# 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 · 2022 · $159,262

## Abstract

PROJECT 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). Previous models
grossly overestimated the MDR risk and exacerbated the escalating rates of antimicrobial resistance and excess
mortality. The overall goal of this proposed K08 research is to identify common sepsis phenotypes that
will enable better prescribing practices and standardized comparisons across hospitals, which will help
practicing clinicians, researchers, healthcare institutions, and policy makers. These themes correlate
with NIGMS's interest in finding innovative methods and leveraging big data to improve sepsis
outcomes. Our three specific aims reflect these goals: (1) establish resistance thresholds for MDR Gram-
negative bacilli (GNB) that cause sepsis, (2) assess the impact of sepsis definition on the performance of risk
prediction models for MDR GNB, and (3) identify sepsis phenotypes at high risk for MDR GNB in a well-balanced
cohort and assess the impact of case mix on risk prediction model performance. We will mathematically derive
resistance thresholds that link population resistance rates to individual patient risk of death in sepsis caused by
MDR GNB, assess factors that impact prediction performance, and incorporate rich clinical data from 15 hospitals
in our healthcare system to identify stable common sepsis phenotypes. Dr. Vazquez Guillamet has training in
Infectious Diseases and Critical Care Medicine and experience in antimicrobial resistance in critically ill patients.
This proposal will build on her clinical work and previous research experience in finding innovative methods to
solve challenging problems at the intersection of infectious diseases and critical care medicine. Dr. Vazquez
Guillamet has six career objectives: (1) pursue advanced training in clinical epidemiology; (2) acquire skills in
advanced linear regression and multilevel modeling; (3) learn supervised machine learning methods; (4) acquire
skills in big data management in healthcare and methods to handle missing data; (5) improve scientific
communication, grantsmanship, and leadership, and (6) participate in training in the responsible conduct of
research. She will achieve these goals through didactic coursework, hands-on research experience, and active
mentoring from experts in Infectious Diseases, Critical Care Medicine, and applied clinical informatics. She will
continue to develop innovative methods to mitigate the antimicrobial resistance crisis, especially in critically ill
patients, and become an analytics translator at the intersection of clinical medicine and clinical applied
informatics. The fertile research environment at Washington University in St. Louis, the experienced mentorship
team, and a well-crafted car...

## Key facts

- **NIH application ID:** 10469491
- **Project number:** 5K08GM140310-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Maria Cristina Vazquez Guillamet
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $159,262
- **Award type:** 5
- **Project period:** 2020-09-10 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10469491

## Citation

> US National Institutes of Health, RePORTER application 10469491, 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 (5K08GM140310-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10469491. Licensed CC0.

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