摘要
背景：最近的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，现有的心血管疾病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，预测心血管疾病风险：数据来源于 "ASPirin in Reducing Events in the Elderly (ASPREE) "纵向研究及其子研究 "ASPREE Longitudinal Study of Older Persons (ALSOP)"。该研究包括 12,896 名 70 岁或以上的老年人（男性 5884 人，女性 7012 人），他们最初没有心血管疾病、痴呆症和限制自理能力的肢体残疾。对参与者进行了中位数为 8 年的随访。采用最先进的机器学习（ML）和深度学习（DL）模型预测心血管疾病风险：随机生存森林 (RSF)、Deepsurv 和神经多任务逻辑回归 (NMTLR)，将 SDoH 和传统心血管疾病风险因素作为候选预测因子。结果显示，在男性中，RSF 模型的预测结果达到了预期水平，而在女性中，RSF 模型的预测结果则低于预期水平：结果：在男性中，RSF 模型的性能相对较好（C 指数 = 0.732，综合布赖尔评分 (IBS) = 0.071，5 年和 10 年 AUC 分别为 0.657 和 0.676）。对于女性而言，DeepSurv 是表现最好的模型（C 指数 = 0.670，IBS = 0.042，5 年和 10 年的 AUC 分别为 0.676 和 0.677）。关于候选预测因子的贡献，对于男性而言，年龄、尿白蛋白与肌酐的比率、吸烟以及 SDoH 变量被认为是心血管疾病最重要的预测因子。对于女性而言，社会网络、生活安排和教育等 SDoH 变量比传统风险因素更能预测心血管疾病风险，但年龄是个例外：结论：SDoH 可以提高心血管疾病风险预测的准确性，并成为心血管疾病的主要预测因素之一。SDoH对女性的影响大于男性，反映了SDoH对不同性别的影响。Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
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