# R01 Administrative Supplement for AI Prediction of Trauma Resuscitation Responsiveness

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2023 · $768,943

## Abstract

PARENT R01 PROJECT ABSTRACT
The initial resuscitation of a trauma patient is often described as chaos and the clinician directing
the care must create calm while making life and death decisions often with inadequate
information. Despite some advances in understanding the biology of hemorrhage, injury still
accounts for over 5 million deaths per year, represents 1 out of every 10 deaths worldwide, and
remains the leading U.S. cause of death for those under 45. While > 90% of trauma patients do
well, the largest contribution to preventable death remains for those suffering from hemorrhage
and its related complications. Trauma has a known time zero of onset which makes it an ideal
model to study the immediate pathophysiologic changes associate with hemorrhage that lead to
differential outcome. To date, treatment pathways are considered rudimentary reflecting attempts
to optimize outcome based upon the average treatment effect, rather than being adaptable for
unique patient phenotypes. The parent R01 proposal is focused on exploring a deeper
understanding of the mechanistic modeling of initial patient response to injury (Aim 1) and
coupling this with improved real-time point of care bedside decision support technology (Aim 2)
to identify early those at risk of poor outcome. The net product of the parent proposal is to define
novel patient phenotypes that may require precision resuscitation approaches to maximize
outcome following hemorrhage. The goal of the parent R01 is to develop digital biomarkers for
precision trauma resuscitation with a focus on understanding the cross talk between the
inflammatory and coagulation profiles that occur as a systemic response to traumatic injury. The
overall goal of the parent R01 project remains unchanged and are to address limitations of our
current knowledge by improving forecasts of patient outcome trajectory at the point of care for
those suffering from traumatic injury. The parent R01 project addresses these gaps through two
interrelated aims which also remain unchanged:
 AIM 1. To develop a knowledge network (neural net) approach for characterizing early
 patient trajectory following hemorrhage. We hypothesize that (1A) predictive trajectories for
 mortality and complications can be ascertained through deep learning approaches; (1B) the addition of
 biologic data (inflammatory and coagulation markers) will further improve the prediction of patient states
 or unique phenotypic patient profiles (also known as digital biomarkers) attributable to differential
 outcome, and (1C) these phenotypes could be utilized to improve earlier recognition of patients off their
 predicted trajectory and thus, optimize triage and treatment pathways.
 AIM 2. To develop pilot prediction models for the detection of occult hemorrhage through
 the integration of high fidelity, integrated point-of-care data and bedside imaging. We
 hypothesize that the poor sensitivity for prediction of occult hemorrhage can be improved by developin...

## Key facts

- **NIH application ID:** 10908960
- **Project number:** 3R01HL149670-05S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Rachael A Callcut
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $768,943
- **Award type:** 3
- **Project period:** 2019-12-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10908960

## Citation

> US National Institutes of Health, RePORTER application 10908960, R01 Administrative Supplement for AI Prediction of Trauma Resuscitation Responsiveness (3R01HL149670-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10908960. Licensed CC0.

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