2026-02-19 マウントサイナイ医療システム(MSHS)
<関連情報>
- https://www.mountsinai.org/about/newsroom/2026/ai-powered-ecg-could-help-guide-lifelong-heart-monitoring-for-patients-with-repaired-tetralogy-of-fallot
- https://pmc.ncbi.nlm.nih.gov/articles/PMC12902437/
修復されたファロー四徴症における心室リモデリングのための人工知能心電図モデルの開発と多施設検証 Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot
Son Q Duong, Akhil Vaid, Pengfei Jiang,, Yuval Bitterman, Yamini Krishnamurthy, I Min Chiu, Joshua Finer, Brian Cleary, Benjamin S Glicksberg, Ruchira Garg, Michael DiLorenzo, Mark Friedberg, Evan Zahn, Matthew Lewis, Michael Satzer, David Ouyang, Pierre Elias, Tal Geva, Sunil Ghelani, Brett R Anderson, Ali Zaidi, Rachel M Wald, Girish N Nadkarni, Joshua Mayourian
European Heart Journal: Digital Health Published:2026 Feb 2
DOI:https://doi.org/10.1093/ehjdh/ztag015
Graphical Abstract
Abstract
Aims
Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR is uneven, and compliance is poor. We developed a 12-lead electrocardiogram (ECG) artificial intelligence (AI) biomarker to identify CMR-quantified adverse biventricular remodelling in repaired TOF.
Methods and results
Six (1 train/5 external test) North American retrospective cohorts with paired ECG and CMR were included. The main outcome was a composite of ≥2 TOF-specific CMR abnormalities: right ventricular (RV) end-diastolic volume ≥ 160 mL/m2, RV end-systolic volume ≥ 80 mL/m2, RV ejection fraction (EF) <47%, and left ventricular EF <55%. Model discrimination, calibration, and net benefit as a screening test to rule out ventricular remodelling were assessed. Nine hundred and eight patients (2552 ECGs) were included in training, and 782 patients (1795 ECGs) in external validation (outcome prevalence 57%). The area under the receiver-operating curve (AUROC) was 0.85 (95% confidence interval 0.83–0.87), and average precision was 0.88. At a screening risk-threshold of 0.25, there was 92% sensitivity, 41% specificity, 87% negative predictive value, and 55% positive predictive value for ventricular remodelling, which yielded a 13% net reduction in CMR use on net benefit analysis. There was no difference by sex or race/ethnicity, but there were differences by age and site, with two of five sites with lower AUROC than the others, and three of five sites met criteria for miscalibration, which improved after centre-specific calibration.
Conclusion
An artificial intelligence analysis of electrocardiogram (AI-ECG) biomarker in repaired TOF effectively identifies ventricular remodelling to inform timing of advanced imaging. Extensive external validation revealed variation in discrimination and calibration that are important considerations for clinical implementation and regulatory approval pathways of AI-ECG in congenital heart disease.
