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. 2024 Aug;21(8):1390-1397.
doi: 10.1016/j.hrthm.2024.01.031. Epub 2024 Jan 26.

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach

Richard T Carrick  1 , Hisham Ahamed  2 , Eric Sung  3 , Martin S Maron  4 , Christopher Madias  5 , Vennela Avula  3 , Rachael Studley  5 , Chen Bao  5 , Nadia Bokhari  5 , Erick Quintana  5 , Ramiah Rajesh-Kannan  2 , Barry J Maron  4 , Katherine C Wu  3 , Ethan J Rowin  4
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Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach

Richard T Carrick et al. Heart Rhythm. 2024 Aug.

Abstract

Background: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide.

Objective: The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features.

Methods: Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India).

Results: ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%.

Conclusion: In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Keywords: Cardiac magnetic resonance imaging; Deep-learning; Electrocardiography; Hypertrophic cardiomyopathy; Risk prediction; Sudden cardiac death.

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Conflict of interest statement

Disclosures Dr Maron is a consultant for Cytokinetics, iRhythm, Imbria Pharmaceuticals, and Takeda Pharmaceuticals. Dr Rowin has received research funding from Pfizer and iRhythm. The remaining authors have nothing to disclose.

Figures

Figure 1:
Figure 1:
VAE based ECG-DL model for prediction of high-risk imaging features in HCM patients.
Figure 2:
Figure 2:. Saliency analysis.
Representative ECGs are obtained by averaging latent variables for each high-risk (HR) feature and converting to 12-lead, median-beat ECG voltage wave-forms using the pre-trained VAE decoder.
See this image and copyright information in PMC

Comment in

References

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