Teshale Achamyeleh Birhanu, Htun Htet Lin, Vered Mor, Owen Alice J, Ryan Joanne, Polkinghorne Kevan R, Kilkenny Monique F, Tonkin Andrew, Freak-Poli Rosanne
School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
J Am Geriatr Soc. 2025 Jun;73(6):1797-1807. doi: 10.1111/jgs.19440. Epub 2025 Mar 18.
Recent evidence underscores the significant impact of social determinants of health (SDoH) on cardiovascular disease (CVD). However, available CVD risk assessment tools often neglect SDoH. This study aimed to integrate SDoH with traditional risk factors to predict CVD risk.
The data was sourced from the ASPirin in Reducing Events in the Elderly (ASPREE) longitudinal study, and its sub-study, the ASPREE Longitudinal Study of Older Persons (ALSOP). The study included 12,896 people (5884 men and 7012 women) aged 70 or older who were initially free of CVD, dementia, and independence-limiting physical disability. The participants were followed for a median of eight years. CVD risk was predicted using state-of-the-art machine learning (ML) and deep learning (DL) models: Random Survival Forest (RSF), Deepsurv, and Neural Multi-Task Logistic Regression (NMTLR), incorporating both SDoH and traditional CVD risk factors as candidate predictors. The permutation-based feature importance method was further utilized to assess the predictive potential of the candidate predictors.
Among men, the RSF model achieved relatively good performance (C-index = 0.732, integrated brier score (IBS) = 0.071, 5-year and 10-year AUC = 0.657 and 0.676 respectively). For women, DeepSurv was the best-performing model (C-index = 0.670, IBS = 0.042, 5-year and 10-year AUC = 0.676 and 0.677 respectively). Regarding the contribution of the candidate predictors, for men, age, urine albumin-to-creatinine ratio, and smoking, along with SDoH variables, were identified as the most significant predictors of CVD. For women, SDoH variables, such as social network, living arrangement, and education, predicted CVD risk better than the traditional risk factors, with age being the exception.
SDoH can improve the accuracy of CVD risk prediction and emerge among the main predictors for CVD. The influence of SDoH was greater for women than for men, reflecting gender-specific impacts of SDoH.
近期证据强调了健康的社会决定因素（SDoH）对心血管疾病（CVD）的重大影响。然而，现有的CVD风险评估工具往往忽视了SDoH。本研究旨在将SDoH与传统风险因素相结合，以预测CVD风险。
数据来源于老年人阿司匹林减少事件（ASPREE）纵向研究及其子研究——老年人ASPREE纵向研究（ALSOP）。该研究纳入了12896名70岁及以上的人群（5884名男性和7012名女性），这些人最初没有CVD、痴呆症和限制独立生活的身体残疾。对参与者进行了为期八年的中位数随访。使用先进的机器学习（ML）和深度学习（DL）模型预测CVD风险：随机生存森林（RSF）、深度生存（Deepsurv）和神经多任务逻辑回归（NMTLR），将SDoH和传统CVD风险因素作为候选预测因子纳入其中。进一步利用基于排列的特征重要性方法来评估候选预测因子的预测潜力。
在男性中，RSF模型表现相对较好（C指数 = 0.732，综合Brier评分（IBS） = 0.071，5年和10年AUC分别为0.657和0.676）。对于女性，深度生存（DeepSurv）是表现最佳的模型（C指数 = 0.670，IBS = 0.042，5年和10年AUC分别为0.676和0.677）。关于候选预测因子的贡献，对于男性，年龄、尿白蛋白与肌酐比值和吸烟，以及SDoH变量，被确定为CVD的最显著预测因子。对于女性，SDoH变量，如社交网络、生活安排和教育程度，比传统风险因素更能预测CVD风险，年龄除外。
SDoH可以提高CVD风险预测的准确性，并成为CVD的主要预测因子之一。SDoH对女性的影响大于男性，反映了SDoH的性别特异性影响。
