Machine-learning Model for Perioperative Risk Calculation
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 175,559
- 主要终点
- postoperative complications
研究概览
简要总结
The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values recorded in the Patient Data Management System (PDMS), which include not only vital and respiratory parameters, but also medication and doses, intraoperative events and times. Postoperative complications are defined according to their severity according to the Clavien-Dindo score (Dindo, Demartines et al., 2004) and are collected from the data available in the health information system (HIS).
The machine-learning model is created using an extreme-gradient boosting algorithm which has been updated with new data from the year 2021 to ensure accuracy of the model.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •all patients who underwent surgery with anesthesia
排除标准
- 未提供
结局指标
主要结局
postoperative complications
时间窗: 30 days
Postoperative complications are classified by means of the Clavien-Dindo-Score. The Clavien-Dindo-Score describes classes of severity of postoperative complications: Grade I: any deviation from the normal postoperative course without the need for pharmacological treatment or surgical, endoscopic and radiological interventions Grade II: requiring pharmacological treatment Grade IIIa: requiring surgical, endoscopic or radiological intervention not under general anesthesia Grade IIIb: requiring surgical, endoscopic or radiological intervention under general anesthesia Grade IVa: single organ dysfunction Grade IVb: multiorgandysfunction Grade V: death of a patient
次要结局
- in-hospital mortality(30 days)