# Artificial Intelligence to Improve Resuscitation following Out-of-Hospital Cardiac Arrest

> **NIH NIH K01** · UNIVERSITY OF WASHINGTON · 2024 · $163,034

## Abstract

Project Summary Introduction: Jason Coult, PhD, is a scientist whose career goal is to become an
independent computational investigator improving survival from out-of-hospital sudden cardiac arrest
(OHCA) through research of novel OHCA resuscitation technologies that incorporate clinical
understanding. His K01 proposal, “Artificial Intelligence to Improve Resuscitation following Out-of-Hospital
Cardiac Arrest”, seeks to develop smarter, deep learning-based defibrillator algorithms that can improve
OHCA survival by guiding personalized resuscitation treatment. Candidate: Dr. Coult is a recently-
appointed Research Assistant Professor at the University of Washington Department of Medicine. He
completed a PhD in bioengineering in 2019, and has expertise in signal processing, artificial intelligence
(AI), and OHCA research. Career development and mentorship: Dr. Coult has convened an
interdisciplinary team of mentors and advisors with expertise in animal and human clinical resuscitation,
cardiac electrophysiology, prehospital emergency medical services (EMS), AI and deep learning,
computational arrhythmia models, biostatistics, and defibrillators. The training plan cultivates necessary
skills in deep learning and biostatistics, advances understanding of clinical resuscitation, provides career
guidance, and facilitates Dr. Coult’s progression to become a successful independent investigator. The
proposed work will take place under Thomas Rea MD (primary mentor) at the University of Washington.
Proposed research: OHCA is a leading cause of mortality. Survival is possible though generally poor.
Resuscitation currently follows a fixed, one-size-fits-all protocol that requires CPR interruption at regular
intervals to determine the patient’s cardiac rhythm, assess vital status, and apply treatment. This research
will use large retrospective datasets of human OHCA defibrillator recordings, Dr. Coult’s expertise and
mentorship/advisory group, and emerging deep learning methods to achieve the following aims: 1) to
design deep learning-based AI algorithms that can identify the specific OHCA rhythm (asystole, ventricular
fibrillation (VF), organized rhythm) and estimate the “vitality” (morphologic phenotype associated with
clinical outcome) of these rhythms during ongoing CPR, 2) to characterize the effect of drug interventions
on measures of vitality, and 3) to apply rhythm and vitality features to predict shock-refractory VF patients
(requiring ≥ 3 shocks) during CPR, enabling preemptive antiarrhythmics or other strategies to mitigate
subsequent shock failure. Summary: The proposed research will advance resuscitation towards a
precision strategy that aligns treatment with an individual patient’s real-time physiology, providing the
potential to improve OHCA survival. The training and mentorship will foster the development of necessary
AI-related technical skills, deepen clinical understanding of OHCA and resuscitation, and help inform next-
step, R01-funded res...

## Key facts

- **NIH application ID:** 10983612
- **Project number:** 1K01HL171797-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Jason Coult
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $163,034
- **Award type:** 1
- **Project period:** 2024-07-05 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10983612

## Citation

> US National Institutes of Health, RePORTER application 10983612, Artificial Intelligence to Improve Resuscitation following Out-of-Hospital Cardiac Arrest (1K01HL171797-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10983612. Licensed CC0.

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