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

影响心力衰竭患者心脏复苏治疗反应的相关因素 非LBBB ECG模式 主要研究者：Valentina Kutyifa，医学博士，哲学博士 罗切斯特大学医学中心，罗切斯特，纽约 收缩性心力衰竭（HF）的发病率、死亡率和治疗的医疗保健费用正在迅速增加。 使用除颤器（CRT-D）的心脏除颤治疗可经济有效地减少HF事件和死亡 在宽QRS和低射血分数的HF患者中。然而，对CRT-D的反应并不明确 在所有患者中均存在，在不存在ECG异常的患者中获益较少，左束支分支 阻滞（非LBBB），构成了重大的治疗挑战。 由于该队列中关于CRT-D应答的数据相互矛盾且有限，因此我们目前可能 治疗大部分从器械获益有限或无获益的非LBBB HF患者。因此，我们认为， 需要更好地选择患者来进行这种昂贵的治疗。我们最近的一项研究表明， 在非LBBB和显著超声心动图反应的患者中获益，并确定了预测因素。然而，在这方面， 需要前瞻性验证这些非LBBB患者对CRT-D的超声心动图反应的预测因素 在这个难以治疗的患者人群中，并确定潜在的新型ECG和超声心动图预测因子， 利用机器学习的新统计方法。我们提出了一个前瞻性，观察性，单臂 在当前指南指示的队列中进行的验证和识别超声心动图反应预测因子的研究 CRT-D，包括新型ECG和超声心动图标记物，并评估 270例植入CRT-D和非LBBB ECG模式的HF患者。 这项研究的主要目的是前瞻性地验证我们以前确定的临床预测因子， 非LBBB HF患者对CRT-D的超声心动图反应，可以更好地选择患者。 我们的次要目的是确定新的ECG和超声心动图变量的增量值，以预测 非LBBB患者对CRT-D的超声心动图反应，包括ECG变量（绝对QRR总和） 积分和心室电激动延迟，以及超声心动图导出的左心室变量 不同步和收缩性。然后，我们将应用开发的预测模型来前瞻性地识别非 接受CRT-D的LBBB患者存在心力衰竭、室性心律失常或死亡风险。第三个目标是识别 使用机器学习分析的新型ECG和超声心动图预测非LBBB患者的反应。 研究人群将包括270例非LBBB HF患者（135例轻度HF和135例晚期HF）， 植入CRT-D，由超声心动图核心实验室进行6个月超声心动图随访分析，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.