# Data-Driven Phenotyping of Severe Traumatic Brain Injury

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $38,704

## Abstract

Project Summary
This predoctoral fellowship will provide the applicant (Hayley Falk), a doctoral candidate in the Department of
Computational Medicine & Bioinformatics at the University of Michigan, with the skills necessary to become an
independent research investigator with expertise in novel applications of machine learning for TBI. The limited
accuracy of current models for early prediction of GCS 3-8 TBI (commonly referred to as severe TBI) outcomes
(prognostic models) is a major barrier to improving the clinical care of patients with GCS 3-8 TBI. Less than
20% of patients with GCS 3-8 TBI experience a good neurologic recovery and currently there are no
therapeutic agents that improve long-term outcomes. In GCS 3-8 TBI clinical trials of promising therapeutic
agents, a favorable outcome is typically defined as a better outcome than would be expected, taking into
account the predicted prognosis for each individual patient. Therefore, accurate estimation of predicted
prognosis is critical to assessing the efficacy of novel therapeutic agents. The leading prognostic models for
GCS 3-8 TBI, IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in TBI) and CRASH
(Corticosteroid Randomization After Significant Head Injury), have undergone extensive external validation,
however, the discriminative accuracy is highly cohort dependent with AUCs as low as 0.60 in some patient
groups. The two major limitations of the IMPACT and CRASH models include one-time measurements of
clinical predictor variables and regression-based methods, which are not designed to handle complex,
multidimensional datasets. Our objective is to derive a dynamic prognostic model which provides updated
outcome predictions as new data becomes available. We will then develop a clustering algorithm to identify
physiologically distinct subtypes (clusters) of GCS 3-8 TBI derived from continuous, high frequency data
streams. Our proposed goals will be achieved by the following specific aims: 1) we will derive a dynamic
prognostic model using a RNN (recurrent neural network)-based framework and data collected during the first
two weeks post-injury from BOOST-2 (Brain Oxygen Optimization in Severe Traumatic Brain Injury: Phase 2)
which provides updated 6-month outcome predictions every 24 hours; and 2) using time series hierarchical
clustering and continuous measures of physiologic parameters collected from subjects enrolled in BOOST-2
during the first two weeks post-injury, we will identify distinct subtypes of GCS 3-8 TBI and examine the
association between subtype and 6-month outcome. In Aim 1, we hypothesize that our dynamic prognostic
model derived from time-varying data will have a higher discriminative accuracy (AUC) than a static prognostic
model (similar to IMPACT and CRASH) derived using single timepoint data collected on the day of injury. In
Aim 2, we hypothesize that subtypes of GCS 3-8 TBI characterized by continuous physiologic parameters such
as incre...

## Key facts

- **NIH application ID:** 10379063
- **Project number:** 5F31NS118944-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Hayley Falk
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $38,704
- **Award type:** 5
- **Project period:** 2021-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10379063

## Citation

> US National Institutes of Health, RePORTER application 10379063, Data-Driven Phenotyping of Severe Traumatic Brain Injury (5F31NS118944-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10379063. Licensed CC0.

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