# Leveraging Artificial Intelligence Solutions to Develop Digital Biomarkers for Precision Trauma Resuscitation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $756,268

## Abstract

PROJECT SUMMARY / ABSTRACT
 In the U.S., trauma is the leading cause of death for those 1-45 years old and hemorrhage remains the largest
contributing factor to preventable death. Providers must rapidly identify those suffering from hemorrhage to
optimize outcome, but internal bleeding remains difficult to diagnose even for experienced clinicians. Little is
known on presentation about those suffering from occult hemorrhage and providers must quickly make treatment
decisions in these time-pressured, time-sensitive clinical scenarios. This proposal seeks to develop through
artificial intelligence, a type of advanced machine learning, prediction algorithms that could be deployed
at the bedside of patients to assist clinicians with more timely recognition of hemorrhage. By doing so,
we hypothesize that this approach (integrating diverse data sources that have not previously been combined to
one another) could identify patterns in our patients that far surpass current capabilities to quickly detect and act
on the critical components contributing to outcome. The ability to rapidly pinpoint these patterns and display
them to the bedside clinician could allow more timely intervention and precise therapeutic approaches for
hemorrhage control.
 Beyond the challenges in rapidly identifying bleeding, current treatment of hemorrhage is rudimentary with
a standard resuscitation approach for all patients. This reflects attempts to optimize outcome based upon the
average treatment effect, rather than being adaptable for unique patient phenotypes. Hemorrhage is believed to
initiate a complex chain of events involving crosstalk between the coagulation and inflammatory systems that
are hypothesized to play a key role in outcome. Trauma has a known time zero of onset, making it an ideal model
to study the immediate pathophysiologic changes associated with hemorrhage. This complex, individual patient
biology is believed to explain why those suffering similar injury have differing outcomes. However, to date, these
individual characteristics are poorly understood and not factored into initial treatment approaches. Through this
proposal, I also seek to define novel digital biomarkers representing patient phenotypes that require
precision resuscitation approaches to maximize outcome. Fundamental to reducing hemorrhagic deaths is
the need to elucidate a deeper understanding of these mechanistic models of patient states. Strategies that help
to identify novel patient phenotypes that could benefit from more tailored treatment pathways may provide
important advances in decreasing preventable death.
 The net result of this proposal will be a deeper insight into the mechanistic models contributing to
evolving patient states following hemorrhage, and identify the key phenotypes or digital biomarkers
associated with mortality, complications, and occult hemorrhage. Finding solutions to advance our
resuscitation approaches following hemorrhage has potential to decrease complicatio...

## Key facts

- **NIH application ID:** 10765634
- **Project number:** 5R01HL149670-06
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Rachael A Callcut
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $756,268
- **Award type:** 5
- **Project period:** 2019-12-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765634

## Citation

> US National Institutes of Health, RePORTER application 10765634, Leveraging Artificial Intelligence Solutions to Develop Digital Biomarkers for Precision Trauma Resuscitation (5R01HL149670-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10765634. Licensed CC0.

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