Why the study?
The study aimed to investigate the risk of detrimental cardiovascular events, including malignant ventricular arrhythmias, new-onset HFrEF, and long-term mortality, in an Asian population of ARVC/D patients.
Cohort (n=109)
Yes
Hazard Ratio: 1.74 (95% CI 1.3–2.33)
p-value: p=<0.001
Clinical and electrocardiographic parameters, particularly QRS and QTc duration, can be combined with machine learning models to effectively predict incident VT/VF, new-onset HFrEF, and mortality in ARVC/D patients.
Background Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterized by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD). The present study aims to investigate the risk of detrimental cardiovascular events in an Asian population of ARVC/D patients, including the incidence of malignant ventricular arrhythmias, new-onset heart failure with reduced ejection fraction (HFrEF), as well as long-term mortality. Methods and Results This was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019 in Hong Kong. This study consisted of 109 ARVC/D patients (median age: 61 [46-71] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariable Cox regression identified prolonged QRS duration as a predictor of VT/VF (p < 0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (P < 0.05. The presence of epsilon waves, in addition to the parameters of prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p<0.05). Clinical scores were developed to predict incident VT/VF, new-onset HFrEF and all-cause mortality, and all were significantly improved by machine learning techniques. Conclusion Clinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should in turn be used in tandem to aid risk stratification in the hospital setting.
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Lakhani et al. (2021) conducted a cohort in Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) (n=109). Clinical risk score (QRS duration > 98.5ms, syncope, epsilon waves) vs. Lower clinical risk score was evaluated on Incident ventricular tachycardia/fibrillation (VT/VF) (HR 1.74, 95% CI 1.30-2.33, p=<0.001).
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