Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers-reaching the frontiers of individual risk prediction

Aims This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. Methods and results Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27-55]. During a median follow-up of 4.3 years (IQR 1.7-7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability fC-statistic 0.83 [95% confidence interval (CI) 0.78-0.88]g. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61-0.75). Conclusion This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach.

Automated summary

This study aimed to develop a new mutation-specific prediction model for malignant VA in PLN p.Arg14del mutation carriers to improve risk stratification for primary prevention ICD implantation. The model outperformed the existing PLN risk model, indicating that using mutation-specific phenotypic features for risk prediction can enhance accuracy.