Incisional Hernia
Conditions
Brief summary
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.
Detailed description
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).
Interventions
DIAGNOSTIC_TESTNot intervention
Not having intervention is an observational study
Sponsors
Hospital Regional de Alta Especialidad del Bajio
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Inclusion criteria
* 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.
Exclusion criteria
* Reoperated for any cuestion diferent to present of hernia * Management of open abdomen
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incisional Hernia | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Facial dehiscence | 30 days | Incidence of fascial dehiscence (the fascial dehiscence is the presentation of separation of fascie with leakage of contents from the abdominal cavity |
Countries
Mexico
Contacts
Primary ContactEdgard Efren Lozada Hernández, Dr
Outcome results
None listed