic regression, highlighting six independent factors: age, poor sleep quality, depression, malnutrition, stroke, and the number of comorbid chronic diseases. A nomogram prediction model was then established and validated internally and externally. The model demonstrated good fit according to the Hosmer-Lemeshow test and provided significant benefits across various thresholds, as shown by decision curve analysis. This nomogram accurately and quickly identifies high-risk groups, aiding in early intervention.
