NCT07596264已完成不适用
Development and Validation of an Interpretable Machine Learning Model for Predicting Venous Thromboembolism（VTE）in Intensive Care Unit (ICU) Patients
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 12,061
- 试验地点
- 1
- 主要终点
- validate an interpretable machine learning (ML) model to predict VTE in ICU patients
研究概览
简要总结
Venous thromboembolism remains a leading cause of preventable mortality in intensive care unit (ICU) patients. Existing risk-stratification tools were developed in general medical populations and lack ICU-specific predictors. This study was to develop and validate an interpretable machine learning (ML) model to predict VTE in ICU patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 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 ≥18 years;
- •ICU length of stay ≥48 hour
- •the first ICU admission
排除标准
- •VTE diagnosed prior to ICU admission
- •VTE diagnosed within 24 hours of ICU admission
- •>20% missing values in key variables
结局指标
主要结局
validate an interpretable machine learning (ML) model to predict VTE in ICU patients
时间窗: the first day after the patients leaf ICU
次要结局
未报告次要终点
研究者
Weiwei Wu
vice president
Beijing Tsinghua Chang Gung Hospital
研究点 (1)
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