Sex-specific machine learning models for cardiovascular disease risk prediction in adults aged ≥ 80 years: insights from the Chinese longitudinal healthy longevity survey

Abstract

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of mortality among older adults, yet existing risk prediction tools lack validation for individuals aged ≥ 80 years. This study aims to develop and validate gender-specific machine learning models for CVD risk prediction in adults aged ≥ 80 years using routine biomarkers, and to identify key predictors through interpretable artificial intelligence approaches. METHODS: This prognostic study analyzed two independent longitudinal datasets (2012-2014 and 2014-2018) from the Chinese Longitudinal Healthy Longevity Survey including 1,954 community-dwelling adults aged ≥ 80 years (715 males, 1,239 females) without CVD at baseline. The outcome was incident CVD within 4 years. Five machine learning algorithms (i.e., logistic regression decision tree, support vector machine, random forest, and extreme gradient boosting (XGBoost)) were compared using area under the receiver operating characteristic curve (AUC), recall, specificity, precision, and F1-score. Model interpretability was assessed using SHapley Additive exPlanations values. RESULTS: The XGBoost model demonstrated superior performance for both males (AUC [95% confidence interval], 0.751 [0.635-0.855]; recall, 759 [0.593-0.909]; F1-score, 0.506 [0.369-0.628]) and females (AUC, 0.748 [0.671-0.819]; recall, 0.891 [0.791-0.976]; F1-score, 0.463 [0.369-0.551]). Shared top predictors included vitamin D3, glycated albumin, platelet count, and vitamin B12. Risk stratification based on model predictions effectively identified high-risk individuals, with hazard ratios of 7.46 (95% CI, 2.81-19.78) for males and 5.18 (95% CI, 2.27-11.82) for females in the highest risk group compared to the lowest risk group. CONCLUSIONS: This study developed interpretable, gender-specific machine learning models for CVD risk prediction in the oldest old population using routine biomarkers. The models demonstrated good discrimination and calibration, offering a practical tool for identifying high-risk individuals who may benefit from targeted preventive interventions. These findings suggest the potential utility of biomarker-based machine learning approaches in cardiovascular risk assessment for the rapidly growing elderly population.

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