# Transforming Resuscitation through Artificial INtelligence (TRAIN Study)

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $666,891

## Abstract

Project Summary
 Mortality from out-of-hospital sudden cardiac arrest (OHCA) is a large public health burden, accounting for
approximately 10% of all deaths in the US. Because OHCA is a leading cause of mortality, advances in
resuscitation have the potential to improve public health. Currently resuscitation protocols use a one-size-fits-
all approach. However, we now understand that OHCA occurs via heterogeneous mechanisms and manifests
a time-dependent pathophysiology, which influences prognosis. The heterogeneity suggests discrete clinical
phenotypes and an opportunity for individualized therapy. Distinguishing information about patient physiology
can be harnessed from the defibrillator. Continuous bio-measures of ECG, end-tidal carbon dioxide (ETCO2),
and transthoracic impedance (TI) can determine physiologic status and potentially guide optimal treatment.
 However, a real-time continuous approach to characterize a patient’s physiology and prognosis by
accurately determining the underlying rhythm and its vitality is not presently feasible without repeatedly
interrupting CPR. CPR interruption is required because chest compressions introduce ECG artifact that
prevents rhythm identification, prognostic assessment of rhythm morphology, and a patient’s underlying vital
status (vitality). However, CPR interruption is harmful since it disrupts perfusion in the otherwise pulseless
OHCA victim. Consequently, the current protocol is a compromise: CPR is interrupted every 2 minutes to help
inform care decisions though treatment proceeds empirically as CPR resumes and providers are typically
“blinded” to the actual underlying rhythm and vital status.
 Emerging evidence from the proposal team highlight the ability to use signal processing techniques to
investigate the ECG, ETCO2, and TI defibrillator signals during CPR to improve OHCA resuscitation. These
investigations use artificial intelligence (AI) methods to determine a patient’s instantaneous physiological status
and predict downstream resuscitation outcomes. We propose an investigative plan that will:
1. Derive and validate an integrated ventricular fibrillation (VF) OHCA algorithm that incorporates and builds
upon previously-validated modular algorithms, using artificial intelligence (AI) methods that process and
integrate ECG, TI, and ETCO2 bio-signals during active CPR.
2. Prospectively evaluate the integrated algorithms and their validated building block components in distinct
adult and pediatric OHCA populations.
3. Conduct a simulated randomized trial among EMS to compare the described precision strategy to the
current-day, fixed protocol to understand how dynamic prompts of a precision strategy affect CPR metrics.
 The project leverages an unparalleled data resource and a tested, multidisciplinary team with a track-record
of impactful resuscitation investigations involving novel approaches to AI and resuscitation. This consequent
precision strategy ultimately could transform resuscitation and...

## Key facts

- **NIH application ID:** 10854801
- **Project number:** 5R01HL169323-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** THOMAS D REA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $666,891
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10854801

## Citation

> US National Institutes of Health, RePORTER application 10854801, Transforming Resuscitation through Artificial INtelligence (TRAIN Study) (5R01HL169323-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10854801. Licensed CC0.

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