Zhang Baowei, Xie Xin, Yu Jinbo, Wu Yizhang, Zhou Jian, Li Xiaorong, Yang Bing
Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Cardiology, Ji'an Center People's Hospital, Ji'an, China.
Front Cardiovasc Med. 2024 Dec 16;11:1477931. doi: 10.3389/fcvm.2024.1477931. eCollection 2024.
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.
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.
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 patients with high risk of sVT.
致心律失常性心肌病（ACM）是一种遗传性心肌病，其特征是持续性室性心动过速（sVT）和心源性猝死风险较高。识别sVT高风险患者对于ACM的管理至关重要。
共有147例ACM患者被回顾性纳入观察性研究，并分为训练组和验证组。采用最小绝对收缩和选择算子（LASSO）回归模型识别与sVT相关的因素。随后，基于多变量逻辑回归分析构建列线图。使用受试者操作特征（ROC）曲线的曲线下面积（AUC）和校准曲线评估列线图的性能。进行决策曲线分析以评估列线图的临床实用性。
七个参数被纳入列线图：年龄、男性、晕厥、心力衰竭、胸前导联T波倒置、左心室射血分数（LVEF）、SDNN水平。训练组中列线图预测sVT概率的AUC为0.867（95%CI，0.797 - 0.938），验证组中为0.815（95%CI，0.673 - 0.958）。校准曲线显示实际临床结果与预测结果之间具有良好的一致性。决策曲线分析表明列线图在预测sVT方面具有更高的总体净效益。
我们开发并内部验证了一种用于ACM中sVT的新预测模型。该模型可作为准确识别sVT高风险ACM患者的有价值工具。
