AI-Facilitated Assessment of Built Environment Using Neighborhood Satellite Imagery and Cardiovascular Risk.
AI
built environment
cardiovascular risk prediction
coronary artery calcium
major adverse cardiovascular events
satellite imagery
Journal
Journal of the American College of Cardiology
ISSN: 1558-3597
Titre abrégé: J Am Coll Cardiol
Pays: United States
ID NLM: 8301365
Informations de publication
Date de publication:
29 Oct 2024
29 Oct 2024
Historique:
received:
14
06
2024
revised:
12
08
2024
accepted:
13
08
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
23
10
2024
Statut:
ppublish
Résumé
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. The purpose of this study was to investigate the association between satellite image-based environment and risk of major adverse cardiovascular events (MACE). 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. 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. 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.
Sections du résumé
BACKGROUND
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
OBJECTIVE
The purpose of this study was to investigate the association between satellite image-based environment and risk of major adverse cardiovascular events (MACE).
METHODS
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
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
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.
Identifiants
pubmed: 39443017
pii: S0735-1097(24)08317-7
doi: 10.1016/j.jacc.2024.08.053
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1733-1744Informations de copyright
Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Funding Support and Author Disclosures This work was funded by the National Institute on Minority Health and Health Disparities Award P50MD017351 and 1R35ES031702-01 awarded to Dr Rajagopalan. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.