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

评估人工智能控制的心肺复苏在长时间复苏期间改善重要器官灌注和生存的效果

• 批准号: 10591524
• 负责人: Demetris Yannopoulos
• 金额: $ 58.17万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591524
• 关键词: Acute; Algorithms; American Heart Association; Animal Experiments; Applications Grants; Artificial Intelligence; Basic Science; Biofeedback; Blood flow; Carbon Dioxide; Cardiac; Cardiopulmonary Resuscitation; Cerebrum; Cessation of life; Characteristics; Chest wall structure; Choices and Control; Chronic; Circulation; Clinical; Clinical Trials; Collaborations; Coronary Arteriosclerosis; Coronary heart disease; Data; Databases; Device or Instrument Development; Devices; E-learning; Early Mobilizations; Evaluation; Family suidae; Feedback; Frequencies; Future; Generations; Goals; Hospitals; Hour; Human; Knowledge; Learning; Light; Linear Regressions; Machine Learning; Measurement; Measures; Mechanics; Metabolic; Methods; Modeling; Near-Infrared Spectroscopy; Neurologic; Organ; Outcome; Oxygen; Patients; Performance; Perfusion; Phase I Clinical Trials; Pre-Clinical Model; Process; Publishing; Recommendation; Research; Resuscitation; Survival Rate; System; Techniques; Testing; Time; Training; United States; Validation; Ventricular Fibrillation; Ventricular Tachycardia; algorithm training; clinic ready; clinically relevant; coronary perfusion; experience; experimental study; hemodynamics; improved; in vivo; indexing; innovation; machine learning algorithm; machine learning prediction algorithm; neural network; out-of-hospital cardiac arrest; porcine model; pre-clinical; prediction algorithm; pressure; prospective; time interval; verification and validation

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.

项目摘要 /摘要 在美国，每年发生近40万例院外心脏骤停（OHCA）。在 需要长时间需要心肺复苏（CPR）的患者，当前的CPR方法为 无法维持足够的血流和氧气递送到重要器官。患者的生存率<10％ 具有令人震惊的节奏，在具有不可震动的节奏的人中约为0％。现任美国心脏协会 （aha）CPR的建议遵循“千篇一律的”范式。我们的目标是提高重要 通过“个性化”压缩/减压，可以长时间CPR灌注 采用动态性心肺复合式方法的治疗，该方法在整个过程中改变压缩特征 考虑到胸壁依从性和血液动力学的暂时变化后，CPR 增加OHCA后神经学完整生存率的速度。 在此赠款建议中，我们正在研究机器学习算法的部署 进入机械性心肺复苏型装置，以预测和优化心肺复苏术期间的血液动力学。我们将使用最先进的 与闭环控制算法结合使用的动态建模 个性化CPR特征并优化临时血流。我们的初步结果表明 机器学习预测算法与控制算法配对的机器学习预测算法 纤颤模型可以实时适应压缩和减压深度，从而增加了生命力 根据这些结果，与标准CPR技术相比，器官血流，我们假设 CPR的压缩深度，减压深度，占空比和压缩率的优化将领导 更好的结果。我们提出的研究将：1）确定预测最有希望的算法 CPR血液动力学2）确定最佳的控制算法与该预测算法配对 优化CPR血液动力学和赞助商的返回圈3）使用预测和控制配对 在心室纤颤模型中改善神经学完整的生存率，与 标准CPR技术。通过此过程，我们将确定非侵入性替代测量值 提供算法的最终目标是进行设备开发和人体试验。