Evolution and Optimization of the HALP Formula for Predicting Free Flap Failure: A Progressive Analysis of Predictive Accuracy

Background: Reconstructive surgery with free flaps is a fundamental component in head and neck surgery, but flap failure remains a significant concern. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a prognostic factor commonly applied in the evaluation of patients suffering from malignant tumours. This score has emerged as a potential preoperative risk assessment tool; however, its predictive accuracy for free flap failure has not been fully evaluated. The potential of this formula, in the cervical–facial reconstructive area, has not yet been well studied in the international literature. Methods: This retrospective study included patients who had undergone free flap reconstruction in the maxillofacial surgery departments of Perugia and Ancona over the past ten years. To optimize the HALP formula, advanced machine learning technologies, mainly the Julius AI platform, were applied. The HALP formula was refined through three phases: the original formula, a weighted version, and the final “Modified HALP.” Predictive performance was assessed using ROC curve analysis, multivariate logistic regression, and internal validation. Results: The final version of the HALP score, created in the present study, achieved an AUC-ROC of 0.95 (95% CI: 0.91–0.99), outperforming the original and weighted versions. At the optimal cut-off, the final “Modified” HALP score demonstrated 90.91% sensitivity and 92.36% specificity, remaining an independent predictor of flap failure after adjusting for confounders. Conclusions: The aim of the study was to optimize the HALP score by integrating clinical information with the aim of making significant progress in the prediction of failure in reconstructive flaps. The Modified HALP score emerges as a powerful tool for preoperative risk stratification in free flap surgery, potentially improving patient outcomes through targeted interventions. Future research should focus on external validation and exploring the underlying biological mechanisms.