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Aim “Incisional Hernia (IH) has an incidence of 10-23%, which can increase to 38% in specific risk groups. The objective of this study was developed and validated an artificial neural network (ANN) model for the prediction of IH after midline laparotomy (ML) and this model can be used by surgeons to help judge a patient’s risk for IH.” Material and Methods “A retrospective, single arm, observational cohort trial was conducted from January 2016 to December 2020. Study participants were recruited from patients undergoing ML for elective or urgent surgical indication. Using logistic regression and ANN models, we evaluated surgical treated IH, wound dehiscence, morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates.” Results “There was no significant difference in the power of the ANN and logistic regression for predicting IH, wound dehiscence, mortality, readmission, and all morbidities after ML. The resulting model consisted of 4 variables: surgical site infection, emergency surgery, previous laparotomy, and BMI(Kg/m2) &amp;gt; 26. The patient with the four positive factors has a 73% risk of developing incisional hernia. The area under the curve was 0.82 (95% IC 0.76-0.87). Conclusions “ANNs perform comparably to logistic regression models in the prediction of IH. ANNs may be a useful tool in risk factor analysis of IH and clinical applications.”

P055 THE USE OF ARTIFICIAL NEURAL NETWORK TO PREDICT THE RISK OF INCISIONAL HERNIA AFTER MIDLINE LAPAROTOMY