Ventral hernia repair in emergency settings. A machine learning model to predict post-operative complications

Ortenzi, Monica;Crepaz, Lorenzo;Anania, Gabriele;Balla, Andrea;Morales-Conde, Salvador;Mastronardi, Manuela;Arezzo, Alberto;Podda, Mauro;Azzolina, Danila;null, null;Di Pietrantonio, Daniela;Ercolani, Giorgio;Sartori, Alberto;Renzi, Francesco;Falsetti, Elena;Verdi, Daunia;Mondi, Isabella;Cianci, Pasquale;Cappiello, Miriam;Abdallah, Hussein;Palmisano, Silvia;Petrucciani, Niccolò;Capozucco, Alessandro;Bressan, Lidia;Soliani, Giorgio;Troia, Alessandro De;Torre, Beatrice;Laureti, Silvio;Piras, Carla;Marongiu, Paola;Cestaro, Giovanni;Bottini, Corrado;Nicotera, Antonella;Bonomo, Luca Domenico;Corallino, Diletta;Patriti, Alberto;Montori, Giulia;Agresta, Ferdinando;Guariniello, Anna;Piccoli, Micaela;Esposito, Sofia;Romano, Lucia;Padula, Martina;Lepiane, Pasquale;Pontecorvi, Emanuele;Silvestri, Vania;Neri, Silvia;Leonardi, Luca;Oldani, Alberto;Nardi, Mario;Millo, Paolo;Tilocca, Pier Luigi;Delogu, Daniele;Pierobon, Elisa Sefora;Baldan, Nicola;Sganga, Gabriele;Bianchi, Valentina;Ascari, Francesca;Scotto, Bruno;Vedana, Laura;Francesco, Fleres;Carmelo, Mazzeo;Clarizia, Guglielmo;Spolini, Alessandro;Brancato, Giorgio;Gervasi, Maria Clelia;Ferraro, Alessandro;Andreuccetti, Jacopo

2026-01-01

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Abstract

Background: Emergency ventral hernia repair remains a challenging procedure due to patient instability, contaminated surgical fields, and heterogeneity in hernia types and operative techniques. Predicting postoperative complications in this setting is difficult using traditional statistical methods. Machine learning (ML) may offer improved predictive accuracy by recognizing nonlinear patterns among multiple perioperative factors. Methods: A retrospective multicenter analysis was performed using data from the ACTIVE (Acute Treatment for Incisional Ventral Hernias) study, including 557 adult patients undergoing emergent ventral hernia repair between 2018 and 2021 in 31 Italian surgical centers. Demographic, preoperative, intraoperative, and postoperative variables were analyzed. Three ML algorithms—Decision Tree, Random Forest, and Deep Learning Neural Network—were trained and validated using five-fold cross-validation after class balancing with SMOTE. Model performance was compared with traditional logistic regression using accuracy, area under the ROC curve (AUC), and F1 score. Results: Postoperative complications occurred in 181 patients (32.5%), while major complications (Clavien–Dindo ≥ II) occurred in 10%. Random Forest achieved the best performance (AUC 0.95, accuracy 0.88, F1 score 0.86), outperforming logistic regression (AUC 0.82, accuracy 0.78). The most influential predictors were operative duration, ASA score, and sepsis for overall complications, while bowel obstruction and BMI were key factors for major complications. Surgical approach (open vs. laparoscopic) did not independently correlate with adverse outcomes, highlighting the complexity of patient- and case-specific interactions. Conclusions: Machine learning models can accurately predict postoperative complications following emergent ventral hernia repair, surpassing traditional regression methods. These findings suggest that ML-based decision tools could support risk stratification and optimize surgical planning in high-risk emergency settings. Prospective validation is warranted to integrate AI-assisted prediction into perioperative clinical workflows.