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Validation of an Arrhythmogenic Right Ventricular Cardiomyopathy Risk-Prediction Model in a Chinese Cohort
Nixiao Zhang1,2, Chuangshi Wang3, Alessio Gasperetti4
1Department of Cardiology, Cardiovascular Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Insights
A new risk model accurately predicts ventricular arrhythmias in Asian arrhythmogenic right ventricular cardiomyopathy patients for primary prevention. Recalibration improved its performance for secondary prevention cases.
Area of Science:
- Cardiology
- Genetics
- Medical Technology
Background:
- Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic heart condition.
- Ventricular arrhythmias (VAs) are a major complication of ARVC.
- Existing risk prediction models like ITFC and HRS criteria have limitations.
Purpose of the Study:
- To externally validate a novel VA risk-prediction model in a Chinese ARVC population.
- To assess the model's effectiveness in predicting ventricular events compared to previous criteria.
- To evaluate the model's performance in both primary and secondary prevention cohorts.
Main Methods:
- Enrolled 88 ARVC patients who received implantable cardioverter-defibrillators (ICDs) between 2005 and 2020.
- Calculated a priori predicted VA risk using the novel model.
- Compared predicted risk with observed rates of appropriate ICD therapies over a median follow-up of 3.9 years.
Main Results:
- The novel model showed good predictive accuracy (C-statistic 0.833) in primary prevention patients.
- In secondary prevention patients, the model initially underestimated risk (C-statistic 0.640).
- Recalibration significantly improved the model's performance in secondary prevention patients (mean predicted-observed risk -0.04).
Conclusions:
- The novel risk-prediction model is suitable for predicting arrhythmic risk in Asian ARVC patients undergoing primary prevention.
- The model requires recalibration for accurate risk prediction in secondary prevention ARVC patients.
- This validation supports the model's utility in diverse ARVC populations.
Background:
The novel arrhythmogenic right ventricular cardiomyopathy (ARVC)-associated ventricular arrhythmias (VAs) risk-prediction model endorsed by Cadrin-Tourigny et al. was recently developed to estimate visual VA risk and was identified to be more effective for predicting ventricular events than the International Task Force Consensus (ITFC) criteria, and the Heart Rhythm Society (HRS) criteria. Data regarding its application in Asians are lacking.
Objectives:
We aimed to perform an external validation of this algorithm in the Chinese ARVC population.
Methods:
The study enrolled 88 ARVC patients who received implantable cardioverter-defibrillator (ICD) from January 2005 to January 2020. The primary endpoint was appropriate ICD therapies. The novel prediction model was used to calculate a priori predicted VA risk that was compared with the observed rates.
Results:
During a median follow-up of 3.9 years, 57 (64.8%) patients received the ICD therapy. Patients with implanted ICDs for primary prevention had non-significantly lower rates of ICD therapy than secondary prevention (5-year event rate: 0.46 (0.13-0.66) and 0.80 (0.64-0.89); log-rank p = 0.098). The validation study revealed the C-statistic of 0.833 (95% confidence interval (CI) 0.615-1.000), and the predicted and the observed patterns were similar in primary prevention patients (mean predicted-observed risk: -0.07 (95% CI -0.21, 0.09)). However, in secondary prevention patients, the C-statistic was 0.640 (95% CI 0.510-0.770) and the predicted risk was significantly underestimated (mean predicted-observed risk: -0.32 (95% CI -0.39, -0.24)). The recalibration analysis showed that the performance of the prediction model in secondary prevention patients was improved, with the mean predicted-observed risk of -0.04 (95% CI -0.10, 0.03).
Conclusions:
The novel risk-prediction model had a good fitness to predict arrhythmic risk in Asian ARVC patients for primary prevention, and for secondary prevention patients after recalibration of the baseline risk.
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