Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery

Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30 % of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being.Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models-logistic regression, support vector machine, and XGBoost-to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union.The models provided prediction results with 70% sensitivity, and the specificities of 66 % (XGBoost), 49 % (support vector machine), and 43 % (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol.