The development and validation of a nomogram-based risk prediction model for mortality among older adults

Jun Duan; MingXia Wang; Napoleon Bellua Sam; Qin Tian; TingTing Zheng; Yun Chen; XiaoMei Deng; Yan Liu

doi:10.1016/j.ssmph.2024.101605

The development and validation of a nomogram-based risk prediction model for mortality among older adults

SSM Popul Health. 2024 Jan 6:25:101605. doi: 10.1016/j.ssmph.2024.101605. eCollection 2024 Mar.

Authors

Jun Duan¹, MingXia Wang², Napoleon Bellua Sam³, Qin Tian⁴, TingTing Zheng⁵, Yun Chen⁵, XiaoMei Deng⁶, Yan Liu¹

Affiliations

¹ Department of Medical Record Statistics, Peking University Shenzhen Hospital, Shenzhen, China.
² Department of Stomatology, Luohu Hospital of Traditional Chinese Medicine, Shenzhen, China.
³ Department of Epidemiology and Biostatistics, University for Development Studies, Tamale, Ghana.
⁴ Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.
⁵ Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Institute of Ultrasound Medicine, Shenzhen-PKU-HKUST Medical Center, Shenzhen, China.
⁶ Department of Comprehensive Ward, Peking University Shenzhen Hospital, Shenzhen, China.

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.

Keywords: Longevity; Oldest old; Population-based longitudinal study; Predictive model.