# Sepsis phenotypes at risk for infections caused by multidrug resistant Gram-negative bacilli: elucidating the impact of sepsis definition and patient case mix on prediction performance

> **NIH NIH K08** · WASHINGTON UNIVERSITY · 2021 · $138,103

## Abstract

SUPPLEMENT ABSTRACT
Sepsis is a devastating syndrome that represents a leading cause of death, morbidity, and healthcare costs. Its
impact is amplified by rising rates of antimicrobial resistance. Improving sepsis outcomes primarily results from
prescribing timely antibiotics based on the estimated risk of multidrug resistance (MDR). Artificial intelligence
(AI) and machine learning (ML) are data- driven approaches looking for patterns in massive datasets. While
the AI/ ML algorithms rapidly advanced and built successful imaging processing applications, the promise of
AI/ML in sepsis and antimicrobial resistance research remains largely unfulfilled. The main reasons stem from
deficient, inaccessible and poorly labeled clinical data allowing for only a small portion of the electronic health
records (EHR) data to be used. More so, clinical narratives such as notes and imaging reports which contain
unstructured data elements in free text format are almost never used. Our parent K08 award aims to identify
sepsis phenotypes at risk for MDR GNB that will enable better antibiotic prescribing practices and standardize
comparisons across hospitals. We propose to accomplish our goal by leveraging big data and using innovative
methods such as ML methods. This supplement will strengthen our project by analyzing in detail the barriers to
efficiently using EHR data including unstructured data elements and providing data engineering solutions. The
objective is to provide the framework for ML use in sepsis research. Demonstrating reproducibility and rigor of
our ML methods and making the algorithms and datasets accessible per FAIR and TRUST principles will be
responsive to NIGMS and broader NIH priorities. Our aims reflect these priorities: 1) Analyze barriers to use
of EHR structured data and provide data engineering solutions for data enrichment, 2) Extract and
assess the importance of unstructured data in developing ML sepsis models, and 3) Compare the ML
sepsis models using unstructured and structured data VS structured data only and ensure algorithm
fairness by testing it across subgroups of interest based on gender and race. We will incorporate clinical
data from the 15 hospitals in our healthcare system serving an ethnically and socioeconomically diverse
patient population in rural, suburban and urban hospitals.
Dr. Vazquez Guillamet has training in Infectious Diseases and Critical Care Medicine and experience in sepsis
research. This supplement complements and broadens the initial K08 award. It serves as the natural next step
in deepening her expertise in innovative methods. This supplement will provide the opportunity for meaningful
collaborations with data scientists with ample expertise in unstructured data methods and data engineers
specialized in ML methods. It will help Dr. Vazquez Guillamet to promote clinically applicable algorithms for
challenging problems such as sepsis treatment.
For this supplement, Dr. Vazquez Guillamet will continue the col...

## Key facts

- **NIH application ID:** 10412800
- **Project number:** 3K08GM140310-02S1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Maria Cristina Vazquez Guillamet
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $138,103
- **Award type:** 3
- **Project period:** 2020-09-10 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412800

## Citation

> US National Institutes of Health, RePORTER application 10412800, Sepsis phenotypes at risk for infections caused by multidrug resistant Gram-negative bacilli: elucidating the impact of sepsis definition and patient case mix on prediction performance (3K08GM140310-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10412800. Licensed CC0.

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