Prediction of Sinus Rhythm Maintenance After Electrical Cardioversion Using Spectral and Vector Cardiographic ECG Analysis

IF 1.1 4区医学 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS

Annals of Noninvasive Electrocardiology Pub Date : 2025-08-15 DOI:10.1111/anec.70105

Sabri Hassouna, Marek Hozman, Dalibor Heřman, Jana Veselá, Věra Filipcová, Filip Plesinger, Zbyněk Bureš, Pavel Osmančík

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引用次数: 0

Abstract

Introduction

Electrical cardioversion (ECV) remains a treatment option for atrial fibrillation (AF). The study aimed to find predictors of SR maintenance after ECV using spectral and vector cardiographic (VCG) analysis of ECGs.

Methods

Consecutive patients with AF referred for elective ECV were prospectively enrolled. A digital ECG recording was obtained before the ECV and was analyzed using spectral and VCG analysis. AF activity was analyzed using spectral analysis to determine the dominant frequency (DF), RI (regularity index), and OI (organizational index). QRS complexes were analyzed using vectorcardiography to determine the dXmean, dYmean, and dZmean (derivation of VCG signals). We used Lasso Logistic Regression (LLR) in five-fold cross-validation for feature selection and to build combined predictive models of SR maintenance. For model training and evaluation, data were split in a 60%–40% ratio for training and testing, respectively.

Results

A total of 80 patients were enrolled (age 70.2 ± 10.6 years, 49 (61%) were men, BMI 29.7 kg/m²). At the 3-month follow-up, AF recurrence was present in 36 patients (45%). The best single VCG parameter to predict SR maintenance was dZMean (OR 0.18, 95% CI 0.06–0.51, p < 0.001). VCG-domain parameters combined into the LLR model showed an area under the curve (AUC) of 0.78. From the spectral analysis domain, the best predictor was DF (OR 3.54, 95% CI 1.28–10.25), p = 0.006; spectral features led to an AUC of 0.76 when combined in the LLR model. Clinical features did not form a model since no features passed feature selection. Combining VCG and spectral analysis features led to an LLR model with an AUC of 0.79.

Conclusion

The combination of spectral analysis of AF activity and VCG analysis of ventricular activity provided more accurate predictive information than either analysis alone.

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利用频谱和矢量心电图分析预测电转复后窦性心律维持

电复律（ECV）仍然是房颤（AF）的一种治疗选择。本研究旨在通过心电图的频谱和矢量心动图（VCG）分析，寻找ECV后SR维持的预测因素。方法前瞻性纳入连续房颤患者择期ECV。在ECV前获得数字心电记录，并使用频谱和VCG分析进行分析。使用频谱分析分析AF活动，以确定主导频率（DF）， RI（规律性指数）和OI（组织指数）。采用矢量心动图分析QRS复合体，确定dXmean、dYmean和dZmean （VCG信号的推导）。我们使用Lasso Logistic回归（LLR）进行五重交叉验证进行特征选择，并建立了SR维护的组合预测模型。对于模型训练和评估，数据按60%-40%的比例分别进行训练和测试。结果共纳入80例患者（年龄70.2±10.6岁，男性49例（61%），BMI 29.7 kg/m2）。在3个月的随访中，36例（45%）患者出现房颤复发。预测SR维持的最佳单一VCG参数是dZMean （OR 0.18, 95% CI 0.06-0.51, p < 0.001）。将vcg域参数合并到LLR模型中，曲线下面积（AUC）为0.78。从光谱分析领域来看，最佳预测因子是DF (OR 3.54, 95% CI 1.28-10.25), p = 0.006；在LLR模型中结合光谱特征时，AUC为0.76。临床特征没有形成模型，因为没有特征通过特征选择。结合VCG和光谱分析特征，得到了AUC为0.79的LLR模型。结论房颤活动谱分析与心室活动VCG分析相结合的预测信息比单独分析更准确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Noninvasive Electrocardiology 医学-心血管系统

CiteScore

3.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The ANNALS OF NONINVASIVE ELECTROCARDIOLOGY (A.N.E) is an online only journal that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients. ANE is the first journal in an evolving subspecialty that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients. The publication includes topics related to 12-lead, exercise and high-resolution electrocardiography, arrhythmias, ischemia, repolarization phenomena, heart rate variability, circadian rhythms, bioengineering technology, signal-averaged ECGs, T-wave alternans and automatic external defibrillation. ANE publishes peer-reviewed articles of interest to clinicians and researchers in the field of noninvasive electrocardiology. Original research, clinical studies, state-of-the-art reviews, case reports, technical notes, and letters to the editors will be published to meet future demands in this field.