# Identifying pre-sepsis opportunities for early, targeted intervention

> **NIH NIH R35** · KAISER FOUNDATION RESEARCH INSTITUTE · 2020 · $401,918

## Abstract

ABSTRACT/PROJECT SUMMARY
SARS-CoV-2, the novel coronavirus resulting in COVID19 disease, has caused a global pandemic of
unprecedented impact. In just over three months, SARS-CoV-2 spread has infected more than 2 million
individuals and resulted in at least 150,000 deaths. Non-pharmacologic interventions (NPIs), like social
distancing and shelter-in-place measures, have proven to be the only effective strategy available today to
mitigate rapidly growing outbreaks. However, the effectiveness of social distancing depends on early
identification of viral spread, since even short delays in NPIs can result in overwhelming surges in acute illness
and healthcare demand. Unfortunately, current prediction models of SARS-CoV-2 viral spread are based on
lagging or incomplete indicators of infections like COVID19 case positivity, hospitalization, or death rates. As a
result, these prediction models may have limited efficacy during the earliest stages of viral spread, when NPIs
can have the greatest impact. This project will use methods my laboratory has developed to predict sepsis – a
life-threatening infectious disease marked by a dysregulated host response – that incorporate novel real-time
data to identify and compare the value of early indicators of SARS-CoV-2 viral spread. We will compare the
predictive utility of these data in SARS-CoV-2 with influenza, a seasonal viral disease that can cause sepsis
while also resulting in surges in healthcare demand. We will use a unique source of highly-detailed electronic
health record data arising from an integrated health system with more than 200 medical offices and 21
hospitals caring for 4.4 million patients. Our findings will have broad and immediate impact for predicting
SARS-CoV-2 viral spread that can inform effective strategies for COVID19 mitigation by patients, clinicians,
public health agencies, researchers, and health systems.

## Key facts

- **NIH application ID:** 10151938
- **Project number:** 3R35GM128672-03S1
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** Vincent Liu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $401,918
- **Award type:** 3
- **Project period:** 2018-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10151938

## Citation

> US National Institutes of Health, RePORTER application 10151938, Identifying pre-sepsis opportunities for early, targeted intervention (3R35GM128672-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10151938. Licensed CC0.

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