Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers

Abstract

Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging. Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups. Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively). Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.

Keywords: computational clustering; e-cardiology; electrocardiography; hypertrophic cardiomyopathy; phenotyping.

Figure 1

Summary of the methodologies applied in this study for the analysis and classification of 85 HCM patient ECGs using mathematical modeling and machine learning, and to investigate associations with clinical and cardiovascular magnetic resonance features.

Figure 2

Clustering using QRS morphology alone identifies three HCM groups showing group differences in 3 features in lead II and the lateral precordial leads. (A) The three QRS-based HCM groups identified by cluster analysis using QRS morphological biomarkers alone are shown on the 2-dimensional space obtained by dimensionality reduction, as described in Materials and Methods section. (B–D) These QRS-based HCM groups show differences in the 1st, 2nd, and 3rd Hermite coefficients (mathematical functions representing the QRS shape: QRS morphological biomarkers) in leads II, V4 and V6. Healthy volunteers are shown for visual comparison but were not included in Kruskal–Wallis ANOVA (^**p < 1 × 10⁻⁶, ^*p < 0.001).

Figure 3

Four distinct HCM subgroups are identified based on QRS and T wave morphologies. The four HCM groups identified by cluster analysis using both QRS and T wave morphological biomarkers are shown on the 2-dimensional space obtained by dimensionality reduction, as described in Materials and Methods section.

Figure 4

Four distinct ECG phenotypes in hypertrophic cardiomyopathy exhibit differences in hypertrophy morphology and arrhythmic risk. (A) Representative ECGs for patients in each of the four groups with distinct ECG morphology, identified by combined clustering with QRS morphology and T wave biomarkers. Group 1A—normal QRS with inverted T wave (primary T wave inversion), Group 1B—normal QRS with upright T wave, Group 2—short R wave duration and deep S wave in V4, Group 3—left axis deviation, short R wave duration and amplitude, and long S wave duration and amplitude in V4 and V6. (B) Distribution of hypertrophy illustrated using a representative CMR for each group (top), and the segment of maximum left ventricular wall thickness for each patient (marked as a dot) in each group using the AHA 16-segment model (Cerqueira et al., 2002). Group 1A had a predominance of patients with mixed septal and apical left ventricular hypertrophy (LVH; pink dots). Group 1B had the most gene positive patients with no hypertrophy (gray dots). Group 2 and 3 patients mainly had isolated septal hypertrophy (orange dots). Four patients had apical hypertrophy (navy dots). (C) HCM Risk-SCD score for each group. Patients with primary T wave inversion not secondary to QRS abnormalities (Group 1A), had the greatest HCM Risk-SCD score.