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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. General Cardiovascular Medicine
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1477931
This article is part of the Research Topic Artificial Intelligence for Arrhythmia Detection and Prediction View all 3 articles

A new prediction model for sustained ventricular tachycardia in arrhythmogenic cardiomyopathy

Provisionally accepted
Baowei Zhang Baowei Zhang *Xin Xie Xin Xie Jinbo Yu Jinbo Yu Yizhang Wu Yizhang Wu Jian Zhou Jian Zhou Xiaorong Li Xiaorong Li Bing Yang Bing Yang
  • Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

The final, formatted version of the article will be published soon.

    Background: Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterized by high risks of sustained ventricular tachycardia (sVT) and sudden cardiac death. Identifying patients with high risk of sVT is crucial for the management of ACM.: A total of 147 ACM patients were retrospectively enrolled in the observational study and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify factors associated with sVT. Subsequently, a nomogram was constructed based on multivariable logistic regression analysis. The performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis was conducted to assess the clinical utility of the nomogram. Results: Seven parameters were incorporated into the nomogram: age, male sex, syncope, heart failure, T wave inversion in precordial leads, left ventricular ejection fraction (LVEF), SDNN level. The AUC of the nomogram to predict the probability of sVT was 0.867 (95% CI, 0.797-0.938) in the training group and 0.815 (95% CI, 0.673-0.958) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. Decision curve analysis indicated that the nomogram had higher overall net benefits in predicting sVT. Conclusion: We have developed and internally validated a new prediction model for sVT in ACM. This model could serve as a valuable tool to accurately identify ACM 4 patients with high risk of sVT.

    Keywords: arrhythmogenic cardiomyopathy, Sustained ventricular tachycardia, Sudden cardiac death, Prediction model, nomogram

    Received: 08 Aug 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Zhang, Xie, Yu, Wu, Zhou, Li and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Baowei Zhang, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.