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

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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.