Comparison of Sepsis Prediction Algorithms

Study Overview
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Brief Summary
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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
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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).

Official Title
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Prospective Evaluation of Sepsis Prediction Algorithms in a Multi-Hospital Healthcare System

Conditions
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Sepsis

Intervention / Treatment
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* Other: Epic Sepsis Model Version - 1
* Other: Epic Sepsis Model Version - 2
* Other: Emory Sepsis Model


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: All adult patients admitted through the ED Exclusion Criteria: None

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| ED Patients<br>All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system | Other: Epic Sepsis Model Version - 1<br>* The Epic Sepsis Model (ESM) version 1, a proprietary sepsis prediction model.<br>* Other names: Proprietary Epic sepsis algorithm -1;Other: Epic Sepsis Model Version - 2<br>* The Epic Sepsis Model (ESM) version 2, a proprietary sepsis prediction model.<br>* Other names: Proprietary Epic sepsis algorithm -2;Other: Emory Sepsis Model<br>* Emory internal algorithm<br>* Other names: Emory Sepsis Algorithm;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Patient hospitalization-level area under curve (AUC) for identification of sepsis, | Definition of Sepsis using the Centers for Disease Control and Prevention (CDC) Adult Sepsis Surveillance. | Duration of hospital stay (until discharge or death), an expected average of 30 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity, specificity, and Positive and Negative Predictive Value of algorithms | Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). | Duration of hospital stay (until discharge or death), an expected average of 30 days | 
| Lead time to antibiotic administration | The time between the initial deployment of the alert in patients confirmed to have sepsis (ture positives) and the physician's ordering of intravenous antibiotic therapy. | Duration of hospital stay (until discharge or death), an expected average of 30 days | 
| Percent expected increase in unnecessary antibiotics | Percent of patients who were incorrectly identified as having sepsis (false positives), and received antibiotics. | Duration of hospital stay (until discharge or death), an expected average of 30 days | 
| Number needed to screen | The number of alerts that would need to be processed to find one true positive sepsis. | Duration of hospital stay (or death), an expected average of 30 days | 
| Number of Total and false alert burden | The number of Total and false alert burden cumulative across all study patients over the study period | Duration of hospital stay (until discharge or death), an expected average of 30 days | 
| Time-horizon based AUCs | AUCs will be calculated at 3 pre-specified time horizons. | 4 hours, 8 hours, and 24 hours | 
| Accuracy and calibration by subgroup | The AUC and calibration curves will be compared by sex and race to ensure predictive accuracy is equal across subgroups. | Duration of hospital stay (until discharge or death), an expected average of 30 days | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Infection, Emergency Department, Algorithm, Prediction