Integrating ECG Into the Mayo Score Enhances Genotype Prediction in Hypertrophic Cardiomyopathy

Background In patients with hypertrophic cardiomyopathy (HCM), genetic testing is crucial for cascade screening and risk stratification. However, it remains limited by financial and logistical constraints, necessitating prioritization. The Mayo HCM Genotype Predictor Score, based on clinical and echocardiographic variables, estimates genotype positivity with acceptable performance. Although sarcomeric variants are also associated with electrophysiological abnormalities, ECG parameters were not incorporated into this model. This study aimed to enhance genotype prediction in HCM by integrating ECG parameters. Methods We retrospectively analyzed 466 patients with HCM from a Japanese multicenter cohort. Genotype positivity was defined as harboring pathogenic/likely pathogenic variants in sarcomere‐encoding genes. Candidate ECG variables were selected via multivariable logistic regression with bootstrap aggregation. A point‐based novel score was developed and internally validated using cross‐validation. Model performance was assessed by the area under the receiver operating characteristic curve and Akaike’s information criterion. Results Genotype‐positive patients (30.3%) more frequently exhibited atrial fibrillation, intraventricular conduction disturbance, lower prevalence of high voltage, and abnormal T‐wave inversion in precordial leads than genotype‐negative patients and thus were incorporated into the novel Mayo‐ECG score. This score stratified genotype positivity from 7.1% (score ≤−1) to 91.4% (score ≥4), and its discriminative performance (area under the receiver operating characteristic curve, 0.81 [95% CI, 0.77–0.85]) outperformed the Mayo score (area under the receiver operating characteristic curve, 0.76 [95% CI, 0.71–0.81]; P =0.005) with better overall model fit (Akaike’s information criterion: 439 versus 479). Internal validation yielded consistent results with good calibration. Conclusions The Mayo‐ECG improves genotype prediction, outperforming the conventional model. Given its simplicity, this model has the potential to prioritize genetic testing in HCM.