Coronavirus Infectious Disease 2019: Ventilator Outcomes Using Artificial Intelligence, Chest Radiographs and Other Evidence-based Co-variates (COVID VOICE)
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 300
- 试验地点
- 3
- 主要终点
- Sensitivity and specificity of a convolutional neural network to predict survival outcome
研究概览
简要总结
We will determine ventilator outcomes to Coronavirus Infectious Disease 2019 (COVID-19) using artificial Intelligence with inputs of chest radiographs and other evidence-based co-variates.
详细描述
The chest radiograph (chest x-ray) has emerged as the United Kingdom's National Health Service (NHS) frontline diagnostic imaging test for COVID-19, in conjunction with clinical history and key blood markers: C-reactive protein (CRP) and lymphopenia. Typically, every suspected COVID-19 patient presenting to the emergency department is undergoing blood tests and a chest radiograph. Therefore, it has become critical for radiologists to review and "hot" report the chest x-ray urgently.
Primary Objective: Use chest radiographs and clinical data to determine whether patient can survive with a ventilator
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
- •Admitted to intensive care unit (ITU) or equivalent COVID-19 polymerase chain reaction (PCR) positive
排除标准
- •No imaging prior to ITU admission
结局指标
主要结局
Sensitivity and specificity of a convolutional neural network to predict survival outcome
时间窗: 1 month
Defined by sensitivity, specificity, positive and negative predictive values
次要结局
未报告次要终点