Novel Sepsis Sub-phenotypes Based on Trajectories of Vital Signs

Implementation and Prospective Validation of a Sepsis Sub-phenotyping Algorithm Based on Vital Sign Trajectories

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.

Study Overview

• Status: Completed
• Conditions: Sepsis
• Intervention / Treatment: Other: Implementation and evaluation of a sepsis sub-phenotyping algorithm

Detailed Description

The primary goal of this study is to investigate the feasibility of implementing a prospective sepsis subphenotyping tool in the electronic health record and evaluating the characteristics and outcomes of the sepsis subphenotypes. During this study, clinicians will not see the results of the algorithm or have access to its predictions. Instead, the algorithm will run silently in the background and continuously compute the subphenotypes of patients who are presenting to the emergency department (ED). For each patient, the probability of subphenotype membership over the first 8 hours of presentation to the ED will be calculated using an algorithm previously validated on retrospective data. Differences in clinical characteristics and outcomes between the subphenotypes will be compared. Investigators will seek to classify 1,200 patients with suspected infections. Since it will not be apparent on ED presentation who has suspected infection, all patients will be classified into subphenotypes using the algorithm, but the primary subgroup who will be analyzed will be patients with suspected infection.

• Study Type: Observational
• Enrollment (Actual): 1916

Contacts and Locations

Study Locations

• United States
  • Georgia
    • Atlanta, Georgia, United States, 30322
      • Emory University Hospital
    • Atlanta, Georgia, United States, 30308
      • Emory Saint Joseph's Hospital
    • Atlanta, Georgia, United States, 30308
      • Emory Hospital Midtown
    • Johns Creek, Georgia, United States, 30097
      • Emory Johns Creek Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

All adult patients who present to the emergency department at the participating facilities will automatically be enrolled in the study until the enrollment target for the study is met. All patients will be classified into subphenotypes using the algorithm, but the subgroup that will be analyzed will be patients with suspected infection.

Description

Inclusion Criteria:

• All adults who present to the emergency department

Exclusion Criteria:

• None

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
In-hospital mortality	Comparison of 30 day in-hospital mortality rate between the 4 subphenotypes.	Up to 30 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Renal replacement therapy (RRT)	Proportion of patients requiring (RRT) during hospital admission.	Through study completion, on average 30 days
Mechanical ventilation	Proportion of patients requiring mechanical ventilation during hospital admission.	Through study completion, on average 30 days
Vasopressor use	Proportion of patients requiring vasopressor use during hospital admission.	Through study completion, on average 30 days
Inotrope use	Proportion of patients requiring inotrope use during hospital admission.	Through study completion, on average 30 days
Admission to the intensive care unit (ICU)	Proportion of patients requiring admission to ICU during hospital admission.	Through study completion, on average 30 days
Hospital Length of stay	Duration of hospital length (from arrival to ED until hospital discharge) of stay in days.	Through study completion, on average 30 days
Response to Balanced Crystalloids vs Normal Saline	Within each subphenotype, the mortality rate will be compared between patients who received at least 2 liters in 24 hours of balanced crystalloids and patients who received normal saline. This is to evaluate the replicability of the finding of a significant mortality benefit from balanced crystalloids in Group D.	24 hours

Collaborators and Investigators

• Sponsor: Emory University
• Collaborators: National Institute of General Medical Sciences (NIGMS)
• Investigators: Principal Investigator: Sivasubramanium Bhavani, MD, Emory University

Publications and helpful links

General Publications

• Bhavani SV, Semler M, Qian ET, Verhoef PA, Robichaux C, Churpek MM, Coopersmith CM. Development and validation of novel sepsis subphenotypes using trajectories of vital signs. Intensive Care Med. 2022 Nov;48(11):1582-1592. doi: 10.1007/s00134-022-06890-z. Epub 2022 Sep 24.

Study record dates

Study Major Dates

• Study Start (Actual): September 18, 2025
• Primary Completion (Actual): January 14, 2026
• Study Completion (Actual): January 14, 2026

Study Registration Dates

• First Submitted: April 12, 2023
• First Submitted That Met QC Criteria: April 12, 2023
• First Posted (Actual): April 24, 2023

Study Record Updates

• Last Update Posted (Actual): February 23, 2026
• Last Update Submitted That Met QC Criteria: February 20, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: infection; vital signs; phenotypes; sub-phenotypes; subphenotypes
• Additional Relevant MeSH Terms: Pathologic Processes; Systemic Inflammatory Response Syndrome; Inflammation; Pathological Conditions, Signs and Symptoms; Sepsis; Infections
• Other Study ID Numbers: STUDY00004970; 5K23GM144867-02 (U.S. NIH Grant/Contract); 2022P004808 (Other Identifier: Emory Insight Humans IRB)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No