AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CAC
Artificial intelligence
Atrial fibrillation
CHARGE-AF
Coronary artery calcium
Left atrial volume
NT-proBNP
Journal
Journal of cardiovascular computed tomography
ISSN: 1876-861X
Titre abrégé: J Cardiovasc Comput Tomogr
Pays: United States
ID NLM: 101308347
Informations de publication
Date de publication:
23 Apr 2024
23 Apr 2024
Historique:
received:
07
02
2024
revised:
04
04
2024
accepted:
13
04
2024
medline:
24
4
2024
pubmed:
24
4
2024
entrez:
23
4
2024
Statut:
aheadofprint
Résumé
Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p < 0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p < 0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p < 0.01). AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.
Sections du résumé
BACKGROUND
BACKGROUND
Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.
METHODS
METHODS
We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC
RESULTS
RESULTS
At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p < 0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p < 0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p < 0.01).
CONCLUSION
CONCLUSIONS
AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.
Identifiants
pubmed: 38653606
pii: S1934-5925(24)00079-0
doi: 10.1016/j.jcct.2024.04.005
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.