A new prediction model for sustained ventricular tachycardia in arrhythmogenic cardiomyopathy

Background: Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterized by high risks of sustained ventricular tachycardia (sVT) and sudden cardiac death. Identifying patients with high risk of sVT is crucial for the management of ACM.

Methods: A total of 147 ACM patients were retrospectively enrolled in the observational study and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify factors associated with sVT. Subsequently, a nomogram was constructed based on multivariable logistic regression analysis. The performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis was conducted to assess the clinical utility of the nomogram.

Results: Seven parameters were incorporated into the nomogram: age, male sex, syncope, heart failure, T wave inversion in precordial leads, left ventricular ejection fraction (LVEF), SDNN level. The AUC of the nomogram to predict the probability of sVT was 0.867 (95% CI, 0.797-0.938) in the training group and 0.815 (95% CI, 0.673-0.958) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. Decision curve analysis indicated that the nomogram had higher overall net benefits in predicting sVT.

Conclusion: We have developed and internally validated a new prediction model for sVT in ACM. This model could serve as a valuable tool to accurately identify ACM patients with high risk of sVT.