Prediction of Patient Deterioration Using Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 526
- 试验地点
- 2
- 主要终点
- Alarm burden
研究概览
简要总结
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration throughout a patient's admission. This algorithm was then validated in a validation cohort.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 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
- •Cared for in the Brigham and Women's Home Hospital study
排除标准
- •Incomplete continuous monitoring data
研究组 & 干预措施
Training
A subset of patients that are used to train the machine learning algorithm.
干预措施: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2 (Other)
Validation
A subset of patients that are "held back" and used to validate the algorithm's accuracy.
干预措施: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2 (Other)
结局指标
主要结局
Alarm burden
时间窗: From admission to discharge, measured in hours, on average 5 days
The number of alarms fired per patient per hour
次要结局
- Rate of alarms with clinical utility(From admission to discharge, on average 5 days)
- Specificity for recognition of a safety composite(From admission to discharge, on average 5 days)
- Positive predictive value for recognition of a safety composite(From admission to discharge, on average 5 days)
- Sensitivity for recognition of a safety composite(From admission to discharge, on average 5 days)
- Negative predictive value for recognition of a safety composite(From admission to discharge, on average 5 days)
研究者
David Levine
Attending Physician
Brigham and Women's Hospital