A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 30,000
- 试验地点
- 6
- 主要终点
- Hospital mortality for elevated risk patients
研究概览
简要总结
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
详细描述
The objective of this proposal is to rapidly deploy a clinical decision support tool (eCARTv5) within the electronic health record of multiple medical-surgical units. eCART combines a real-time machine learning algorithm for identifying patients at increased risk for intensive care (ICU) transfer and death with clinical pathways to standardize the care of these patients based on a real-time, quantitative assessment of patient risk.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Background:
Clinical deterioration occurs in approximately 5% of hospitalized adults. Delays in recognition of deterioration heighten the risk of adverse outcomes. Machine learning algorithms enhance clinical decision-making and can improve the quality of patient care. However, their impact on clinical outcomes depends not only on the sensitivity and specificity of the algorithm but also on how well that algorithm is integrated into provider workflows and facilitates timely and appropriate intervention.
Preliminary Data:
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- Triple (Participant, Care Provider, Outcomes Assessor)
盲法说明
In control hospitals, eCART will be scoring silently in the background and not visible to the care provider or the patient. Because this is administrative data, the outcomes assessor will similarly be blinded to the score. In the intervention hospitals, care providers will be aware of the score and trained to it. Patients may be aware as a result.
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
- •18 years old
- •Admitted to an eCART-monitored medical-surgical unit (scoring location)
排除标准
- •Younger than 18 years old
- •Not admitted to an eCART-monitored medical surgical unit (scoring location)
结局指标
主要结局
Hospital mortality for elevated risk patients
时间窗: The outcome of hospital mortality for elevated risk patients will be tracked across 12 months
Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient. This data will be taken from the complete hospitalization, from admission to discharge.
次要结局
- Total hospital length of stay (LOS) for elevated risk patients(Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months)
- Ventilator-free days following an eCART elevation(The outcome of 30-day ventilator-free days will be tracked across 12 months)
- ICU-free days following an eCART elevation(The outcome of 30-day ICU-free days will be tracked across 12 months)