Moorthy Skanda, Chen Zhuo, Zhang Tong, Ponnana Sai Rahul, Sirasapalli Santosh Kumar, Shivanantham Kanimozhi, Khraishah Haitham, Dazard Jean-Eudes, Al-Kindi Sadeer G, Deo Salil V, Rajagopalan Sanjay
Department of Medicine, Cardiovascular Research Institute, Case Western Reserve University, Cleveland, OH, USA.
Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist, Houston, TX, USA.
Sci Total Environ. 2025 Jun 10;980:179596. doi: 10.1016/j.scitotenv.2025.179596. Epub 2025 May 3.
Can AI-extracted interpretable built environment features predict major adverse cardiovascular events (MACE) in a national veteran population?
In this cohort study of 770,990 U.S. veterans, seven built environment features were significantly associated with an increased risk of MACE. Two features, old/dilapidated buildings and visible wire, were associated with a decreased risk. Greenery was only linked to increased risk in higher social deprivation index quartiles.
Built environment features can serve as predictors of MACE, highlighting the potential role of neighborhood characteristics in cardiovascular risk stratification beyond traditional factors.
The relationship between built environment features and major adverse cardiovascular events (MACE) in patients with atherosclerotic cardiovascular disease (ASCVD) remains understudied. Our study aims to discover what specific built environmental features influence cardiovascular mortality.
Retrospective cohort study of US Veterans (2016-2021) (98 % male, 86 % white) with stable ASCVD (coronary artery disease, cerebrovascular disease, or peripheral artery disease).
Built environment features were assessed using 164 million Google Street View images (2019) sampled every 50 m across the US. Eleven features, including greenery, sidewalks, and non-single-family homes, were analyzed as the percentage of images containing each element. These were summarized at the census tract level and linked to participant healthcare data via residential addresses.
The primary outcome was first MACE occurrence (non-fatal myocardial infarction, non-fatal stroke, or cardiovascular mortality). Associations were analyzed using multivariable Fine Gray models, adjusting for demographics, clinical factors, the social deprivation index, and competing risks.
Over a 4-year median follow-up, features associated with increased MACE risk included greenery (subHR: 1.054, 95 % CI: 1.047-1.061), single-lane roads (subHR: 1.059, 95 % CI: 1.054-1.065), sidewalks (subHR: 1.023, 95 % CI: 1.020-1.026), crosswalks (subHR: 1.062, 95 % CI: 1.040-1.083), non-single-family homes (subHR: 1.088, 95 % CI: 1.083-1.094), and two or more cars (subHR: 1.013, 95 % CI: 1.006-1.019). Features linked to lower MACE risk included old buildings (subHR: 0.976, 95 % CI: 0.971-0.982) and visible wiring (subHR: 0.972, 95 % CI: 0.967-0.976).
Built environment features influence MACE risk in US Veterans with ASCVD, emphasizing the role of the exposome in cardiovascular health.
人工智能提取的可解释的建筑环境特征能否预测全国退伍军人中的主要不良心血管事件(MACE)?
在这项对770,990名美国退伍军人的队列研究中,七个建筑环境特征与MACE风险增加显著相关。两个特征,即老旧/破败建筑和可见电线,与风险降低相关。绿化仅在社会剥夺指数较高的四分位数中与风险增加有关。
建筑环境特征可作为MACE的预测指标,突出了社区特征在心血管风险分层中超越传统因素的潜在作用。
建筑环境特征与动脉粥样硬化性心血管疾病(ASCVD)患者的主要不良心血管事件(MACE)之间的关系仍未得到充分研究。我们的研究旨在发现哪些特定的建筑环境特征会影响心血管死亡率。
对患有稳定ASCVD(冠状动脉疾病、脑血管疾病或外周动脉疾病)的美国退伍军人(2016 - 2021年)进行回顾性队列研究(98%为男性,86%为白人)。
使用1.64亿张谷歌街景图像(2019年)对建筑环境特征进行评估,这些图像在美国每隔50米采样一次。分析了包括绿化、人行道和非单户住宅在内的11个特征,以包含每个元素的图像百分比表示。这些特征在普查区层面进行汇总,并通过居住地址与参与者的医疗保健数据相关联。
主要结局是首次发生MACE(非致命性心肌梗死、非致命性中风或心血管死亡)。使用多变量Fine Gray模型分析关联,对人口统计学、临床因素、社会剥夺指数和竞争风险进行调整。
在4年的中位随访期内,与MACE风险增加相关的特征包括绿化(亚危险比:1.054,95%置信区间:1.047 - 1.061)、单车道道路(亚危险比:1.059,95%置信区间:1.054 - 1.065)、人行道(亚危险比:1.023,95%置信区间:1.020 - 1.026)、人行横道(亚危险比:1.062,95%置信区间:1.040 - 1.083)、非单户住宅(亚危险比:1.088,95%置信区间:1.083 - 1.094)以及两辆或更多汽车(亚危险比:1.013,95%置信区间:1.006 - 1.019)。与MACE风险降低相关的特征包括老旧建筑(亚危险比:0.976,95%置信区间:0.971 - 0.982)和可见电线(亚危险比:0.972,95%置信区间:0.967 - 0.976)。
建筑环境特征会影响患有ASCVD的美国退伍军人的MACE风险,强调了暴露组在心血管健康中的作用。