Development and Internal Validation of a Machine Learning Model to Predict the Occurrence of Incisional Hernia After a Midline Laparotomy
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Incisional Hernia
研究概览
简要总结
The objective of this study is to develop a predictive model of IH based on machine learning with the use of the XGBoost technique, this will help surgeons in charge of abdominal wall closure to have objective support to determine high-risk patients and in them modify the closure technique or use a mesh according to their choice or the degree of contamination of the abdominal cavity.
详细描述
Retrospective and observational study. The predictions will make using machine learning models. The programs use the scikit-learn, xgboost and catboost Python packages for modeling.
The evaluation of models will be using fourfold cross-validation, the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics calculated on the union of the test sets of the cross-validation.
The most critical factors and their contribution to the prediction will identify using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients older than 18 years of age
- •Postoperative midline exploratory laparotomy, who underwent urgent or scheduled surgery, regardless of their underlying diagnosis,
- •included between January 2010 and December 2016 and who completed 24 months of follow-up after surgery initial surgery.
排除标准
- •Reoperated for any cuestion diferent to present of hernia
- •Management of open abdomen
结局指标
主要结局
Incisional Hernia
时间窗: 24 months
incidence of Incisional hernia (the incisional hernia was defined according to the EHS guidelines as: a mass in the abdominal wall with or without visceral outlet or palpable in the surgical site determined by clinical examination or tomography).
次要结局
- Facial dehiscence(30 days)
研究者
Edgard Lozada
Clinical Research Level D
Hospital Regional de Alta Especialidad del Bajio