NCT07428694招募中不适用
From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 20
- 试验地点
- 1
- 主要终点
- Correct interpretation of inspiratory leak by machine learning tool
研究概览
简要总结
Study of the applicability of machine learning tools in detecting inspiratory leakage in longterm non-invasive ventilation. The study was conducted in two stages. Firstly the ML model was trained on both bench model created scenarios and then ten patients. And secondly the success of the model was assessed in a proof of concept pilot study of ten patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 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
- •elective hospitalisation for control of non-invasive ventilation
- •use of ResMedLumis 100/150 ventilator
- •treatment for >3 months
排除标准
- •current exacerbation
结局指标
主要结局
Correct interpretation of inspiratory leak by machine learning tool
时间窗: one year
Measured in comparison with god standard method of polygraphy
次要结局
未报告次要终点
研究者
Marte Skogstad Allgot
Principal investigator
University of Oslo
研究点 (1)
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