A novel prediction model for ventricular arrhythmias in ARVC accurately distinguished patients with and without events (C-index 0.77; 95% CI 0.73-0.81) and reduced ICD placements by 20.6% (P<0.001).
Cohort (n=528)
Yes
Does a new clinical prediction model accurately predict incident ventricular arrhythmias and improve ICD placement decisions compared to current consensus algorithms in patients with ARVC?
A new prediction model using readily available clinical parameters accurately estimates ventricular arrhythmia risk in ARVC patients and may reduce unnecessary primary prevention ICD placements by 20.6% compared to current consensus algorithms.
Effect estimate: C-index 0.77 (95% CI 0.73-0.81)
p-value: p=<0.001
AIMS: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC) is characterized by ventricular arrhythmias (VAs) and sudden cardiac death (SCD). We aimed to develop a model for individualized prediction of incident VA/SCD in ARVC patients. METHODS AND RESULTS: Five hundred and twenty-eight patients with a definite diagnosis and no history of sustained VAs/SCD at baseline, aged 38.2 ± 15.5 years, 44.7% male, were enrolled from five registries in North America and Europe. Over 4.83 (interquartile range 2.44-9.33) years of follow-up, 146 (27.7%) experienced sustained VA, defined as SCD, aborted SCD, sustained ventricular tachycardia, or appropriate implantable cardioverter-defibrillator (ICD) therapy. A prediction model estimating annual VA risk was developed using Cox regression with internal validation. Eight potential predictors were pre-specified: age, sex, cardiac syncope in the prior 6 months, non-sustained ventricular tachycardia, number of premature ventricular complexes in 24 h, number of leads with T-wave inversion, and right and left ventricular ejection fractions (LVEFs). All except LVEF were retained in the final model. The model accurately distinguished patients with and without events, with an optimism-corrected C-index of 0.77 95% confidence interval (CI) 0.73-0.81 and minimal over-optimism calibration slope of 0.93 (95% CI 0.92-0.95). By decision curve analysis, the clinical benefit of the model was superior to a current consensus-based ICD placement algorithm with a 20.6% reduction of ICD placements with the same proportion of protected patients (P < 0.001). CONCLUSION: Using the largest cohort of patients with ARVC and no prior VA, a prediction model using readily available clinical parameters was devised to estimate VA risk and guide decisions regarding primary prevention ICDs (www.arvcrisk.com).
“The ARVCrisk calculator can be used as a guide for making relevant clinical decisions, such as the implantation of a cardioverter-defibrillator (ICD) for primary prevention in patients with ARVC.”
Cadrin‐Tourigny et al. (2019) conducted a cohort in Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC) (n=528). Prediction model for ventricular arrhythmias vs. Current consensus-based ICD placement algorithm was evaluated on Sustained VA (SCD, aborted SCD, sustained ventricular tachycardia, or appropriate ICD therapy) (C-index 0.77, 95% CI 0.73-0.81, p=<0.001). A novel prediction model for ventricular arrhythmias in ARVC accurately distinguished patients with and without events (C-index 0.77; 95% CI 0.73-0.81) and reduced ICD placements by 20.6% (P<0.001).
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