Recruiting
Phase 2

Observational Study

Sponsor:

Dascena

Code:

NCT04005001

Conditions

Sepsis

Severe Sepsis

Septic Shock

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

Interventions

HindSight

InSight

Study Details

Brief summary:

Machine learning is a powerful method to create clinical decision support (CDS) tools, when training labels reflect the desired alert behavior. In our Phase I work for this project, we developed HindSight, an encoding software that was designed to examine discharged patients' electronic health records (EHRs), identify clinicians' sepsis treatment decisions and patient outcomes, and pass those labeled outcomes and treatment decisions to an online algorithm for retraining of our machine-learning-based CDS tool for real-time sepsis alert notification, InSight. HindSight improved the performance of InSight sepsis alerts in retrospective work. In this study, we propose to assess the clinical utility of HindSight by conducting a multicenter prospective randomized controlled trial (RCT) for more accurate sepsis alerts.

Conditions

Sepsis

Severe Sepsis

Septic Shock

Study ID

NCT04005001

Start date

Sep 25, 2021

Status verified date

Apr, 2022

Completion date

Aug 31, 2022

Anticipated

Primary completion date

Aug 31, 2022

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

Inclusion Criteria:

  • During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the study, until the enrollment target for the study is met

Exclusion Criteria:

  • Patients under the age of 18
  • Prisoners

Study Design

Enrollment

37986 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Other

Interventions and Outcome Measures

Arms

experimental: Experimental

The experimental arm will involve patients monitored by HindSight.

active comparator: Control

The control arm will involve patients monitored by InSight.

Interventions

HindSight

HindSight will examine the dynamic trends of clinical measurements taken from a patient's EHR and analyzes correlations between vital signs to alert for the onset of sepsis.This machine learning based tool is optimized by encoder and utilizes periodic retraining to improve its performance over time.

InSight

Compared to the ability of the InSight software's recognition of sepsis onset to HindSight's performance. The study determines if the HindSight software has equivalent or better performance than the InSight software.

Primary outcome measure

  • Rate of reduction in false alerts [ Time Frame: Through study completion, human subjects involvement will occur for an average of eight months ]

Central Contacts and Locations

Central contacts

Jana Hoffman, PhD

2158806619jana@dascena.com

Gina Barnes, MPH

2158806619gbarnes@dascena.com

Locations

Baystate Health

Recruiting

Springfield, Massachusetts, United States, 01199

Cooper University Health Care

Recruiting

Camden, New Jersey, United States, 08103

Cape Regional Medical Center

Recruiting

Cape May, New Jersey, United States, 08210

More Information

Sponsor

Dascena

Last update posted

May 3, 2022

Last verified

Apr, 2022

Keywords

  • Sepsis
  • Machine learning algorithm
  • Clinical decision support

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Dascena on 2022-05-03.