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Related Experiment Video

Updated: May 28, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Cardiovascular Subphenotypes in Sepsis.

Minesh Chotalia1,2, Muzzammil Ali2, Ravi Chotalia3

  • 1Birmingham Acute Care Research Group, Department of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom.

Critical Care Medicine
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Unsupervised clustering identified four cardiovascular subphenotypes in sepsis patients, each with distinct circulatory failure mechanisms and varying mortality risks. These identified traits may guide personalized shock management strategies.

Keywords:
cardiovascularclustersechocardiographysepsissubphenotypes

Related Experiment Videos

Last Updated: May 28, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

  • Cardiovascular Physiology
  • Critical Care Medicine
  • Data Science in Healthcare

Background:

  • Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.
  • Cardiovascular dysfunction is a hallmark of sepsis, but heterogeneity exists in its presentation and underlying mechanisms.
  • Identifying distinct cardiovascular subphenotypes in sepsis is crucial for targeted therapeutic interventions.

Purpose of the Study:

  • To apply unsupervised clustering to hemodynamic and transthoracic echocardiography (TTE) parameters for identifying cardiovascular subphenotypes in ICU sepsis patients.
  • To investigate the association between these subphenotypes and mortality.
  • To determine the influence of different hemodynamic management strategies on these associations.

Main Methods:

  • Retrospective, single-center cohort study including ICU patients who received TTE within 7 days of sepsis onset.
  • Unsupervised clustering methods were applied to hemodynamic and TTE parameters.
  • Derivation and validation cohorts were used to identify and confirm four distinct cardiovascular subphenotypes.

Main Results:

  • Four cardiovascular subphenotypes were identified, characterized by differing left ventricular (LV) and right ventricular (RV) function, cardiac index, and ejection fraction.
  • Significant differences in 90-day mortality rates were observed across the four subphenotypes (ranging from 18% to 58%).
  • Subphenotypes 2-4 were independently associated with increased mortality, and their association varied with hemodynamic management strategies.

Conclusions:

  • Clustering analysis successfully identified four cardiovascular subphenotypes in sepsis, reflecting distinct circulatory failure mechanisms.
  • These subphenotypes are identifiable using simple models and are associated with differential mortality risks and responses to hemodynamic therapies.
  • The identified subphenotypes represent potential 'treatable traits' for personalizing shock management in sepsis.