Qi Chen1, Bihan Tang2, Yinghong Zhai3, Yuqi Chen4, Zhichao Jin1, Hedong Han1, Yongqing Gao3, Cheng Wu1, Tao Chen5, Jia He1. 1. Department of Health Statistics, Navy Medical University, Shanghai, China. 2. Institute of Military Health Management, Navy Medical University, Shanghai, China. 3. School of Medicine, Tongji University, Shanghai, China. 4. Department of Mathematics, New York University, Shanghai, China. 5. Department of Cardiology, PLA General Hospital, Beijing, China.
Abstract
BACKGROUND: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI). METHODS: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model. RESULTS: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. CONCLUSIONS: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.
BACKGROUND: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI). METHODS: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model. RESULTS: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. CONCLUSIONS: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.
Authors: Siying Li; Wenye Fan; Boya Zhu; Chao Ma; Xiaodong Tan; Yaohua Gu Journal: Int J Environ Res Public Health Date: 2022-07-09 Impact factor: 4.614