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
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.