Factors Associated with Response to Cardiac Resynchronization Therapy in Heart Failure Patients with Non-LBBB ECG Pattern

心力衰竭患者心脏再同步化治疗反应的相关因素 具有非LBBB心电图型 Pi：瓦伦蒂娜·库蒂法，医学博士，博士 罗切斯特大学医学中心，罗切斯特，纽约 收缩性心力衰竭(HF)治疗的发病率、死亡率和医疗费用正在迅速增加。 使用除颤器进行心脏再同步治疗(CRT-D)可有效降低心力衰竭事件和死亡率 QRS宽、射血分数低的心衰患者。然而，对CRT-D的反应并不是明确的 在所有患者中都存在，而在那些没有心电图异常的患者中获益较少，左束支 阻断(非LBBB)，构成了重大的治疗挑战。 由于这个队列中关于CRT-D反应的相互冲突和有限的数据，我们目前有可能 治疗大部分患有非LBBB的心力衰竭患者，这些患者从该设备中受益有限或没有受益。因此， 对于这种昂贵的治疗，更好地选择患者是有必要的。我们最近的一项研究表明，一种临床 受益于非LBBB且超声心动图反应显著的患者，并确定了预测因素。然而， 有必要前瞻性地验证这些超声心动图对非LBBB患者CRT-D反应的预测因素 在这个难以治疗的患者群体中，并确定潜在的新的心电和超声心动图预测指标， 利用机器学习的新统计方法。我们提出了一个前瞻性的、观察性的、单臂的 验证和确定超声心动图反应的预测因素的当前指南指示的队列研究 CRT-D，包括新的心电和超声心动图标志物，并评估随后的临床结果 270例植入CRT-D和非LBBB心电图型的心衰患者。 这项研究的主要目的是前瞻性地验证我们之前确定的临床预测因素。 超声心动图对非LBBB心衰患者CRT-D的反应有助于更好地选择患者。 我们的次要目标是确定新的心电和超声心动图变量的增量价值来预测 非LBBB患者对CRT-D的超声心动图反应，包括QRST和绝对值的心电图变量 积分和室电激活延迟，以及超声心动图衍生的左心室变量 不同步和收缩。然后，我们将应用开发的预测模型来前瞻性地识别非 患有CRT-D的LBBB患者有心力衰竭、室性心律失常或死亡的风险。第三个目标是确定 用机器学习分析新的心电图和超声心动图预测非LBBB患者的反应。 研究人群将包括270名患有非LBBB的心力衰竭患者(135名轻度心力衰竭和135名晚期心力衰竭)和 超声心动图核心实验室分析植入CRT-D后6个月的超声心动图随访结果，以及 评估心力衰竭、室性心律失常或死亡的临床结果。

Factors Associated with Response to Cardiac Resynchronization Therapy in Heart Failure Patients with Non-LBBB ECG Pattern PI: Valentina Kutyifa, MD, PhD University of Rochester Medical Center, Rochester, NY Morbidity, mortality, and health care costs of the treatment of systolic heart failure (HF) are rapidly increasing. Cardiac resynchronization therapy with a defibrillator (CRT-D) is cost-effectively reducing HF events and death in HF patients with a wide QRS and low ejection fraction. However, response to CRT-D is not unequivocally present in all patients, with less benefit in those without the presence of an ECG abnormality, left bundle branch block (non-LBBB), posing a significant treatment challenge. Because of the conflicting and limited data on response to CRT-D in this cohort, it is possible that we currently treat a large proportion of HF patients with non-LBBB who have limited or no benefit from the device. Therefore, better selection of patients for this expensive therapy is warranted. One of our recent studies suggested a clinical benefit in patients with non-LBBB and marked echocardiography response, and identified predictors. However, there is a need to prospectively validate these predictors of echocardiography response to CRT-D in non-LBBB in this hard-to-treat patient population, and identify potential novel ECG and echocardiography predictors, utilizing novel statistical methods of machine learning. We propose a prospective, observational, single-arm study in a currently guideline-indicated cohort to validate and identify predictors of echocardiography response to CRT-D, including novel ECG and echocardiography markers, and to assess subsequent clinical outcomes in 270 HF patients with an implanted CRT-D and non-LBBB ECG pattern. The primary aim of the study is to prospectively validating our previously identified clinical predictors of echocardiography response to CRT-D in HF patients with non-LBBB that could enable better patient selection. Our secondary aim is to identify the incremental value of novel ECG and echocardiography variables to predict echocardiography response to CRT-D in non-LBBB patients, including ECG variables of sum absolute QRST integral and ventricular electrical activation delay, and echocardiography-derived variables of left ventricular dyssynchrony and contractility. Then we will apply the developed predictive model to prospectively identify non- LBBB patients with CRT-D at a risk of heart failure, ventricular arrhythmias, or death. Tertiary aim is to identify novel ECG and echocardiography predictors of response in non-LBBB using machine learning analysis. Study population will include 270 HF patients with non-LBBB (135 with mild HF and 135 with advanced HF) and an implanted CRT-D with 6 months echocardiography follow-up analyzed by an echocardiography core lab, and assessing clinical outcomes of heart failure, ventricular arrhythmias, or death.