Machine Learning Sepsis Alert Notification Using Clinical Data

Status unconfirmed · Phase 2

Conditions studied: Sepsis, Severe Sepsis, Septic Shock

In brief

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.

Key facts

Study ID

NCT04005001

Run by

Dascena

People needed

37986

Starts

2021-09-25

Expected to finish

2022-08-31

Last updated by the study team

2022-05-03

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• 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

You may not qualify if…

• Patients under the age of 18
• Prisoners

Where it is running

• Baystate Health — Springfield, Massachusetts, United States (enrolling)
• Cooper University Health Care — Camden, New Jersey, United States (enrolling)
• Cape Regional Medical Center — Cape May, New Jersey, United States (enrolling)

Full record on ClinicalTrials.gov

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