Importance of genotype for risk stratification in arrhythmogenic right ventricular cardiomyopathy using the 2019 ARVC

Alexandros Protonotarios1,2, Riccardo Bariani3, Chiara Cappelletto4,5

  • 1Institute of Cardiovascular Science, University College London, London, UK.

Insights

The 2019 arrhythmogenic right ventricular cardiomyopathy (ARVC) risk model performs well for gene-positive patients, especially those with PKP2 mutations. However, its accuracy is limited in gene-elusive ARVC cases, suggesting genotype inclusion in future models.

Area of Science:

  • Cardiology
  • Genetics
  • Medical Diagnostics

Background:

  • Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable heart muscle disease.
  • Accurate risk stratification is crucial for managing ARVC patients and preventing sudden cardiac death.
  • Current risk models may not fully account for genetic heterogeneity in ARVC.

Purpose of the Study:

  • To evaluate the performance of the 2019 ARVC risk model across different genetic subtypes of the disease.
  • To determine if patient genotype influences the model's accuracy in predicting ventricular arrhythmia (VA).

Main Methods:

  • A cohort of 554 ARVC patients without prior sustained ventricular arrhythmia (VA) was analyzed.
  • The 2019 ARVC risk model's discriminative and calibration abilities were assessed.
  • Performance was compared across four genotype groups: PKP2, desmoplakin (DSP), other desmosomal genes, and gene-elusive patients.

Main Results:

  • The 2019 ARVC risk model demonstrated reasonable discriminative ability but tended to overestimate risk.
  • Model performance varied significantly by genotype, with highest discrimination in the PKP2 group and lowest in the gene-elusive group.
  • Clinical risk markers like ventricular dimensions and function showed genotype-dependent significance.

Conclusions:

  • The 2019 ARVC risk model is reasonably effective for gene-positive ARVC, particularly PKP2-related cases.
  • The model's utility is limited in gene-elusive ARVC patients.
  • Incorporating genetic information into future ARVC risk models is recommended for improved accuracy.
Abstract

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