What is the clinical evidence from this study?

Study design: Cohort. Population: hypertrophic cardiomyopathy (n=576). Primary outcome: sudden cardiac death or appropriate implantable cardioverter-defibrillator therapy.

March 13, 2026Open Access

Comprehensive risk stratification model for sudden death in hypertrophic cardiomyopathy: integration of cardiac magnetic resonance and clinical parameters

Key Result

Integration of cardiac magnetic resonance parameters with clinical variables improved sudden cardiac death risk prediction in hypertrophic cardiomyopathy, with a C-index of 0.607 and 58 events recorded.

Key Points

To develop and validate a risk prediction model integrating cardiac magnetic resonance and clinical parameters for sudden cardiac death in hypertrophic cardiomyopathy.
Analyzed data from 576 hypertrophic cardiomyopathy patients at a tertiary referral center.
Developed four prediction models: Traditional, Clinical, CMR, and Comprehensive.
Assessed model performance using C-index and time-dependent ROC analysis.
During a median follow-up of 3 years, 10.1% of patients experienced sudden death or appropriate ICD therapy.
The comprehensive model demonstrated superior performance with a C-index of 0.607.
CMR ejection fraction and left ventricular diastolic pressure were identified as independent predictors of risk.

Study Design

Type

Cohort (n=576)

Multicenter

Structured PICO

Does a comprehensive risk prediction model integrating CMR and clinical parameters improve sudden cardiac death risk stratification in patients with hypertrophic cardiomyopathy compared to the traditional HCM Risk-SCD score?

Population

576 adult patients with established hypertrophic cardiomyopathy (HCM), mean age 54.9, 54.9% male, evaluated at a tertiary referral center in China. Key exclusions: prior septal reduction therapy, inadequate CMR quality, significant valvular heart disease, secondary hypertrophy, and life expectancy <1 year.

Intervention

Comprehensive risk prediction model integrating cardiac magnetic resonance (CMR) parameters (ejection fraction, left ventricular diastolic pressure) with clinical and biomarker variables

Comparator

Traditional HCM Risk-SCD score alone, clinical model (traditional + clinical variables), and CMR model (traditional + CMR parameters)

Outcome

Composite of sudden cardiac death or appropriate implantable cardioverter-defibrillator therapy for ventricular arrhythmiascomposite

Integrating CMR parameters with clinical variables significantly improves sudden cardiac death risk prediction in hypertrophic cardiomyopathy compared to the traditional guideline-recommended HCM Risk-SCD score.

Limitations

Retrospective design may introduce selection bias and unmeasured confounding.
External validation in independent cohorts is necessary.
Single-center design may limit generalizability to community-based populations.

Abstract

Background Current risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) relies primarily on the HCM Risk-SCD score, but its predictive accuracy remains suboptimal. Objectives This study aimed to develop and validate a comprehensive risk prediction model integrating cardiac magnetic resonance (CMR) parameters with clinical and biomarker variables. Methods We analyzed 576 consecutive HCM patients from a tertiary referral center. The primary endpoint was sudden death or appropriate implantable cardioverter-defibrillator therapy. We developed four prediction models: (1) Traditional (HCM Risk-SCD alone), (2) Clinical (traditional + clinical variables), (3) CMR (traditional + CMR parameters), and (4) Comprehensive (integrating all variables). Model performance was assessed using C-index and time-dependent receiver operating characteristic (ROC) analysis. Results During median follow-up of 3 years, 58 patients (10.1%) experienced the primary endpoint. The comprehensive model demonstrated superior performance (C-index 0.607) compared to traditional (0.565), clinical (0.598), and CMR (0.607) models. In multivariable analysis, CMR ejection fraction (HR: 0.94, 95% CI: 0.91–0.97, P 0.001) and left ventricular diastolic pressure (HR: 0.94, 95% CI: 0.89–0.98, P = 0.010) were independent predictors. Time-dependent ROC analysis showed maintained predictive accuracy over 3 years (AUC 0.78–0.85). Risk stratification using the comprehensive model effectively discriminated low, intermediate, and high-risk groups (log-rank P 0.001). Conclusions Integration of CMR parameters with clinical variables significantly improves SCD risk prediction in HCM compared to traditional risk stratification. The comprehensive model provides enhanced accuracy for identifying high-risk patients who may benefit from primary prevention implantable cardioverter-defibrillator therapy.

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