Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
Dept. ESAII, CREB, Universitat Politècnica de Catalunya, Barcelona, Spain.
PLoS One. 2018 May 15;13(5):e0197367. doi: 10.1371/journal.pone.0197367. eCollection 2018.
Ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest and especially during sleep, suggesting that changes in the autonomic modulation may play an important role in arrhythmogenesis. The autonomic response to exercise and subsequent recovery was evaluated on 105 patients diagnosed with BS (twenty-four were symptomatic), by means of a time-frequency heart rate variability (HRV) analysis, so as to propose a novel predictive model capable of distinguishing symptomatic and asymptomatic BS populations. During incremental exercise, symptomatic patients showed higher HFnu values, probably related to an increased parasympathetic modulation, with respect to asymptomatic subjects. In addition, those extracted HRV features best distinguishing between populations were selected using a two-step feature selection approach, so as to build a linear discriminant analysis (LDA) classifier. The final features subset included one third of the total amount of extracted autonomic markers, mostly acquired during incremental exercise and active recovery, thus evidencing the relevance of these test segments in BS patients classification. The derived predictive model showed an improved performance with respect to previous works in the field (AUC = 0.92 ± 0.01; Se = 0.91 ± 0.06; Sp = 0.90 ± 0.05). Therefore, based on these findings, some of the analyzed HRV markers and the proposed model could be useful for risk stratification in Brugada syndrome.
Brugada 综合征(BS)中的室性心律失常通常在休息时发生,尤其是在睡眠期间,这表明自主神经调节的变化可能在心律失常发生中起重要作用。通过时频心率变异性(HRV)分析评估了 105 名诊断为 BS(24 名有症状)的患者的运动和随后恢复期间的自主神经反应,以便提出一种新的预测模型,能够区分有症状和无症状的 BS 人群。在递增运动中,有症状的患者表现出更高的 HFnu 值,这可能与迷走神经调节增加有关,而无症状的患者则不同。此外,使用两步特征选择方法选择了最佳区分人群的 HRV 特征,以便构建线性判别分析(LDA)分类器。最终特征子集包括提取的自主神经标记物总量的三分之一,这些标记物主要在递增运动和主动恢复期间获得,从而证明了这些测试片段在 BS 患者分类中的相关性。与该领域的先前工作相比,所得到的预测模型表现出更好的性能(AUC=0.92±0.01;Se=0.91±0.06;Sp=0.90±0.05)。因此,基于这些发现,一些分析的 HRV 标记物和提出的模型可能有助于 Brugada 综合征的风险分层。