Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 494
- 试验地点
- 1
- 主要终点
- Maturation of the fistula
研究概览
简要总结
The goal of this observational study is to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation. The main question it aims to answer is:
Can AI-driven models analyzing comprehensive ultrasonographic variables accurately predict the successful maturation of arteriovenous fistulas (AVFs)?
Participants who underwent radiocephalic arteriovenous fistula (AVF) creation had their preoperative ultrasonographic data analyzed using AI-driven models to predict successful AVF maturation over a four-year retrospective period.
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
- •patients who underwent RCAVF due to advanced chronic kidney disease from 2018 to 2022
排除标准
- •Patients who did not have follow-up data available
结局指标
主要结局
Maturation of the fistula
时间窗: 90 days
Fistula maturation was defined as an arteriovenous fistula that matures and is usable for dialysis with two-needle cannulation for hemodialysis for at least 90 days without the need for endovascular or surgical interventions.
次要结局
未报告次要终点
研究者
Ara Cho
Professor
Seoul National University Hospital