Machine Learning-Based Near-infrared Vision to Evaluate the Microcirculatory of Critical Ill Patients: A Prospective Observational Study
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 2,000
- 主要终点
- Hospital mortality
研究概览
简要总结
The investigators aimed to combine the image of near-infrared vision and machine learning method to evaluate the microcirculatory status of critical ill patients.
详细描述
The heat distribution of body is determined by the circulatory status. The investigators plan to the near-infrared vision to collect heat distribution information of limbs. Then, the machine learning method will be performed to recognize the subtle differences between images. Due to lack of golden standard of microcirculatory status, indirect parameters (such as lactate clearance, capillary refill time) and clinical outcomes will be recorded to evaluate the performance of maching learning model.
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
- •Age≥18 years;
- •Patients who were transfered to our ICU.
排除标准
- •Abnormalities of lower limbs arteries
结局指标
主要结局
Hospital mortality
时间窗: From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.
The rate of patients who died during hospital stay.
次要结局
- Lacteta clearance rate(When the near-infrared image is taken for a patient, the blood gas analysis will be performed immediately to get the value of lactate. After 2-hours, another blood gas analysis will be conducted to get the second value of lactete.)
- Capillary refill time (CRT)(When the near-infrared image is taken for a patient, the capillary refill time will be measured immediately.)