Using Machine Learning to Predict Acute Kidney Injury Requiring Renal Replacement Therapy After 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
- patients required renal replacement therapy
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 researcher here 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
- 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
- •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
patients required renal replacement therapy
时间窗: 14 days
The primary outcome was patients with the requirement for acute dialysis within 14 days after cardiac surgery. Renal replacement therapy is recommended for patients with severe acute kidney injury as well as hemodynamic instability or severe electrolyte disturbances (e.g. blood potassium \> 6) or acid-base balance disturbances (e.g. H value less than or equal to 7.15). Prior to the start of renal replacement therapy, the investigator invited a consultation with the nephrology department to assess the condition
次要结局
未报告次要终点
研究者
Yunlong Fan
Clinical Professor
Chinese PLA General Hospital