Univ Rennes, CHU Rennes, Inserm, LTSI UMR 1099, 35000, Rennes, France.
Oslo University Hospital, Department of Cardiology, Oslo, Norway.
Int J Cardiovasc Imaging. 2021 Mar;37(3):989-998. doi: 10.1007/s10554-020-02083-1. Epub 2020 Nov 23.
We hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic and echocardiographic parameters, could enhance the appraisal of the likelihood of reverse remodeling and prognosis of favorable clinical evolution to improve the response of cardiac resynchronization therapy (CRT). Three hundred and twenty-three heart failure patients were retrospectively included in this multicenter study. 221 patients (68%) were responders, defined by a decrease in left ventricle end-systolic volume ≥15% at the 6-month follow-up. In addition, strain data coming from echocardiography were analyzed with custom-made signal processing methods. Integrals of regional longitudinal strain signals from the beginning of the cardiac cycle to strain peak and to the instant of aortic valve closure were analyzed. QRS duration, septal flash and different other features manually extracted were also included in the analysis. The random forest (RF) method was applied to analyze the relative feature importance, to select the most significant features and to build an ensemble classifier with the objective of predicting response to CRT. The set of most significant features was composed of Septal Flash, E, E/A, E/EA, QRS, left ventricular end-diastolic volume and eight features extracted from strain curves. A Monte Carlo cross-validation method with 100 runs was applied, using, in each run, different random sets of 80% of patients for training and 20% for testing. Results show a mean area under the curve (AUC) of 0.809 with a standard deviation of 0.05. A multiparametric approach using a combination of echo-based parameters of left ventricular dyssynchrony and QRS duration helped to improve the prediction of the response to cardiac resynchronization therapy.
我们假设，基于心电图和超声心动图参数的组合的多参数评估，可以增强对逆向重构可能性和有利临床转归的预测，以改善心脏再同步治疗（CRT）的反应。这项多中心研究回顾性纳入了 323 例心力衰竭患者。221 例（68%）患者为应答者，定义为 6 个月随访时左心室收缩末期容积减少≥15%。此外，还使用定制的信号处理方法分析来自超声心动图的应变数据。分析了从心动周期开始到应变峰值再到主动脉瓣关闭瞬间的局部纵向应变信号的积分。还包括 QRS 持续时间、室间隔闪烁和其他不同的手动提取特征。随机森林（RF）方法用于分析相对特征重要性，选择最重要的特征，并构建一个集成分类器，目的是预测 CRT 的反应。最重要的特征集由室间隔闪烁、E、E/A、E/EA、QRS、左心室舒张末期容积和从应变曲线中提取的 8 个特征组成。应用了具有 100 次运行的蒙特卡罗交叉验证方法，在每次运行中，使用不同的 80%患者的随机集进行训练，20%的患者进行测试。结果显示平均曲线下面积（AUC）为 0.809，标准偏差为 0.05。使用左心室不同步和 QRS 持续时间的超声心动图参数组合的多参数方法有助于改善对心脏再同步治疗反应的预测。
