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Journal Article Ppublish 2 citations

PubMed: 39443017 DOI: 10.1016/j.jacc.2024.08.053

AI-Facilitated Assessment of Built Environment Using Neighborhood Satellite Imagery and Cardiovascular Risk.

J Am Coll Cardiol 84(18):1733-1744

Authors: Zhuo Chen, Pedro Rafael Vieira de Oliveira Salerno, Jean-Eudes Dazard, Santosh Kumar Sirasapalli, Mohamed H E Makhlouf, Issam Motairek, Skanda Moorthy, Sadeer Al-Kindi, Sanjay Rajagopalan

Affiliations

  • Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
  • Center for Health and Nature and Department of Cardiology, Houston Methodist, Houston, Texas, USA.
  • Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

Abstract

BACKGROUND

Built environment affects cardiovascular health, but comprehensive assessment in a scalable fashion, for population health and resource allocation, is constrained by limitations of current microscale measures.

OBJECTIVES

The purpose of this study was to investigate the association between satellite image-based environment and risk of major adverse cardiovascular events (MACE).

METHODS

Using a pretrained deep neural network, features depicting the built environment from Google Satellite Imagery (GSI) around 64,230 patients in Northern Ohio undergoing coronary artery calcium (CAC) scoring were extracted. Elastic net regularized Cox proportional hazards models identified associations of GSI features with MACE risk (defined as myocardial infarction, stroke, heart failure, or death). A composite GSI risk score was constructed using features that demonstrated nonzero coefficients in the elastic net model. We assessed association of this score with MACE risk, after adjusting for CAC scores and the social vulnerability index (SVI). Its interactions with CAC scores were also examined in subgroups.

RESULTS

Adjusting for CAC and traditional risk factors, the GSI risk score was significantly associated with higher MACE risk (HR: 2.67; 95% CI: 1.63-4.38; P < 0.001). However, adding SVI reduced this association to nonsignificance (HR: 1.54; 95% CI: 0.91-2.60; P = 0.11). Patients in the highest quartile (Q4) of GSI risk score had a 56% higher observed risk of MACE (HR: 1.56; 95% CI: 1.32-1.86; P < 0.005) compared with the lowest quartile (Q1). The GSI risk score had the strongest association with MACE risk in patients with CAC = 0. This association was attenuated, but remained significant, with higher CAC.

CONCLUSIONS

AI-enhanced satellite images of the built environment were linked to MACE risk, independently of traditional risk factors and CAC, but this was influenced by social determinants of health, represented by SVI. Satellite image-based assessment of the built environment may provide a rapid scalable integrative approach, warranting further exploration for enhanced risk prediction.

MeSH

Built Environment, Imagery, Psychotherapy, Cardiology, Environment Design, Risk Factors, Environment, Satellite Imagery

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