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Allan E Stolarski¹, Jiyoun Kim², Jacob Nudel¹

¹Department of Surgery, Boston Medical Center, Boston University, Boston, Massachusetts.

Shock (Augusta, Ga.)

|June 27, 2022

Summary

This summary is machine-generated.

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Early non-invasive physiologic parameters in murine sepsis models can identify distinct patient phenotypes. This stratification aids future research into novel sepsis therapies and understanding inflammatory states.

Area of Science:

Immunobiology
Sepsis Pathophysiology
Machine Learning in Medicine

Background:

The immunobiology underlying varying clinical responses to sepsis is not fully understood.
Identifying distinct inflammatory states early in sepsis is crucial for effective treatment.

Purpose of the Study:

To hypothesize and test the identification of sepsis phenotypes using non-invasive physiologic parameters (NIPP) in murine models.
To distinguish between different inflammatory states in sepsis using early NIPP.

Main Methods:

Two murine models of sepsis were employed: gram-negative pneumonia (PNA) and cecal ligation and puncture (CLP).
Non-invasive physiologic parameters (NIPP) were collected at 6 and 24 hours post-infection in 291 mice.
Statistical modeling, including Lasso regression and machine learning, was used for analysis and phenotype identification.

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Main Results:

NIPP at 6 and 24 hours successfully identified sepsis phenotypes with an AUC of 0.93.
Key discriminating variables included temperature and pulse distention at 6 and 24 hours, and heart rate (HR) at 24 hours.
In CLP models, a 24-hour HR < 620 bpm predicted mortality (AUC 0.90), while PNA models showed complex physiological alterations without a single predictive variable.

Conclusions:

Non-invasive vitals assessed early (6 and 24 hours) can identify distinct sepsis phenotypes across different etiologies in murine models.
Stratification of sepsis based on these identified phenotypes can significantly advance future studies on novel sepsis therapies.