# Using Big Data to Understand Sepsis in an Immunocompromised Population

> **NIH NIH F31** · UNIVERSITY OF WASHINGTON · 2020 · $40,862

## Abstract

PROJECT SUMMARY/ABSTRACT
Through this proposal, I will develop and evaluate a sepsis prediction tool targeted to allogeneic hematopoietic
cell transplant (HCT) recipients. Allogeneic HCT recipients are an immunocompromised population that is
disproportionately affected by sepsis, a life-threatening dysregulated immunologic response to an infection.
While it is well established that early detection and treatment of sepsis with fluids and broad-spectrum antibiotics
reduce the risk of mortality, recent data suggests early broad-spectrum antibiotic use in allogeneic HCT
recipients may have microbiota-mediated detrimental effects on morbidity and mortality. Because of the risks
associated with both missed and falsely identified sepsis events among allogeneic HCT recipients, early and
accurate sepsis diagnosis is crucial. However, sepsis is generally challenging to diagnose and is made more
complicated in allogeneic HCT recipients by the fact that sepsis presents differently following transplantation and
common complications of the transplant procedure present like sepsis. In previous work, we demonstrated that
current sepsis clinical criteria have low predictive value among allogeneic HCT recipients and concluded that
population specific prediction tools are needed. Recently developed single algorithm, machine learning sepsis
prediction tools have shown promising results in general, immuno-competent populations. However, few studies
have tested the ability of machine learning workflows to predict sepsis in high-risk, immunocompromised
patients, such as allogeneic HCT recipients. Additionally, current sepsis prediction tools rely on the assumption
that the true relationship between the predictors and the outcome is contained within a single algorithm. This
proposed work has two main objectives. The first is to develop an automated sepsis prediction tool for allogeneic
HCT recipients using a state-of-the-art ensemble-based machine learning workflow (the super learner) that
relaxes the single algorithm assumption of current sepsis prediction tools. The second is to estimate the utility of
this tool in comparison to currently available tools in both traditional (accuracy methods) and novel ways
(mathematical modeling of health outcomes). Both aims will be completed with the ultimate goal of improving
sepsis prediction among allogeneic HCT recipients and in such, reducing sepsis related mortality and
inappropriate antibiotic use among this hard to diagnose population. Further, this research will advance the
methodological discussion around the usefulness of machine learning prediction tools in clinical practice and the
use of ensemble modeling for prediction of rare, high-case fatality diseases. Such advances have the potential
to improve the prediction of health outcomes beyond sepsis and reduce the burden of treatable diseases among
immunocompromised populations.

## Key facts

- **NIH application ID:** 10064529
- **Project number:** 1F31HL154509-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Margaret Lind
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $40,862
- **Award type:** 1
- **Project period:** 2020-08-05 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10064529

## Citation

> US National Institutes of Health, RePORTER application 10064529, Using Big Data to Understand Sepsis in an Immunocompromised Population (1F31HL154509-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10064529. Licensed CC0.

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