Development and Pre-Clinical Testing of a Smart Defibrillator Algorithm for Treatment of Cardiac Arrhythmias

项目摘要/摘要： 意义：在美国，每年有30万至40万患者发生心脏性猝死。对病人来说 在风险中，植入式心脏除颤器(ICD)可提高存活率，主要是通过将高能电击传递给 恢复窦性心律。虽然高能量冲击显然是救命的，但它们不一定是良性的。患者接受 高能电击增加了死亡率，对设备进行编程以减少电击次数可以 提高死亡率。高能电击与心肌顿抑、损伤和细胞死亡有关。此外，他们还 对患者来说是痛苦的，与巨大的医疗成本相关，并导致更快的电池耗尽。 创新：心律失常动力学公司正在开发IntelliSync ICD控制算法，使ICD能够拯救生命 同时最大限度地减少有害和破坏性高能量冲击的使用。IntelliSync将通过两种方式实现这一点：1) 认识到室性心律失常发作有很高的可能在没有治疗的情况下自行终止； 以及2)使低能量除颤的传送更有可能终止心律失常，方法是最佳地将其定时到 脑室的高基线同步期。 方法：在这项授权中，我们建议在计算模型和RAT模型中测试这些算法的可行性 室颤。为了与临床相关，算法必须起作用并指导除颤器的 迅速做出决定。我们的智能同步算法的目标有两个：1)在2秒内分析心律失常的前6-8秒 秒，识别心脏内的区域耦合模式，这预示着即将发生的心律失常终止。如果 算法将触发ICD等待VF自行终止，并仅在 已发生超时。2)测量心脏内部的耦合模式和时间低能量治疗(IF 适当地)到更有可能成功的高度同步的时间段。IntelliSync也可能适用于心房 如果可以避免超过痛阈值的电击，可能会使心房除颤器变得美味。 目标1(1-6个月)：优化计算模型中的算法性能 次目标1A：确定表示高密度细胞内电压图所需的最小时空分辨率。 次级目标1B：确定预测即将终止的RQA变量熵的变化阈值。 分目标1C：证明低能量疗法可以有效地计时到更大的组织同步化时期。 目标2(3-9个月)：论证在高同步期进行低能量治疗的可行性 基于VF小动物模型的实时计算 次级目标2A：证明从有限的心外膜传感器测量的同步性是全光学标测的代表 次级目标2B：为确定可能自我终止的情节的RQA变量信息建立阈值 子目标2C：前瞻性测试目标2B中的熵阈值。 次级目标2D：论证进行实时RQA计算以指导低能量治疗的可行性。

Project Summary / Abstract: Significance: Each year 300,000 to 400,000 patients experience sudden cardiac death in the United States. For patients at risk, implantable cardiac defibrillators (ICDs) improve survival, largely through the delivery of high energy shocks to restore sinus rhythm. While high energy shocks are clearly life-saving, they are not necessarily benign. Patients receiving high energy shocks have increased mortality and programming the device to reduce the number of shocks delivered can improve mortality. High energy shocks are associated with myocardial stunning, injury, and cell death. In addition, they are painful to the patient, are associated with significant healthcare costs, and result in more rapid battery depletion. Innovation: Arrhythmia Dynamics, LLC is developing the IntelliSync ICD control algorithm which enable ICDs to save lives while minimizing the use of harmful and damaging high energy shocks. IntelliSync will do this in two ways: 1) recognizing ventricular arrhythmia episodes which have high probability of terminating on their own without therapy; and 2) making the delivery of low energy defibrillation more likely to terminate the arrhythmia by optimally timing it to a period of high baseline synchronization in the ventricular chambers. Approach: In this grant we propose testing the feasibility of these algorithms in computational and rat models of ventricular fibrillation. In order to be clinically relevant, the algorithms must function and guide the defibrillator’s decision quickly. The goal of our IntelliSync algorithm are 2-fold: 1) Analyze the first 6-8 seconds of an arrythmia within 2 seconds, recognize patterns of regional coupling within the heart that herald imminent arrhythmia termination. If identified, the algorithm would trigger the ICD to wait for VF to terminate on its own, and only deliver therapy after a time-out period has occurred. 2) Measure patterns of coupling within the heart and time low energy therapy (if appropriate) to a period of high synchronization when it is more likely to succeed. IntelliSync may also work for atrial fibrillation (AF) and could make an atrial defibrillator palatable if shocks above the pain threshold could be avoided. Aim 1 (months 1-6): Optimize algorithm performance in computational models of ventricular fibrillation Sub-aim 1A: Determine minimum spatiotemporal resolution needed to represent high density intracellular voltage maps. Sub aim 1B: Identify threshold of change in the RQA variable Entropy which predicts imminent termination. Sub-aim 1C: Demonstrate that low energy therapies can be effectively timed to periods of greater tissue synchronization. Aim 2 (months 3-9): Demonstrate feasibility of delivering low energy therapy timed to period of high synchronization based on real-time calculations in a small animal model of VF Sub aim 2A: Demonstrate that synchrony measured from limited epicardial sensors are representative of full optical maps Sub-aim 2B: Establish threshold values for the RQA variable Entropy which identify episodes likely to self-terminate Sub-aim 2C: Prospectively test threshold value of Entropy from Aim 2B. Sub-Aim 2D: Demonstrate feasibility of performing real-time RQA calculations to guide delivery of low energy therapy.