Validation of an Arrhythmogenic Right Ventricular Cardiomyopathy Risk-Prediction Model in a Chinese Cohort

J Clin Med. 2022 Apr 1;11(7):1973. doi: 10.3390/jcm11071973.

Authors

Nixiao Zhang^{1

2}, Chuangshi Wang³, Alessio Gasperetti⁴, Yanyan Song⁵, Hongxia Niu², Min Gu², Firat Duru⁴, Liang Chen⁶, Shu Zhang², Wei Hua²

Affiliations

¹ Department of Cardiology, Cardiovascular Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
² Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
³ Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102300, China.
⁴ Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, 8091 Zurich, Switzerland.
⁵ Department of CMR, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
⁶ Department of Cardiac Surgery, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.

Abstract

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.

Keywords: arrhythmogenic cardiomyopathy; implantable cardioverter-defibrillator; primary prevention; recalibration; secondary prevention; ventricular arrhythmias.

Grants and funding

82100377/National Natural Science Foundation of China