Duan J, Wang M, Sam NB, Tian Q, Zheng T, Chen Y, Deng X, Liu Y. The development and validation of a nomogram-based risk prediction model for mortality among older adults.
SSM Popul Health 2024;
25:101605. [PMID:
38292049 PMCID:
PMC10825771 DOI:
10.1016/j.ssmph.2024.101605]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 10/15/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Objective
This research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.
Methods
The derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.
Results
The CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.
Conclusion
This study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.
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