# Classifying Sepsis Survivors into Actionable Phenotypes

> **NIH NIH R21** · CAROLINAS MEDICAL CENTER · 2021 · $168,563

## Abstract

PROJECT SUMMARY
Sepsis survivors face myriad challenges in the post-sepsis recovery period. Both sepsis
survivors and healthcare systems are motivated to reduce the strikingly high rate of hospital
readmission following an initial hospitalization for sepsis. Unfortunately, current one-size-fits-all
approaches to risk prediction and treatment strategies are inadequate for optimizing care for this
heterogenous population. The overarching goal of our work is to improve health outcomes and
reduce healthcare utilization for sepsis survivors. The objectives of this study are to apply latent
class analysis (LCA) to identify important new phenotypes of sepsis survivors with distinct
characteristics and risk profiles, and to demonstrate the application of LCA in determining
whether phenotype membership moderates the effectiveness of interventions designed to
reduce readmission. To achieve this objective we will 1) use our team's large, feature-rich
sepsis datamart to identify distinct phenotypes of sepsis survivors based on patients'
predisposing characteristics, illness factors, and treatments, 2) validate the predictive utility of
the developed method in a separate sample of 1200 sepsis survivors, and 3) determine whether
phenotype is a moderator of the effectiveness of a sepsis recovery program currently being
tested by our team in a clinical trial. Sepsis survivors represent a vulnerable population with high
morbidity, mortality, and hospital readmissions, and strategies to improve transition and
recovery are urgently needed. By transitioning away from whether post-sepsis treatment
strategies work toward which strategies work best for whom, our project will be the first rigorous
examination of phenotypes of sepsis survivors and their association with readmission risk and
differential treatment effects. Ultimately, these results will provide clinicians, researchers, and
policy makers with immediate, actionable data about how to target interventions to specific
group vulnerabilities, leading to effective and efficient reduction in post-sepsis morbidity.

## Key facts

- **NIH application ID:** 10149400
- **Project number:** 5R21LM013373-02
- **Recipient organization:** CAROLINAS MEDICAL CENTER
- **Principal Investigator:** Stephanie P Taylor
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $168,563
- **Award type:** 5
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10149400

## Citation

> US National Institutes of Health, RePORTER application 10149400, Classifying Sepsis Survivors into Actionable Phenotypes (5R21LM013373-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10149400. Licensed CC0.

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