Using Machine Learning to Predict Acute Kidney Injury in Patients Following Cardiac Surgery
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 2,108
- 试验地点
- 1
- 主要终点
- acute kidney injury
研究概览
简要总结
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The investigatorshere developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes.
The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 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
- •age over 18 years who underwent cardiac surgery
排除标准
- •data miss greater than 10%
结局指标
主要结局
acute kidney injury
时间窗: 7 days
postoperative AKI was defined according to KDIGO criteria during the first 7 days after operation. Postoperative AKI was defined as either at an increase of at least 50% within 7 days or 0.3 mg/dL elevation within 48 h compared with the reference serum creatinine level.
次要结局
未报告次要终点
研究者
Yunlong Fan
Clinical Professor
Chinese PLA General Hospital