用于心血管评估的综合社会环境风险评分：一种可解释的机器学习方法。
Composite socio-environmental risk score for cardiovascular assessment: An explainable machine learning approach.
作者信息
Chen Zhuo, Dazard Jean-Eudes, Salerno Pedro Rafael Vieira de Oliveira, Sirasapalli Santosh Kumar, Makhlouf Mohamed He, Rajagopalan Sanjay, Al-Kindi Sadeer
机构信息
Herman K Hellerstein Professor of Cardiovascular Research, Director, Cardiovascular Research Institute, Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States.
Associate Professor of Cardiology, Associate Director, Cardiovascular Prevention & Wellness, Center for CV Computational & Precision Health, Jerold B Katz Investigator, Academy of Translational Research, Houston Methodist DeBakey Heart & Vascular Center, Medical Director, Center for Health and Nature and Department of Cardiology, Houston Methodist, Houston, TX, United States.
出版信息
Am J Prev Cardiol. 2025 Mar 17;22:100964. doi: 10.1016/j.ajpc.2025.100964. eCollection 2025 Jun.
BACKGROUND
Cardiovascular disease (CVD) is the leading global cause of death, with socio-environmental factors significantly influencing morbidity and mortality. Understanding these factors is essential for improving risk assessments and interventions.
OBJECTIVE
To develop and evaluate the predictive power of a composite socio-environmental (SE) cardiovascular risk score in forecasting major adverse cardiovascular events (MACE) among patients, considering both traditional and novel socio-environmental risk factors.
METHODS
A Survival Random Forest (RSF) model was used to create a composite socio-environmental (SE) cardiovascular risk score using 22 census-tract level variables from 62,438 patients in the CLARIFY registry undergoing coronary artery calcium (CAC) scoring. A Cox Proportional Hazard (CPH) model was then applied to assess the association between the SE-MACE risk score and MACE in a hold-out test set. SHapley Additive exPlanations (SHAP) values were used to identify variable importance.
RESULTS
The study included 62,438 individuals (mean age 59.6 years, 53.2 % female, 87.7 % White). Hypertension (55.4 %), diabetes (15.7 %), and dyslipidemia (72.3 %) were common, with a median CAC score of 168. The RSF model showed a concordance index of 0.58, with significant factors including smoking prevalence, insurance status, and median household income impacting cardiovascular risk. The SE-MACE risk score was robustly associated with MACE (HR, 1.21 [95 % CI, 1.11-1.32]), independent of clinical variables and the CAC score. Kaplan-Meier analysis highlighted clear risk stratification across SE-MACE score quartiles.
CONCLUSION
The SE-MACE risk score effectively incorporates socio-environmental factors into cardiovascular risk assessment, identifying individuals at higher risk for MACE and supporting the need for holistic assessment frameworks. Further validation in diverse settings is recommended to confirm these findings.
背景
心血管疾病（CVD）是全球首要的死亡原因，社会环境因素对发病率和死亡率有显著影响。了解这些因素对于改善风险评估和干预措施至关重要。
目的
开发并评估综合社会环境（SE）心血管风险评分在预测患者主要不良心血管事件（MACE）方面的预测能力，同时考虑传统和新型社会环境风险因素。
方法
使用生存随机森林（RSF）模型，利用CLARIFY注册研究中62438例接受冠状动脉钙化（CAC）评分患者的22个普查区水平变量创建综合社会环境（SE）心血管风险评分。然后应用Cox比例风险（CPH）模型在一个保留测试集中评估SE-MACE风险评分与MACE之间的关联。使用SHapley加法解释（SHAP）值来确定变量的重要性。
结果
该研究纳入了62438名个体（平均年龄59.6岁，53.2%为女性，87.7%为白人）。高血压（55.4%）、糖尿病（15.7%）和血脂异常（72.3%）较为常见，CAC评分中位数为168。RSF模型的一致性指数为0.58，显著因素包括吸烟率、保险状况和家庭收入中位数，这些因素会影响心血管风险。SE-MACE风险评分与MACE密切相关（HR，1.21[95%CI，1.11-1.32]），独立于临床变量和CAC评分。Kaplan-Meier分析突出了SE-MACE评分四分位数间明显的风险分层。
结论
SE-MACE风险评分有效地将社会环境因素纳入心血管风险评估中，可以识别出发生MACE风险较高的个体，并支持采用整体评估框架的必要性。建议在不同环境中进行进一步验证以证实这些发现。
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