HCMR (NHLBI HCM Registry) prospectively enrolled 2,750 patients with HCM at 44 expert sites across North America and Europe; all underwent contrast CMR, biomarker/genotyping bloodwork, and structured history. Median follow-up >7 years. An integrated multidimensional model (combining clinical, imaging, genetic and biomarker data via elastic-net selection) outperforms current SCD-only risk prediction, capturing patients at risk of HF events and transplant that are under-recognized today. Likely reduces both unnecessary ICDs (overtreatment) and avoidable deaths (undertreatment). Important context for HCM clinics now selecting candidates for mavacamten/aficamten therapy.
To combine prospectively collected clinical history, imaging, genetic, and biomarker data to improve risk prediction of adverse events in hypertrophic cardiomyopathy. Design, Setting, and Participants: A total of 2,750 patients with hypertrophic cardiomyopathy were prospectively enrolled in the registry-based study from 44 sites in North America and Europe with expertise in hypertrophic cardiomyopathy and cardiac magnetic resonance (CMR) imaging. Participants were enrolled from April 1, 2014, to April 7, 2017. Mean follow-up exceeded 7 years. Exposures: Patients underwent a health history questionnaire, blood sampling for biomarkers and genotyping, and contrast-enhanced CMR. Main Outcomes and Measures: The predefined composite adjudicated primary end point was time to first event for hypertrophic cardiomyopathy–related deaths; nonfatal sustained ventricular arrhythmias requiring cardioversion or defibrillation; and left ventricular assist device implant or heart transplant. A secondary end point was a composite of sudden cardiac death and nonfatal VA events. The elastic-net method identified the most important predictors. Cox proportional hazards regression assessed associations with time to the first end point.
Of the 2,750 prospectively enrolled patients, 2,698 (98%) had analyzable data; 1,919 (71%) were male, mean age 50 years. The integrated multidimensional model outperformed traditional risk prediction restricted to sudden cardiac death, identifying patients at risk for HF events, heart transplant, and ventricular arrhythmias that were previously under-recognized.
A multidimensional risk prediction model combining clinical, imaging, genetic and biomarker data provides more comprehensive risk stratification in hypertrophic cardiomyopathy than the current SCD-focused frameworks.
