Evaluation of artificial intelligence-controlled CPR to improve vital organ perfusion and survival during prolonged resuscitation

项目摘要/摘要 在美国，每年发生近40万例院外心脏骤停(UchA)。在……里面 需要长时间进行心肺复苏(CPR)的患者，目前的CPR方法有 不能维持足够的血液流动和向重要器官输送氧气。患者的存活率为10% 有电击节律的有~0%，无电击节律的有~0%。当前美国心脏协会 (AHA)CPR建议遵循“一刀切”的模式。我们的目标是提高生命 “个人化”加压/减压延长心肺复苏期间的器官灌注 用动态CPR方法在治疗过程中改变按压特性 心肺复苏后考虑胸壁顺应性和血流动力学的时间变化 增加神经学上完好的存活率。 在这项拨款提案中，我们正在调查包含机器学习算法的部署 进入机械心肺复苏设备，以预测和优化心肺复苏过程中的血流动力学。我们将使用最先进的 与闭环控制算法相结合的动态建模 个性化心肺复苏术的特点，优化颞叶血流。我们的初步结果表明 在临床前脑室中部署机器学习预测算法和控制算法 颤动模型可实时调整压缩和解压深度，从而增加生命 器官血流与基于这些结果的标准CPR技术相比较，我们假设 对CPR的压缩深度、解压缩深度、占空比和压缩率进行优化将导致 为更好的结果干杯。我们提出的研究将：1)确定最有希望的预测算法 CPR血流动力学2)确定与该预测算法配对的最佳控制算法 优化心肺复苏血流动力学，恢复自主循环3)使用预测和控制配对 与对照组相比，提高猪室颤模型的48小时神经功能完整存活率 标准的心肺复苏技术。在整个过程中，我们将确定非侵入性替代测量 提供给算法，最终目标是继续进行设备开发和人体试验。

Project Summary / Abstract Almost 400,000 cases of out-of-hospital cardiac arrest (OHCA) occur each year in the United States. In patients requiring cardiopulmonary resuscitation (CPR) for prolonged periods, current CPR methods are unable to maintain adequate blood flow and oxygen delivery to the vital organs. Survival is <10% in patients with shockable rhythms and ~0% in those with non-shockable rhythms. Current American Heart Association (AHA) recommendations for CPR follow a “one-size-fits-all” paradigm. Our goal is to improve vital organ perfusion during prolonged CPR by “personalizing” compression/decompression therapy with a dynamic CPR method that changes compression characteristics over the course of CPR after taking into account the temporal changes of chest wall compliance and hemodynamics in order to increase the rate of neurologically intact survival after OHCA. In this grant proposal, we are investigating the deployment of machine learning algorithms incorporated into a mechanical CPR device to predict and optimize hemodynamics during CPR. We will use state-of-the-art dynamical modeling in conjunction with closed-loop control algorithms to individualize CPR characteristics and optimize temporal blood flow. Our preliminary results suggest that deployment of machine learning prediction algorithms paired with control algorithms in a preclinical Ventricular Fibrillation model can adapt compression and decompression depth in real time, resulting in increased vital organ blood flow as compared to standard CPR techniques Based on these results, we hypothesize that optimization of compression depth, decompression depth, duty cycle, and compression rate of CPR will lead to better outcomes. Our proposed research will: 1) identify the most promising algorithm for the prediction of CPR hemodynamics 2) identify the best control algorithm to pair with this prediction algorithm in terms of optimizing CPR hemodynamics and return of spontaneous circulation 3) use the prediction and control pairing to improve 48h neurologically intact survival in a porcine model of ventricular fibrillation, as compared to standard CPR techniques. Throughout this process, we will identify non-invasive alternative measurements to provide to the algorithms with the ultimate goal of proceeding with device development and human trials.