Elderly Surgical Patients Multi-Infection Prediction: Machine Learning Model Development & Validation With SHAP Analysis
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 42,540
- 试验地点
- 1
- 主要终点
- Machine Learning Prediction of Multiple Infections in Elderly Surgery Patients
研究概览
简要总结
Utilizing machine learning techniques, investigators developed the geriatric infection assessment model, leveraging domestic databases to predict multiple postoperative infections in elderly patients. The model addresses the current gap in predictive tools tailored for elderly surgical patients in China, offering insights into both overall and specific infection risks.
详细描述
Backgrounds:
Postoperative infections are a leading cause of adverse perioperative outcomes, particularly for elderly patients. Given the varied diagnostic presentations of infection, there is a significant gap in the use of predictive tools to identify those at high risk of developing such complications.
Objective:
Investigators aimed at developing machine learning models to predict various postoperative infection risks in elderly patients, facilitating early detection and intervention.
Methods:
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
- 65 Years 至 —（Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 65 years;
- •Patients undergoing surgeries not involving local anesthesia.
排除标准
- •Patients undergoing neurosurgery or cardiac surgery;
- •Patients with preoperative infections (including pneumonia, SSIs, UTIs, and bloodstream infections).
结局指标
主要结局
Machine Learning Prediction of Multiple Infections in Elderly Surgery Patients
时间窗: January 2012 - August 2018
Utilizing machine learning techniques, investigators developed the geriatric infection assessment model, leveraging domestic databases to predict multiple postoperative infections in elderly patients.
次要结局
未报告次要终点
研究者
Weidong Mi
Depatment of Anesthesiology, The First Medical Center
Chinese PLA General Hospital