# Optimizing Recovery prediction after Cardiac Arrest (ORCA)

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $636,141

## Abstract

Abstract
Predicting recovery from anoxic brain injury and coma after cardiac arrest is challenging. Although patients
resuscitated from cardiac arrest are intensively monitored in critical care units, clinicians use only a tiny subset
of available data to predict potential for recovery, making neurological prognostication both slow and imprecise.
This is a specific example of a ubiquitous problem in modern medicine: routine clinical monitoring generates
vast quantities of rich information, but tools to transform these data to useful knowledge are lacking.
This project will leverage expertise in post-arrest critical care, information science, statistical modeling and
machine learning to make a system that rapidly delivers actionable prognostic knowledge. We have cleaned,
organized and aggregated a large, highly multivariate time series database with physiological and clinical
information with over 170,000 hours of quantitative electroencephalographic (EEG) features for >1,850 post-
arrest patients. We will refine and optimize analytical tools that predict recovery in this patient population more
rapidly and accurately than clinical experts. We will use innovative approaches to minimize risk of bias during
training of models introduced by outcome labels created by fallible human providers.
In Aim 1 of this proposal, we will use novel approaches to create informative and interpretable features from
heterogeneous clinical data including EEG waveforms, vital signs, medications and laboratory test results. We
will use deep learning to identify interpretable and parsimonious sets of these features that predict outcome.
We will train, test and compare the performance of multiple analytical tools. In Aim 2, we will prospectively
compare the best performing model(s) against a panel of expert clinicians. Models that confidently identify
patients with near-zero prospect of recovery with greater sensitivity or faster than expert clinicians can serve as
decision support systems. Improving the speed and accuracy of post-arrest prognostication will save lives,
allow appropriate resources to be directed to patients who are likely to benefit, avoid long and difficult care for
patients who cannot recover, and spare families the agony of uncertainty.

## Key facts

- **NIH application ID:** 10775812
- **Project number:** 5R01NS124642-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Jonathan Elmer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $636,141
- **Award type:** 5
- **Project period:** 2022-04-15 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10775812

## Citation

> US National Institutes of Health, RePORTER application 10775812, Optimizing Recovery prediction after Cardiac Arrest (ORCA) (5R01NS124642-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10775812. Licensed CC0.

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