Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery
<|reference_start|>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.<|reference_end|>
@article{donié2024predictive,
    title={Predictive Model Development to Identify Failed Healing in Patients
  after Non-Union Fracture Surgery},
    author={Cedric Doni'e, Marie K. Reumann, Tony Hartung, Benedikt J. Braun,
  Tina Histing, Satoshi Endo, Sandra Hirche},
    journal={arXiv preprint arXiv:2404.11760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11760},
    primaryClass={cs.LG}
}