Abstract
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a prevalent genetic cardiac disorder with variable clinical expression. While global data are available, phenotypic characterization of Indian HCM patients remains limited. Speckle-tracking echocardiography (STE)-derived global longitudinal strain (GLS) may serve as a surrogate marker of myocardial fibrosis in settings with limited access to cardiac magnetic resonance (CMR). OBJECTIVES: To characterize the clinical and imaging features of Indian patients with HCM and evaluate the correlation of GLS with CMR-derived late gadolinium enhancement (LGE) and electrocardiographic abnormalities. METHODS: This cross-sectional study enrolled 150 consecutive adult HCM patients at a tertiary care center in North India. All patients underwent detailed clinical evaluation, standard transthoracic echocardiography including STE-derived GLS, electrocardiography, and 24-h Holter monitoring. CMR was performed in 74 patients based on physician discretion. GLS was quantified using vendor-specific semi-automated software. LGE was quantified as a percentage of total left ventricular mass. Correlations were assessed using Spearman's coefficient (ρ). RESULTS: The mean age of the cohort was 46.7 ± 13.5 years, with 80 % males. Asymmetric septal hypertrophy (76 %) and obstructive HCM (53 %) were the predominant phenotypes. GLS was reduced (<-20 %) in 89 % of patients (mean GLS: -12.1 % ± 4.1 %). CMR revealed LGE in 92 % of patients, with ≥15 % LGE in 40 %. Peak GLS showed a strong positive correlation with percent LGE (ρ = 0.739). GLS was significantly lower in patients with CMR-detected perfusion deficits (p = 0.04), but not significantly associated with non-sustained ventricular tachycardia (p = 0.18). Modest inverse correlations were noted between GLS and tissue doppler indices (medial e': -0.55; lateral e': -0.60). CONCLUSION: Indian HCM patients exhibit a distinct clinical profile with high fibrosis burden. STE-derived GLS correlates strongly with myocardial fibrosis on CMR and may serve as a practical risk stratification tool in resource-limited settings. Further multicentric studies are needed to validate these findings.