Prediction of family inheritance in non-sarcomeric hypertrophic cardiomyopathy using clinical variables and 12-lead electrocardiogram artificial intelligence models
Authors: Desjardins S , Bahani M , Swedsky F , Rouleau J L , Cadrin-Tourigny J , Ducharme A , Boulet J , Giraldeau G , L'allier P , Garceau P , Gagnon J , Fortier A , Tadros R , Avram R C , Tremblay-Gravel M 
Published: October 2024

Abstract

Background
Hypertrophic cardiomyopathy (HCM) can be familial even in the absence of a causative sarcomeric variant, justifying periodical screening of first-degree relatives. However, indiscriminate screening may burden low-risk individuals unnecessarily. The prevalence and predictors of familial disease in non-sarcomeric HCM remain inadequately characterized.

Purpose
Our study aims to develop a tailored approach to family screening based on index cases’ clinical profiles. By leveraging artificial intelligence (AI) models, we aim to predict the likelihood of family disease using pertinent clinical variables and electrocardiograms (ECGs) from the proband.

Methods
From January 2008 to June 2022, all probands diagnosed with HCM at our Heart Institute genetics clinic were included if they had i) negative genetic testing for a (likely) pathogenic variant in a sarcomeric gene and ii) ≥1 first- or second-degree relative screened for HCM. The outcome was the presence of family disease defined as left ventricular wall thickness ≥13mm or verified autopsy report confirming HCM. Predictors of family disease were assessed at the first genetic clinic visit and considered age, sex, hypertension, and echocardiography metrics (maximal wall thickness, septal morphology, outflow tract obstruction at rest or Valsava). Using 12-lead ECGs and clinical variables, we used machine learning to train two distinct AI models. Model A utilized ECG signals only to train a ResNet model, while Model B used both ECGs and selected clinical variables (below 0.2 alpha level in logistic regressions) to train a random forest model to predict family disease. We performed evaluations at both ECG-level (one ECG used per patient) and patient-level (all available ECGs used in a patient) and we present individual and ensemble model performance.

Results
Among 356 families with sarcomere-negative HCM, 106 (30%) were familial. Probands were aged 53 ± 16 years, 30% female, 36% with a diagnosis of hypertension and 43% with peak outflow tract obstruction ≥30mmHg. In univariable logistic regressions, family disease was associated with age at diagnosis (OR 0.89 per 5-year, P=0.003), hypertension (OR 0.60, P=0.046) and obstruction (OR 0.52, P=0.013). The ensemble AI model, which integrated clinical and ECG data, achieved an AUC of 0.82 (95% CI: [0.795, 0.848]). It demonstrated a specificity of 0.891 (95% CI: [0.867, 0.913]) and a negative predictive value of 0.798 (95% CI: [0.772, 0.823]). When comparing ECG-level and patient-level data (1 to 10 ECGs per patient), the models consistently achieved AUC values between 0.78 and 0.81, indicating stable performance across various data subsets.

Conclusion
The prevalence of family inheritance in sarcomere-negative HCM can be accurately predicted using AI models that integrate clinical variables and 12-lead ECG data. This approach could enable tailored screening strategies based on the likelihood of family disease.