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June 13, 2026European Heart JournalOpen Access

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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Key result

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).

Authors

JCJulia Cadrin‐TourignyLBLaurens P. BosmanANAnna Nozza

Discussion

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Member takes

Key expert perspectives

Captured external expert commentary on this paper, strongest first. Original sources are linked where available.

ADAndrea Di MarcoPostdoctoral researcher, IDIBELL cardiovascular diseases group

“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.”

IDIBELL / Bellvitge University HospitalNews Coverage
CJCynthia JamesGenetic counselor, Johns Hopkins University

“There is general agreement that most ARVC patients diagnosed following an arrhythmic event benefit from a secondary prevention ICD. However, appropriate patient selection for primary prevention ICDs is challenging.”

Johns Hopkins UniversityNews Coverage

Overview

May reduce unnecessary ICDs versus consensus algorithms in ARVC; hypothesis-generating and requires prospective validation.

Key Points

  • The aim is to develop a personalized prediction model for ventricular arrhythmias and sudden cardiac death in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC).
  • Enrolled 528 ARVC patients with no prior history of VAs or SCD over an average follow-up of 4.83 years.
  • Developed a prediction model using Cox regression based on eight potential clinical predictors.
  • Calculated the model's performance with a C-index of 0.77 and conducted decision curve analysis.
  • 27.7% of patients experienced sustained ventricular arrhythmias during follow-up.
  • The model showed a superior clinical benefit over the current ICD placement algorithm, reducing unnecessary placements by 20.6% (P < 0.001).
  • The model incorporated all predictors except left ventricular ejection fraction, demonstrating accurate risk estimation.

Study Design

Type

Cohort (n=528)

Multicenter

Yes

PICO

P
Population
528 patients with a definite diagnosis of ARVC and no history of sustained VAs/SCD at baseline, followed for a median of 4.83 years.
E
Exposure / Comparator
Prediction model for ventricular arrhythmias vs Current consensus-based ICD placement algorithm
O
Primary Outcome
Sustained VA (SCD, aborted SCD, sustained ventricular tachycardia, or appropriate ICD therapy) — C-index 0.77 (0.73-0.81), p=<0.001

Main Result

Effect estimate: C-index 0.77 (95% CI 0.73-0.81)

p-value: p=<0.001

Cite This Study

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).

synapsesocial.com/papers/6a2ccc22b3cfc6b60e4443eehttps://doi.org/10.1093/eurheartj/ehz103
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