Development and Validation of The Skin Graft Loss Risk Estimator: An Interpretable Machine Learning-Driven Tool for Individualized Assessment in Burn Patients.

Abstract

Background: Skin graft failure is a significant complication in burn care that prolongs recovery, compromises surgical outcomes, and necessitates repeat operations. Accurate risk assessment is challenging due to a complex interplay of patient and injury factors, underscoring the need for objective, data-driven predictive tools. We developed and validated an interpretable machine learning (ML)-driven tool to provide individualized, data-driven risk assessment for skin graft loss in burn patients.

Methods: A cohort study of patients who had autologous skin grafting from the American Burn Association Burn Care Quality Platform was conducted from 2013-2022. A Random Forest ML model was developed and validated on independent training (70%), validation (15%), and testing (15%) sets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC).

Results: Of 286,479 patients during the study period, 24,046 underwent autologous skin grafting. ML demonstrated discriminatory predictive performance with AUC-ROC of 0.794. Burns ≥40% total body burn surface area (OR 8.16, p<0.001), history of alcohol abuse (OR 1.86, p<0.001), active tobacco use (OR 1.45, p=0.004), age >60 years (OR 1.70, p=0.013), hypertension (OR 1.52, p=0.003), male sex (OR 0.74, p=0.009), diabetes mellitus (OR 1.45, p=0.030) and contact burns (OR 1.70, p=0.006) emerged as independent predictors of graft loss. The model was translated into an interactive, interpretable web-based clinical tool.

Conclusions: The Skin Graft Loss Risk Estimator provides a transparent, interpretable and personalized risk assessment of skin graft loss to enhance clinical judgment, facilitate effective patient counselling and surgical planning in burn care.