# Sepsis online: learning while doing to understand biology and treatment

> **NIH NIH R35** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $483,099

## Abstract

PROJECT SUMMARY / ABSTRACT
More than 1 million Americans are hospitalized with sepsis each year, and nearly one in
five don’t survive. Most efforts to reduce sepsis deaths begin with the premise that
patients are largely similar, and that ether moving treatment earlier or targeting
therapeutics to a single mechanism will improve outcomes. In prior work funded by a
NIGMS R35 award, we derived sepsis endotypes using a suite of machine learning
methods inside the electronic health records (EHR) in a large integrated health system.
These endotypes differed in biology, outcomes, and treatment response, and were
reproduced in thousands of patients. But how will they lead to precision care? In this
Renewal, we will leverage our clinical translational laboratory and remnant blood
collection to better understand the biology of sepsis endotypes and explore new
domains related to pathogen, microbiome, and molecular mechanisms. We will use
Bayesian causal networks and reinforcement learning to optimize treatment policies over
endotypes in more than 10 million EHR encounters. Finally, we will move learning online
and embed endotypes inside the EHR at the point-of-care. These steps will take the
science of sepsis endotypes and inform clinical decisions made under time pressure and
uncertainty. By testing endotype treatment policies at the “live-edge”, we will strengthen
causal inference, mechanistic insight, and learn while doing. My program will be
supervised by external advisory boards with expertise in machine learning, inflammation,
immunology, computational and systems biology, causal methods, artificial intelligence,
and health information technology. This work will further develop my clinical-translational
laboratory and cross-cutting mentorship of junior scientists.

## Key facts

- **NIH application ID:** 10169130
- **Project number:** 2R35GM119519-06
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Christopher Warren Seymour
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $483,099
- **Award type:** 2
- **Project period:** 2016-08-02 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10169130

## Citation

> US National Institutes of Health, RePORTER application 10169130, Sepsis online: learning while doing to understand biology and treatment (2R35GM119519-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10169130. Licensed CC0.

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