Prospective Evaluation of Sepsis Prediction Algorithms in a Multi-Hospital Healthcare System
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,200
- 试验地点
- 14
- 主要终点
- Patient hospitalization-level area under curve (AUC) for identification of sepsis,
研究概览
简要总结
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.
详细描述
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).
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All adult patients admitted through the ED
排除标准
- 未提供
研究组 & 干预措施
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
干预措施: Epic Sepsis Model Version - 1 (Other)
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
干预措施: Epic Sepsis Model Version - 2 (Other)
ED Patients
All adult patients presenting to Emergency Departments (ED) in the Emory Healthcare system
干预措施: Emory Sepsis Model (Other)
结局指标
主要结局
Patient hospitalization-level area under curve (AUC) for identification of sepsis,
时间窗: 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.
次要结局
- 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)
研究者
Siva Bhavani
Assistant Professor
Emory University