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Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A
Richard T Carrick1, Hisham Ahamed2, Eric Sung1
1Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland.
Insights
Deep-learning models using electrocardiograms (ECG) can identify high-risk hypertrophic cardiomyopathy (HCM) features, potentially reducing the need for resource-intensive cardiac magnetic resonance (CMR) imaging globally.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Hypertrophic cardiomyopathy (HCM) patients face sudden death risk, necessitating identification of high-risk individuals for primary prevention.
- Cardiac magnetic resonance (CMR) imaging is recommended for identifying high-risk HCM features but faces accessibility challenges worldwide.
Purpose of the Study:
- To develop and validate deep-learning (DL) models utilizing electrocardiogram (ECG) data for identifying patients with HCM and high-risk imaging features.
- To assess the potential of ECG-DL models to aid in risk stratification and reduce reliance on CMR imaging.
Main Methods:
- ECG-DL models were developed using data from 1930 HCM patients at Tufts Medical Center.
- Models predicted high-risk features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement.
- External validation was performed on 233 HCM patients from Amrita Hospital HCM Center.
Main Results:
- ECG-DL models demonstrated reliable identification of high-risk features in both holdout testing and external validation cohorts.
- A combined strategy of echocardiography and selective ECG-DL-guided CMR reduced CMR recommendations by 61% while maintaining 97% sensitivity.
- The negative predictive value for absence of high-risk features in patients not recommended for CMR was 99.5%.
Conclusions:
- Novel ECG-DL models effectively identify high-risk imaging features in HCM patients.
- These models offer a potential solution to reduce CMR testing burdens, particularly in underresourced regions.
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.
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