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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
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.
Aims:
To study the impact of genotype on the performance of the 2019 risk model for arrhythmogenic right ventricular cardiomyopathy (ARVC).
Methods And Results:
The study cohort comprised 554 patients with a definite diagnosis of ARVC and no history of sustained ventricular arrhythmia (VA). During a median follow-up of 6.0 (3.1,12.5) years, 100 patients (18%) experienced the primary VA outcome (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted sudden cardiac arrest, or sudden cardiac death) corresponding to an annual event rate of 2.6% [95% confidence interval (CI) 1.9-3.3]. Risk estimates for VA using the 2019 ARVC risk model showed reasonable discriminative ability but with overestimation of risk. The ARVC risk model was compared in four gene groups: PKP2 (n = 118, 21%); desmoplakin (DSP) (n = 79, 14%); other desmosomal (n = 59, 11%); and gene elusive (n = 160, 29%). Discrimination and calibration were highest for PKP2 and lowest for the gene-elusive group. Univariable analyses revealed the variable performance of individual clinical risk markers in the different gene groups, e.g. right ventricular dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true for left ventricular systolic function.
Conclusion:
The 2019 ARVC risk model performs reasonably well in gene-positive ARVC (particularly for PKP2) but is more limited in gene-elusive patients. Genotype should be included in future risk models for ARVC.
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