A pragmatic clinical risk score for adverse outcomes in hypertrophic cardiomyopathy using routine laboratory and bedside variables

BACKGROUND: Early risk prediction in hypertrophic cardiomyopathy (HCM) remains challenging, particularly for short-term adverse outcomes. We aimed to develop a pragmatic risk score for predicting 1-year composite adverse outcomes using routinely available clinical, laboratory, arrhythmic, and echocardiographic variables. METHODS: This single-center retrospective cohort study included 1,200 Chinese adults with HCM evaluated between 2015 and 2024. The primary endpoint was a 1-year composite of sudden cardiac death (SCD) or SCD-equivalent events, heart failure hospitalization, or all-cause mortality. Candidate predictors were selected based on clinical relevance, routine availability, and prior HCM risk evidence, followed by univariate screening and multivariable logistic regression with stepwise selection. A point-based score was developed and internally validated using 1,000 bootstrap resamples and stratified 5-fold cross-validation. RESULTS: During follow-up, 120 patients experienced the composite endpoint, including heart failure hospitalization in 78 patients, SCD or SCD-equivalent events in 24, and all-cause mortality in 18. Ten predictors were included in the HCM Pragmatic Risk Score: age ≥ 60 years, nonsustained ventricular tachycardia, unexplained syncope, maximal left ventricular wall thickness ≥ 20 mm, left atrial diameter ≥ 45 mm, left ventricular outflow tract gradient ≥ 30 mmHg, left ventricular ejection fraction < 50%, NT-proBNP > 1,000 pg/mL, elevated troponin, and creatinine > 1.2 mg/dL. The score showed moderate discrimination (AUC = 0.75) and good calibration. Event rates increased across risk strata: 3.8%, 16.7%, and 77.8%. Bootstrap validation yielded an optimism-corrected AUC of 0.74. CONCLUSION: The HCM Pragmatic Risk Score may assist short-term risk stratification and follow-up planning. Because the endpoint was mainly driven by heart failure hospitalization, it should not be interpreted as a dedicated SCD prediction model or stand-alone ICD decision tool. External validation is required.