Do genetic mutations and clinical markers predict sudden cardiac death risk in patients with hypertrophic cardiomyopathy?
Genetic and clinical markers show a moderate but highly variable association with sudden cardiac death risk in HCM, highlighting the need for integrated multi-parametric risk models.
Hypertrophic cardiomyopathy (HCM) is a genetically diverse cardiac condition and a leading cause of sudden cardiac death (SCD), particularly among young individuals. Despite advancements in imaging and clinical risk scores, stratifying SCD risk in HCM remains challenging. This systematic review and meta-analysis aimed to evaluate the predictive value of genetic mutations and clinical markers in assessing SCD risk in HCM patients. A structured search of PubMed, Scopus, Web of Science, and Google Scholar from 2015 to 2025 identified ten eligible studies involving diverse populations, including both pediatric and adult HCM cohorts. These studies investigated sarcomeric gene mutations such as MYH7 and MYBPC3, as well as clinical indicators including nonsustained ventricular tachycardia (NSVT), syncope, family history of SCD, and late gadolinium enhancement (LGE). Data extraction, risk of bias assessment (using GRADE), and statistical synthesis were conducted in accordance with PRISMA guidelines. The meta-analysis revealed a pooled effect size of 0.89 (95% CI: -0.27 to 2.05) using a random-effects model, indicating a moderate positive association between genetic/clinical predictors and SCD risk. However, heterogeneity was high (I² = 91.78%, p < 0.001), suggesting substantial variability in outcomes across studies. Subgroup analysis revealed no significant differences between genetic and clinical predictors, and publication bias assessment showed minor asymmetry in the funnel plot, though Egger’s test was not statistically significant. These findings suggest that while both genetic and clinical markers contribute to SCD risk prediction in HCM, considerable variability exists in their predictive strength. Future research should focus on harmonizing methodologies and developing integrated, multi-parametric risk models that combine genotype, phenotype, and imaging data to enhance individualized risk stratification.
Mahmood et al. (Wed,) studied this question.
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