A new prediction model for ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy

Abstract

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 20.3% 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).

Integrating Exercise Into Personalized Ventricular Arrhythmia Risk Prediction in Arrhythmogenic Right Ventricular Cardiomyopathy

“…In total, 7% of data were missing and as described previously assumed to be at random and imputed using multiple imputation by chained equations. 8 The Cox model linearity assumption of continuous variables was evaluated using martingale residuals. Possible interactions of exercise with sex, age, and genetic variants were tested.…”

“…Patients with ARVC with a substantial exercise participation history (ie, athletes) are at higher risk of sustained VA. 6,7,10 But the risk calculator for incident sustained VA (arvcrisk.com) does not specifically include an exercise metric. 8 This possible limitation has raised concerns about the validity of its predictions in athletes. 9 Reassuringly, Gasperetti et al 14 recently validated the risk calculator in a cohort of 25 Italian athletes (defined as those practicing >6 hours per week of >6 MET activities during the past 3 years) showing the risk calculator predictions to be accurate.…”

“…We recently developed a risk calculator for incident sustained VA in patients newly diagnosed with ARVC without prior sustained VA (arvcrisk.com). 8 By design, this did not include the quantity of exercise exposure in generating risk estimates, as the aim was to develop a prediction model using risk factors that were consistently collected in participating centers and readily available in clinical practice. Nonetheless, not integrating exercise raises valid concerns about the accuracy of predictions in athletes.…”

Arrhythmogenic Right Ventricular Cardiomyopathy Prevalence and Arrhythmic Outcomes in At-Risk Family Members: A Systematic Review and Meta-Analysis

“…Arrhythmia risk stratification for at-risk family members of patients with ARVC is a challenging scenario for clinicians given the possibility of sudden cardiac death as the presenting symptom and the possibility of implantable cardiac defibrillator implantation for primary prevention in high-risk patients. A recent systematic review and meta-analysis of observational studies in patients with ARVC identified risk factors for VA in diagnosed patients with ARVC, 56 which was successfully used to inform development of a model to predict personalized risk of incident sustained VA. 57 Although recent guidelines recommend the use of device therapy based on overall attributable risk in an individual, similar strategies to predict ARVC onset and risk of life-threatening arrhythmias in at-risk relatives have the potential to promote individualized management of this unique group of individuals.…”

A Territory-Wide Study of Arrhythmogenic Right Ventricular Cardiomyopathy Patients from Hong Kong

“…Such an approach has been adopted previously in a large cohort of 528 ARVC/D patients to predict the long-term risk of ventricular arrhythmias. The model constructed, which included age, male gender, cardiac syncope in the prior 6 months, prior non-sustained VT, number of PVCs in 24 h, number of leads with TWI and right ventricular ejection fraction, demonstrated an improved ability to estimate risk of ventricular arrhythmias and guide decision-making in ICD implantation for such patients [41]. A meta-analysis identified the following 11 variables as the most important factor for predicting arrhythmic events: (1) male gender, (2) presyncope, (3) left ventricular dysfunction, (4) TWI in inferior leads, (5) proband status, (6) late potentials, (7) syncope, (8) inducibility at electrophysiological study, (9) right ventricular dysfunction, (10) epsilon waves, and (11) premature ventricular contractions greater than 1000/24 h [42].…”