Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 5,175
- 试验地点
- 1
- 主要终点
- Deep learning model's prediction ability on intraoperative hypotension event
研究概览
简要总结
The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.
详细描述
Intraoperative hypotension is associated with various postoperative complications such as acute kidney injury. Therefore, precise prediction and prompt treatment of intraoperative hypotension are important. However, it is difficult to accurately predict intraoperative hypotension based on the anesthesiologists' experience and intuition. Recently, deep learning algorithms using invasive arterial pressure monitoring showed the good predictive ability of intraoperative hypotension. It can help the clinician's decisions. However, most patients undergoing general surgery are monitored by non-invasive parameters. Therefore, the investigators investigate the prediction model for intraoperative hypotension using non-invasive monitoring.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •The patients who are included in the open database, VtialDB.
- •The patients who underwent inhaled general anesthesia for non-cardiac surgery.
- •The patients who have non-invasive monitoring data including blood pressure, electrocardiography, pulse oximetry, bispectral index, and capnography.
排除标准
- •The patient with missing data.
结局指标
主要结局
Deep learning model's prediction ability on intraoperative hypotension event
时间窗: through study completion, an average of 3 hour
Area under the curve the receiver operating characteristic (AUROC) curve for the deep learning model to predict intraoperative hypotension.
次要结局
未报告次要终点
研究者
Hyun Joo Ahn
Professor
Samsung Medical Center