Validation of the iCVS Model in Patients Admitted to the Intensive Care Unit: a Retrospective Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,150
- 主要终点
- Optimization and validation of the ICVS model
研究概览
简要总结
One of the significant challenges in the care of critically ill patients in the intensive care unit is the assessment of hemodynamically unstable patients (shock states). The treating team uses data such as heart rate, arterial blood pressure, physical examination findings, etc. to assess the patient's cardiac output, peripheral vascular resistance, filling pressures and volumes, and vascular tone. These assessments are essential to assess the cause of shock (cardiac cause, hypovolemia, sepsis, etc.), but in many situations they are not easy to perform and require extensive knowledge and experience. Assessing the hemodynamic status of a critically ill patient based on dynamic mechanical models may be a good option for more accurate assessment of complex patients. Such a model is, for example, the iCVS model: it is a relatively simple, mechanical-dynamic model that can be used to understand complex cardiovascular conditions. This model requires only age and weight as initial data, and as dynamic data the model requires only an arterial blood pressure (AR) curve and a central venous pressure (CVP) curve. The model is based on the shape of the above curves for the purpose of analyzing the cardiovascular status of the patient. The model is suitable for both adults and children, although most of the work to date has been among pediatric intensive care patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
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
- •The relevant experiment population are all patients over age 18 admitted to the ICU in Meir -
排除标准
- •Missing data
结局指标
主要结局
Optimization and validation of the ICVS model
时间窗: 1 year
The goal of this experiment is to perform an initial optimization and validation of the ICVS model with respect to the following metrics: * detection time before blood pressure drop * clinical shock classification.
次要结局
未报告次要终点
