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arXiv · 2404.11760

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

Cedric Donié·Marie K. Reumann·Tony Hartung·Benedikt J. Braun·Tina Histing·Satoshi Endo·Sandra Hirche
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Abstract

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.

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Cedric Donié, Marie K. Reumann, Tony Hartung, Benedikt J. Braun, Tina Histing, Satoshi Endo, Sandra Hirche. 2024-04-17. Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery. https://doi.org/10.1109/embc53108.2024.10782650

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