Effect of a Sepsis Prediction Algorithm on Clinical Outcomes

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

75147 participants

Study Classification

INTERVENTIONAL

Study Start Date

2017-01-31

Study Completion Date

2018-06-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

In this clinical outcomes analysis, the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay, and 30-day readmission was evaluated.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

NCT03752489

Sepsis Severe Sepsis Septic Shock

UNKNOWN PHASE2

Predictive algoRithm for EValuation and Intervention in SEpsis

NCT03235193

Sepsis Septic Shock Severe Sepsis

COMPLETED NA

An Algorithm Driven Sepsis Prediction Biomarker

NCT03015454

Sepsis Septic Shock Severe Sepsis

COMPLETED NA

RCT of Sepsis Machine Learning Algorithm

NCT03882476

Sepsis Severe Sepsis Septic Shock

WITHDRAWN PHASE2

Subpopulation-Specific Sepsis Identification Using Machine Learning

NCT03644940

Sepsis Severe Sepsis Septic Shock

WITHDRAWN PHASE2

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Materials and Methods: Clinical outcomes evaluation performed on a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals. Mortality, hospital length of stay, and 30-day readmission analysis performed for 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Severe Sepsis

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Comparator

The comparator arm will involve patients monitored by InSight.

Group Type EXPERIMENTAL

InSight

Intervention Type DIAGNOSTIC_TEST

Clinical decision support (CDS) system for severe sepsis detection and prediction

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

InSight

Clinical decision support (CDS) system for severe sepsis detection and prediction

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* All patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities were automatically included for clinical outcomes analysis