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Novel Sepsis Sub-phenotypes Based on Trajectories of Vital Signs

NCT05826223 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1916

Last updated 2026-02-23

No results posted yet for this study

Summary

Sepsis is a dysregulated host response to infection resulting in organ dysfunction. Over the past three decades, more than 30 pharmacological therapies have been tested in \>100 clinical trials and have failed to show consistent benefit in the overall population of patients with sepsis. The one-size-fits-all approach has not worked. This has resulted in a shift in research towards identifying sepsis subphenotypes through unsupervised learning. The ultimate objective is to identify sepsis subphenotypes with different responses to therapies, which could provide a path towards the precision medicine approach to sepsis.

The investigators have previously discovered sepsis subphenotypes in retrospective data using trajectories of vital signs in the first 8 hours of hospitalization. The team aims to prospectively classify adult hospitalized patients into these subphenotypes in a prospective, observational study. This will be done through the implementation of an electronic health record integrated application that will use vital signs from hospitalized patients to classify the patients into one of four subphenotypes. This study will continue until 1,200 patients with infection are classified into the sepsis subphenotypes. The classification of the patients is only performed to validate the association of the subphenotypes with clinical outcomes as was shown in retrospective studies. Physicians and providers treating the patients will not see the classification, and the algorithm classifying the patients will in no way affect the care of the patients. Further, all the data needed for the algorithm (vital signs from the first 8 hours) are standard of care, and enrollment in the prospective study does not require any additional data.

Conditions

Interventions

OTHER

Implementation and evaluation of a sepsis sub-phenotyping algorithm

The algorithm will run silently in the background and continuously compute the subphenotypes of patients who are presenting to the emergency department (ED) with suspected infection.

Sponsors & Collaborators

  • National Institute of General Medical Sciences (NIGMS)

    collaborator NIH

  • Emory University

    lead OTHER

Principal Investigators

  • Sivasubramanium Bhavani, MD · Emory University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-09-18
Primary Completion
2026-01-14
Completion
2026-01-14

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05826223 on ClinicalTrials.gov