Related Experiment Video
Updated: Aug 5, 2026
A Pre-clinical Rat Model for the Study of Ischemia-reperfusion Injury in Reconstructive Microsurgery
Published on: November 8, 2019
Preoperative artificial intelligence-based risk model for surgical reintervention after microsurgical free flap
Luis Arturo Molina Laguna1, Alexandra Porras-Ramírez2, Giovanni Montealegre3
1Master in Epidemiology, Universidad El Bosque, Bogotá, D.C., Colombia; Department of Plastic and Reconstructive Surgery, Clínica Universitaria Colombia, Clínica Colsanitas S.A., Bogotá, D.C., Colombia; School of Medicine, Universidad del Rosario, Bogotá, Colombia; Grupo de investigación Salud de la Mujer, Department of Plastic and Reconstructive Surgery, Clínica Universitaria Colombia, Bogotá, D.C., Colombia.
Background:
Flap-related vascular complications requiring surgical reintervention remain a source of morbidity after microsurgical free flap reconstruction and preoperative risk estimation relies on clinical judgement. Therefore, we developed and internally validated a strictly preoperative multivariable model for this outcome.
Methods:
A retrospective cohort of 650 consecutive adults at two high-volume referral centres (649 analysable) was analysed. The primary outcome was unplanned re-exploration within 30 days for arterial, venous, mixed thrombosis, or clinically significant vasospasm. Nineteen preoperative predictors were considered; intraoperative and surgeon variables were excluded. Least absolute shrinkage and selection operator (LASSO), random forest, and XGBoost were fitted on a 70% training partition and evaluated on a 30% test set. Performance was assessed using discrimination (AUC), calibration (intercept, slope, ICI, and E/O), and Brier score. Reporting followed TRIPOD; risk of bias used the four-domain PROBAST.
Results:
Event rate was 17.1% (111/649). XGBoost achieved the highest discrimination (AUC 0.84; 95% CI 0.74-0.92) and relatively better calibration (intercept 0.02, slope 0.87, ICI 0.039, E/O 0.89). Random forest showed comparable discrimination (AUC 0.82; 0.71-0.90) but poorer calibration (slope 1.33). LASSO demonstrated the lowest discrimination (AUC 0.79; 0.69-0.88). Prior oncologic history, surgical indication, and flap composition were influential predictors. A decile table from XGBoost showed a monotonic gradient, with events rising from ≤10% in lower deciles to 80% (95% CI 58-92) in the top decile.
Conclusions:
XGBoost is retained as the principal model based on combined superiority in discrimination and calibration, with random forest as a robust comparator. The model is not decision-ready, and prospective external validation with recalibration is required before clinical adoption.
Lay Summary:
Using 649 analysable free flap reconstructions, we developed preoperative models to predict unplanned surgical reintervention for flap-related vascular complications within 30 days. XGBoost performed the best (AUC 0.84), supporting risk stratification before surgery, but prospective external validation is needed before clinical use.