A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 2,500
- 试验地点
- 1
- 主要终点
- Acute kidney injury
研究概览
简要总结
Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.
详细描述
The data of this study are divided into two parts: retrospective and prospective. The retrospective data were from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization from July 2015 to June 2025. The ratio of the training set, the internal validation set and the test set was 7:1:2. The prospective data is an external (temporal) validation set. Data collection began in July 2025 and is expected to end in February 2026
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
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
- •18 years old or above
- •Undergo non-cardiac surgery
排除标准
- •At least one measurement of serum creatinine (SCr) was not conducted before and after the operation
- •End-stage renal disease (ESRD) that has received dialysis within the past year
- •Baseline SCr ≥ 4.5 mg/dl (because the clinical criteria for AKI based on elevated SCr may not be applicable to these patients)
- •Acute kidney injury occurred within 7 days before the operation
- •The operation time is less than 2 hours
结局指标
主要结局
Acute kidney injury
时间窗: Within 7 days after the operation
次要结局
- Postoperative complications(Perioperative period)
- Postoperative mortality(Perioperative period)
- Hospitalization costs(Perioperative period)
- Hospital stays(Perioperative period)
研究者
Lanyue Zhu
Attending Physician
Zhongda Hospital