# Situation Awareness to Improve Infant Sepsis Recognition in the Presence of Clinical Uncertainty

> **NIH NIH R01** · CHILDREN'S HOSP OF PHILADELPHIA · 2022 · $369,407

## Abstract

PROJECT SUMMARY
Sepsis has higher mortality in infants than other pediatric age groups, is associated with severe long-
term morbidities in 30-50% of survivors, and burdens healthcare resources with prolonged
hospitalization and complex interventions. Rapid identification of sepsis and timely initiation of
antimicrobial therapy are critical to improve infant outcomes. However, limitations to current diagnostic
approaches include the heterogeneous, subtle clinical presentation of infants and limited accuracy of
laboratory tests. There is therefore an urgent need for strategies to improve the early detection of sepsis
in infants to improve outcomes. Our objective is to improve sepsis recognition by developing an infant
sepsis early recognition system that combines patient data with predictive model outputs to deliver
timely, precise and relevant information to clinicians and nurses. Our hypothesis is that the integration of
a predictive model with clinical data displays that improve situation awareness will improve timely sepsis
recognition and management. We will utilize the strong foundation of our preliminary work in predictive
modeling and existing data from our neonatal sepsis registry to produce novel methods to identify infants
at greatest risk for neonatal sepsis. We have assembled a multi-disciplinary team of investigators from
the disciplines of data science, clinical informatics, neonatology, and sepsis/infectious disease research
to provide expert consensus recommendations. At the conclusion of the proposed work, we will have
developed methods that support the integration of clinical data and machine learning outputs into
decision support tools suited to clinical workflows. We anticipate such systems that pair advanced
prediction methods with user-centered design processes will have broad applicability to many conditions
and populations. This work will form the foundation for a future clinical trial to evaluate its ability to
identify infants at highest risk of sepsis and provide clinicians and nurses with the decision support
needed to improve their health and safety.

## Key facts

- **NIH application ID:** 10449396
- **Project number:** 5R01LM013526-02
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Robert W Grundmeier
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $369,407
- **Award type:** 5
- **Project period:** 2021-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449396

## Citation

> US National Institutes of Health, RePORTER application 10449396, Situation Awareness to Improve Infant Sepsis Recognition in the Presence of Clinical Uncertainty (5R01LM013526-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10449396. Licensed CC0.

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