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

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $608,552

## Abstract

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.

## Key facts

- **NIH application ID:** 10186125
- **Project number:** 1R01HL157625-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Demetris Yannopoulos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $608,552
- **Award type:** 1
- **Project period:** 2021-04-15 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186125

## Citation

> US National Institutes of Health, RePORTER application 10186125, Evaluation of artificial intelligence-controlled CPR to improve vital organ perfusion and survival during prolonged resuscitation (1R01HL157625-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10186125. Licensed CC0.

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