Retracted and Republished: A new prediction model for ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy
Key result
Novel ARVC risk model accurately predicts VA and reduces ICD placements by ~21%.
- C-index 0.77
- 95% CI 0.73-0.81
- P<0.001
- n=528
Why the study?
Arrhythmogenic right ventricular cardiomyopathy is characterized by ventricular arrhythmias and sudden cardiac death, requiring a model for individualized prediction of incident events.
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?
Comparison
New prediction model vs current consensus-based ICD placement algorithm
Design
Multicenter observational cohort study
Follow-up
4.83 (interquartile range 2.44-9.33) years
Authors
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.
“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.”
“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.”
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
Structured PICO
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?
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
A new prediction model estimating annual VA risk using age, sex, cardiac syncope in prior 6 months, non-sustained ventricular tachycardia, number of premature ventricular complexes in 24 h, number of leads with T-wave inversion, and right ventricular ejection fraction.
C
Comparator
Current consensus-based ICD placement algorithm.
O
Outcome
Incident sustained VA (defined as SCD, aborted SCD, sustained ventricular tachycardia, or appropriate implantable cardioverter-defibrillator (ICD) therapy).composite
Main Result
Effect estimate: C-index 0.77 (95% CI 0.73-0.81)
p-value: p=<0.001
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.
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).