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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.
Insights
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.
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.
Objectives:
To apply unsupervised clustering methods to hemodynamic and transthoracic echocardiography (TTE) parameters to identify cardiovascular subphenotypes in ICU patients with sepsis. To examine subphenotype association with mortality and determine how differing hemodynamic management strategies influence these associations.
Design:
Retrospective, single-center cohort study.
Setting:
University Hospital ICU, Birmingham, United Kingdom.
Patients:
ICU patients that received TTE within 7 days of sepsis onset between April 2016 and December 2019 (derivation cohort) and January 2020 and December 2021 (validation cohort).
Interventions:
None.
Measurements And Main Results:
Nine hundred ninety-five patients were included in the derivation cohort and 804 patients in the validation cohort. A four-class model best fit both cohorts: class 1 (51% in derivation cohort, 56% in validation cohort; mostly normal left ventricular [LV] and right ventricular [RV] function), class 2 (30% in derivation cohort, 22% in validation cohort; mostly high cardiac index, hyperdynamic LV ejection fraction), class 3 (10% in derivation cohort, 12% in validation cohort, mostly dilated RV with impaired systolic function), and class 4 (9% in derivation cohort, 10% in validation cohort; mostly low cardiac output, with depressed LV ejection fraction). The four subphenotypes differed in their characteristics and outcomes, with 90-day mortality rates of classes 1-4 of 20%, 46%, 47%, and 41% in the derivation cohort and 18%, 45%, 57%, and 58% in the validation cohort, respectively ( p < 0.0001 for both cohorts). Following multivariable logistic regression analysis, classes 2-4 were independently associated with mortality. Three-variable models had high diagnostic accuracy in identifying all subphenotypes in both cohorts. The association with mortality of classes varied according to differing vasoactive agent and fluid administration strategies.
Conclusions:
Clustering analysis identified four cardiovascular subphenotypes in sepsis that reflected distinct circulatory failure mechanisms, were identifiable using simple models, and were associated with differing mortality risks and response to hemodynamic therapies. These classes may represent treatable traits to personalize shock management in sepsis.
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