# Multi Biomarker-based prediction tool development to determine risk of infections-related outcomes among severe blunt trauma patients

> **NIH NIH R03** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $84,000

## Abstract

Severe trauma injury renders patients vulnerable to infections and subsequent risk of infections-related
outcomes, including multiple organ failure/dysfunction syndrome (MOF/MODS), a major cause of mortality and
morbidity. Although it is well-established that infection is a major risk factor for MOF, not all patients who
experience nosocomial infections develop MOF, highlighting the importance of considering the underlying
molecular biological mechanisms of heterogeneity in susceptibility to MOF development after infections (ie.
infections-related MOF). In current clinical practices, MOF-specific score systems based on physiological
measurements such as the Denver and Marshall Scores are monitored and used to diagnose patients with MOF
after its onset. Here we propose to build prediction models for infections-related MOF before its onset using
molecular signatures in order to significantly increase prediction accuracy. Methods of rapid (ie. immediately
after the detection of infection) and accurate identification of patients who are highly susceptible to infections-
related outcomes are expected to aid in informed decision-making and ensuring appropriate delivery of
preventative measures to control MOF incidence. Such methods may thus result in improved health of patients
and reduced health care costs. This proposal aims to employ an unbiased computational approach to investigate
genome-wide transcriptome profiles and develop a panel of biomarkers to predict infections-related MOF
immediately after the detection of infection. Previous transcriptome studies in the context of infections often
focus on patient responses to infection. In contrast, we propose to focus on biomarker panel development to
predict a specific infections-related adverse outcome before it occurrs. Two Aims are proposed to predict the
outcome of infections-related MOF among blunt trauma patients, a population that is highly susceptible to
infections. Aim 1: using blood samples from the Inflammation and the Host Response to Injury Study (“Glue
Grant”), we will utilize our early blood transcriptome multi-biomarker development machine learning pipeline to
build models for prediction of infections-related MOF outcome among a cohort of blunt trauma patients. Aim 2:
we will build prediction models using injury severity scores and other common demographic and clinical variables
for infections-related MOF and compare their performance with the multi-biomarker model. We hypothesize that,
in comparison to models based on clinical scores, our proposed strategy based on transcriptomic signatures will
result in an increasingly accurate prediction and, furthermore, provide insights into the underlying molecular
mechanisms leading to MOF after infection. Identification of these molecular mechanisms may ultimately aid in
uncovering potential targets for pharmacological interventions. Overall, results from this study may provide the
foundation for further studies of infections-related outcome ...

## Key facts

- **NIH application ID:** 10322737
- **Project number:** 5R03AI151499-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Amy Tsurumi
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $84,000
- **Award type:** 5
- **Project period:** 2021-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10322737

## Citation

> US National Institutes of Health, RePORTER application 10322737, Multi Biomarker-based prediction tool development to determine risk of infections-related outcomes among severe blunt trauma patients (5R03AI151499-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10322737. Licensed CC0.

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