# Precision Medicine Approach to Glucocortisteroids in Sepsis

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $649,030

## Abstract

PROJECT SUMMARY
Sepsis is a disorder that contributes to approximately 1 in 3 hospital deaths in the United States
and 1 in 5 deaths worldwide. Advancing sepsis management has been challenging in part due
to the heterogeneity of septic patients in demographics, comorbidities, infectious source,
microbiologic etiology, and level of organ dysfunction. Our group has previously identified sepsis
subclasses that differ in prognosis and response to treatment, suggesting that precision
medicine may improve sepsis care. Glucocorticoids (GCs) are commonly used in septic patients
despite inconsistent results from randomized controlled trials (RCTs), and they are an ideal
candidate to develop a precision medicine approach. Recently, two of the largest RCTs ever
conducted to test efficacy of GCs in sepsis (APROCCHSS and ADRENAL) also demonstrated
conflicting results. Reconciling discordant results between trials has proved challenging with
traditional methods but may be facilitated by state-of-the-art computational approaches which
incorporate machine learning to estimate the conditional average treatment effect based on
individual covariate patterns. In this proposal, we will create a ‘knowledge network’ using clinical
and biologic data from 4 RCTs of GCs in sepsis (APROCCHSS, ADRENAL, ESCAPe,
HYPRESS) and electronic health record data (Sepsis Endotyping in Emergency Care project).
In Aim 1, we will utilize unsupervised and supervised learning approaches using clinical data
from RCTs to characterize heterogeneity of treatment effect, identify subclasses that benefit,
and develop a treatment policy to reduce 90-day mortality. In Aim 2, we will use causal
Bayesian modeling approaches that incorporate RCT and EHR data to identify effect modifiers
and confounders of GC therapy and mortality. We will use these results to develop a treatment
policy to reduce 90-day mortality. Secondary analyses will compare RCT-only policies in Aim 1
to RCT-EHR policies in Aim 2. In Aim 3, we will perform cytokine assays and RNAseq using
samples from the ADRENAL, ESCAPe, and HYPRESS trials to identify endotypes that benefit
from GCs. We have assembled a multidisciplinary team of clinical trialists, biostatisticians,
computational biologists, and critical care specialists with an established track record of
collaboration for this proposal. Successful completion of our Aims will reconcile discordant
results of prior RCTs testing GCs in sepsis, develop a treatment policy that can be deployed in
EHRs, and improve design of future RCTs.

## Key facts

- **NIH application ID:** 10460580
- **Project number:** 5R01GM141081-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Derek C Angus
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $649,030
- **Award type:** 5
- **Project period:** 2021-08-02 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460580

## Citation

> US National Institutes of Health, RePORTER application 10460580, Precision Medicine Approach to Glucocortisteroids in Sepsis (5R01GM141081-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10460580. Licensed CC0.

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