Prospective Evaluation of Sepsis Prediction Algorithms in a Multi-Hospital Healthcare System
Overview
- Phase
- Not Applicable
- Intervention
- Epic Sepsis Model Version - 1
- Conditions
- Sepsis
- Sponsor
- Emory University
- Enrollment
- 1200
- Locations
- 14
- Primary Endpoint
- Patient hospitalization-level area under curve (AUC) for identification of sepsis,
- Status
- Not yet recruiting
- Last Updated
- 3 months ago
Overview
Brief Summary
Sepsis is a severe response to infection resulting in organ dysfunction and often leading to death. More than 1.5 million people get sepsis every year in the U.S., and 270,000 Americans die from sepsis annually. Delays in the diagnosis of sepsis lead to increased mortality. Several clinical decision support algorithms exist for the early identification of sepsis. The research team will compare the performance of three sepsis prediction algorithms to identify the algorithm that is most accurate and clinically actionable. The algorithms will run in the background of the electronic health record (EHR) and the predictions will not be revealed to patients or clinical staff. In this current evaluation study, the algorithms will not affect any part of a patient's care. The algorithms will be deployed across the Emory healthcare system on data from all patients presenting to the emergency department.
Detailed Description
The primary goal of this study is to prospectively evaluate three sepsis prediction algorithms that are embedded in the EHR. The models will be deployed in a "shadow" mode, and the results will not be displayed to the treatment team during this study. Two of the algorithms are proprietary algorithms of the EHR provider (Epic). The third algorithm is an internally developed, open-source algorithm. The algorithms will compute the probability of sepsis at periodic intervals and will continue to run on a patient's data until the patient's discharge, death, or upon initiation of intravenous antibiotics (at which point there is an indirect record of clinical suspicion of an infection).
Investigators
Siva Bhavani
Assistant Professor
Emory University
Eligibility Criteria
Inclusion Criteria
- •All adult patients admitted through the ED
Exclusion Criteria
- Not provided
Arms & Interventions
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
Intervention: Epic Sepsis Model Version - 1
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
Intervention: Epic Sepsis Model Version - 2
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
Intervention: Emory Sepsis Model
Outcomes
Primary Outcomes
Patient hospitalization-level area under curve (AUC) for identification of sepsis,
Time Frame: Duration of hospital stay (until discharge or death), an expected average of 30 days
Definition of Sepsis using the Centers for Disease Control and Prevention (CDC) Adult Sepsis Surveillance.
Secondary Outcomes
- Sensitivity, specificity, and Positive and Negative Predictive Value of algorithms(Duration of hospital stay (until discharge or death), an expected average of 30 days)
- Lead time to antibiotic administration(Duration of hospital stay (until discharge or death), an expected average of 30 days)
- Number needed to screen(Duration of hospital stay (or death), an expected average of 30 days)
- Percent expected increase in unnecessary antibiotics(Duration of hospital stay (until discharge or death), an expected average of 30 days)
- Number of Total and false alert burden(Duration of hospital stay (until discharge or death), an expected average of 30 days)
- Time-horizon based AUCs(4 hours, 8 hours, and 24 hours)
- Accuracy and calibration by subgroup(Duration of hospital stay (until discharge or death), an expected average of 30 days)