# A precision medicine framework to improve long-term outcomes in Sepsis Survivors

> **NIH NIH K23** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $187,984

## Abstract

PROJECT ABSTRACT Advances in acute care have decreased short-term mortality of sepsis, resulting in an
increasing number of survivors who experience significant morbidity and mortality. Recurrent infections
account for 60% of hospital readmissions after sepsis and are an important cause of long-term mortality.
Identifying sepsis survivors at risk for infections and understanding predisposing factors are therefore important
first steps to develop personalized interventions to improve long-term outcomes.
The overall research goal is to develop innovative targeted interventions to improve long-term outcomes after
sepsis. This proposal focuses on identifying subgroups of sepsis survivors (phenotypes) at high risk for
infection-related hospital readmissions and deaths. It is hypothesized that the combination of host factors,
disease characteristics, and interventions prior to and during sepsis will identify distinct phenotypes that are
captured by clinical data. These clinical phenotypes likely have distinct pathophysiologic mechanisms (e.g.,
immunosuppression), predispose to different outcomes (e.g., recurrent infections), and patients with the same
phenotype may respond similarly to targeted interventions such as immunomodulation. In Aim 1, three
nationally representative datasets of sepsis survivors will be assembled and analyzed by machine learning
techniques to identify valid clinical sepsis phenotypes. In Aim 2, phenotypes at high-risk for infection-related
readmissions and deaths at 6 months will be identified, and underlying biomarker profiles for these phenotypes
analyzed. In Aim 3, these high-risk phenotypes will be used to conceptualize and simulate an adaptive platform
trial designed to test the efficacy of three different immunomodulatory drugs. An adaptive trial design was
chosen, because it can test multiple interventions simultaneously and reduces the chances of exposing
patients to ineffective or harmful interventions.
The research plan is augmented by expert mentoring and rigorous didactic training. Together, this will provide
the candidate with essential career development skills in the science of precision medicine, including machine
learning methodologies, Bayesian statistics, and innovate clinical trial design. This proposal has potentially
groundbreaking implications for the future treatment of sepsis survivors, because it deviates from the current
“one-size fits all” approach and attempts to create the framework for personalized interventions. This
framework sets the stage for future independent investigator (RO1) applications evaluating personalized
treatments in adaptive clinical trials, and will uniquely position the candidate as a future leader in the field of
long-term outcomes after critical illness.

## Key facts

- **NIH application ID:** 9897562
- **Project number:** 5K23GM132688-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Florian Mayr
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $187,984
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897562

## Citation

> US National Institutes of Health, RePORTER application 9897562, A precision medicine framework to improve long-term outcomes in Sepsis Survivors (5K23GM132688-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9897562. Licensed CC0.

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