Objectives: Pharyngocutaneous fistula (PCF) is one of the major complications following total laryngectomy (TL). Previous studies about PCF risk factors showed inconsistent results, and artificial intelligence (AI) has not been used. We identified the clinical risk factors for PCF using multiple AI models.
Materials & methods: Patients who received TL in the authors' institution during the last 20 years were enrolled (N = 313) in this study. They consisted of no PCF (n = 247) and PCF groups (n = 66). We compared 29 clinical variables between the two groups and performed logistic regression and AI analysis including random forest, gradient boosting, and neural network to predict PCF after TL.
Results: The best prediction performance for AI was achieved when age, smoking, body mass index, hypertension, chronic kidney disease, hemoglobin level, operation time, transfusion, nodal staging, surgical margin, extent of neck dissection, type of flap reconstruction, hematoma after TL, and concurrent chemoradiation were included in the analysis. Among logistic regression and AI models, the neural network showed the highest area under the curve (0.667 ± 0.332).
Conclusion: Diverse clinical factors were identified as PCF risk factors using AI models and the neural network demonstrated highest predictive power. This first study about prediction of PCF using AI could be used to select high risk patients for PCF when performing TL.
Keywords: Artificial intelligence; Fistula; Logistic regression; Total laryngectomy.
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