Development and validation of frailty risk prediction model for elderly patients with coronary heart disease

• Published in: BMC geriatrics 2024-09, Vol.24 (1), p.742-11, Article 742
• Main Authors: Liu, Siqin, Yuan, Xiaoli, Liang, Heting, Jiang, Zhixia, Yang, Xiaoling, Gao, Huiming
• Format: Article
• Language: English
• Subjects: Activities of Daily Living; Age; Aged; Aged, 80 and over; Cardiovascular disease; China - epidemiology; Complications and side effects; Coronary artery disease; Coronary Disease - diagnosis; Coronary Disease - epidemiology; Coronary heart disease; Demographic aspects; Elderly; Factor analysis; Female; Frail Elderly; Frailty; Frailty - diagnosis; Frailty - epidemiology; Geriatric Assessment - methods; Heart; Heart diseases; Hospitals; Humans; Male; Marital status; Middle Aged; Mortality; Nomograms; Older people; Patients; Prediction models; Predictive model; Questionnaires; Regression analysis; Risk Assessment - methods; Risk Factors; Software; Variables
• ISSN: 1471-2318; 1471-2318
• DOI: 10.1186/s12877-024-05320-7

To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance. A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model. The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P