Comparison of Sepsis Prediction Algorithms

Официальное название

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

Обзор

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.

Подробное описание

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).

Вмешательства

• Другое Epic Sepsis Model Version - 1
  The Epic Sepsis Model (ESM) version 1, a proprietary sepsis prediction model.
• Другое Epic Sepsis Model Version - 2
  The Epic Sepsis Model (ESM) version 2, a proprietary sepsis prediction model.
• Другое Emory Sepsis Model
  Emory internal algorithm

Первичные конечные точки

• Patient hospitalization-level area under curve (AUC) for identification of sepsis, [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]

Вторичные конечные точки (7)

• Sensitivity, specificity, and Positive and Negative Predictive Value of algorithms [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]
• Lead time to antibiotic administration [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]
• Percent expected increase in unnecessary antibiotics [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]
• Number needed to screen [Срок оценки: Duration of hospital stay (or death), an expected average of 30 days]
• Number of Total and false alert burden [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]
• Time-horizon based AUCs [Срок оценки: 4 hours, 8 hours, and 24 hours]
• Accuracy and calibration by subgroup [Срок оценки: Duration of hospital stay (until discharge or death), an expected average of 30 days]

Критерии участия

Критерии включения

• All adult patients admitted through the ED

Критерии исключения

• None

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

США · 7 центров

• Emory Midtown Hospital — Atlanta
• Emory Saint Joseph's Hospital — Atlanta
• Emory Healthcare System — Atlanta
• Emory Hospital — Atlanta
• Emory Decatur Hospital — Decatur
• Emory Johns Creek Hospital — Johns Creek
• Emory Hillandale Hospital — Lithonia

Идентификаторы

NCT: NCT05943938 · STUDY00005958 · 2024P008316

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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