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: 10392491
• 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/10392491
• 关键词: Acute; Algorithms; American Heart Association; Animal Experiments; Applications Grants; Artificial Intelligence; Basic Science; Biofeedback; Blood Circulation; Blood flow; Carbon Dioxide; Cardiac; Cardiopulmonary Resuscitation; Cerebrum; Cessation of life; Characteristics; Chest wall structure; Choices and Control; Chronic; Clinical; Clinical Data; 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; Gaussian model; 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; Shock; Survival Rate; System; Techniques; Testing; Time; Training; United States; Validation; Ventricular Fibrillation; Ventricular Tachycardia; algorithm training; base; 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

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.

项目概要/摘要 美国每年发生近 40 万例院外心脏骤停 (OHCA) 病例。 对于需要长时间心肺复苏（CPR）的患者，目前的CPR方法是 无法维持重要器官的足够血流和氧气输送的患者存活率<10%。 具有可电击节律的患者和具有不可电击节律的患者约 0% 当前美国心脏协会。 (AHA) 对心肺复苏的建议遵循“一刀切”的范式，我们的目标是改善生命体征。 通过“个性化”加压/减压实现长时间心肺复苏期间的器官灌注 采用动态 CPR 方法进行治疗，该方法会在治疗过程中改变按压特性 考虑到胸壁顺应性和血流动力学的时间变化后进行心肺复苏，以便 提高 OHCA 后神经系统完整的存活率。 在这项拨款提案中，我们正在研究机器学习算法的部署 我们将使用最先进的技术来预测和优化心肺复苏期间的血流动力学。 动态建模与闭环控制算法相结合 我们的初步结果表明，个性化心肺复苏特征并优化颞部血流。 在临床前心室中部署机器学习预测算法与控制算法相结合 颤动模型可以实时调整按压和减压深度，从而增加生命力 与标准心肺复苏技术相比的器官血流量基于这些结果，我们勇敢地说 心肺复苏的按压深度、减压深度、占空比和按压率的优化将导致 我们提出的研究将：1）确定最有前途的预测算法。 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 CPR 血流动力学和自主循环恢复 3) 使用预测和控制配对 与相比，提高心室颤动猪模型的 48 小时神经系统完整存活率 在整个过程中，我们将确定非侵入性替代测量方法。 为算法提供最终目标，即进行设备开发和人体试验。