University of Rennes, CHU Rennes, Inserm, LTSI UMR 1099, Rennes, France.
Center for Cardiological Innovation and Department of Cardiology, Oslo University Hospital, Oslo, Norway.
PLoS One. 2021 Jun 10;16(6):e0252857. doi: 10.1371/journal.pone.0252857. eCollection 2021.
Cardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of patients that may benefit from CRT is a challenging task and the application of current guidelines still induce a non-responder rate of about 30%. Several studies have shown that the assessment of left ventricular (LV) mechanics by speckle tracking echocardiography can provide useful information for CRT patient selection. A comprehensive evaluation of LV mechanics is normally performed using three different echocardioraphic views: 4, 3 or 2-chamber views. The aim of this study is to estimate the relative importance of strain-based features extracted from these three views, for the estimation of CRT response. Several features were extracted from the longitudinal strain curves of 130 patients and different methods of feature selection (out-of-bag random forest, wrapping and filtering) have been applied. Results show that more than 50% of the 20 most important features are calculated from the 4-chamber view. Although features from the 2- and 3-chamber views are less represented in the most important features, some of the former have been identified to provide complementary information. A thorough analysis and interpretation of the most informative features is also provided, as a first step towards the construction of a machine-learning chain for an improved selection of CRT candidates.
心脏再同步治疗（CRT）是一种基于植入物的治疗方法，适用于具有特定心力衰竭（HF）特征的患者。确定可能从 CRT 中受益的患者是一项具有挑战性的任务，目前的指南应用仍然导致约 30%的无反应率。多项研究表明，通过斑点追踪超声心动图评估左心室（LV）力学可以为 CRT 患者选择提供有用信息。通常使用三个不同的超声心动图视图来进行 LV 力学的综合评估：4 腔、3 腔或 2 腔视图。本研究旨在估计从这三个视图提取的基于应变的特征的相对重要性，用于估计 CRT 反应。从 130 名患者的纵向应变曲线上提取了多个特征，并应用了不同的特征选择方法（袋外随机森林、包裹和过滤）。结果表明，20 个最重要特征中的 50%以上是从 4 腔视图计算得出的。尽管 2 腔和 3 腔视图中的特征在最重要特征中代表性较低，但其中一些特征已被确定为提供补充信息。还提供了对最具信息量的特征的深入分析和解释，作为构建用于改进 CRT 候选者选择的机器学习链的第一步。
