Key result
A preoperative XGBoost model predicted unplanned surgical reintervention within 30 days after free flap reconstruction with an AUC of 0.84.
Why the study?
Flap-related vascular complications requiring surgical reintervention cause morbidity after microsurgical free flap reconstruction, and preoperative risk estimation currently relies on clinical judgement.
Cohort (n=649)
Yes
An XGBoost-based preoperative risk model demonstrated good discrimination (AUC 0.84) for predicting unplanned surgical reintervention after microsurgical free flap reconstruction, though external validation is needed.
Answers are grounded in this paper and cite their sources.
May inform preoperative flap re-exploration risk; leaves open external validation before clinical use.
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.
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Laguna et al. (2026) conducted a cohort in microsurgical free flap reconstruction (n=649). Preoperative multivariable risk model (XGBoost) vs. Random forest and LASSO was evaluated on unplanned re-exploration within 30 days for arterial, venous, mixed thrombosis, or clinically significant vasospasm (AUC 0.84, 95% CI 0.74-0.92). A preoperative XGBoost model predicted unplanned surgical reintervention within 30 days after free flap reconstruction with an AUC of 0.84.
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