Machine Learning for Treatment Management Prediction in Laryngeal Fractures

Abstract

ObjectivesLaryngeal fractures are rare but potentially life-threatening traumas. Complications, such as airway obstruction and disrupted laryngeal anatomy, associate with significant morbidity. Early identification of at-risk patients and optimal management remain crucial for improved outcomes. Recently, machine learning (ML) has attained great attention as a unique and novel technique for evaluating complex non-linear relationships between multiple observations to create a predictive model with greater accuracy. This study aimed to demonstrate the potential of ML in predicting airway and surgical management of laryngeal fracture patients and identify key contributing parameters for the predictive performance of the ML models. MethodsThe ML models were developed using a patient series managed at the Helsinki University Hospital during 2005–2019. The developed models were further evaluated independently using a different cohort collected from the same institution between 1995–2004. ResultsThe ML showed a weighted area under curve (AUC) of 0.93 and accuracy of 0.86 following training for airway management. Likewise, for treatment approach, weighted AUC was 0.85 and accuracy 0.78. Injury type, Schaefer-Fuhrman grade (SF gr), age at incident, cause of injury, and fracture of the cricoid, in decreasing order of significance, were the most prominent features for the model's predictive performance for airway management. Similarly, our model identified SF gr, fracture of the cricoid, injury type, age at incident, and cause of injury as the most significant predictors for surgical treatment approach. ConclusionsThe proposed prediction of management approach by an ML technique can provide accurate predictions and thus aid clinicians in administering early and personalized interventions. The model may serve as a supporting tool in recognizing at-risk patients and in timely decision making. Further independent external validation is warranted for model generalizability.