Effects of Sequentially Combined Arytenoid Adduction and Injection Laryngoplasty in Patients With Unilateral Vocal Fold Paralysis.

OBJECTIVES: Arytenoid adduction (AA) and injection laryngoplasty (IL) are major surgical options for the treatment of unilateral vocal fold paralysis (UVFP). AA is a laryngeal framework surgery and IL is a soft-tissue augmentation procedure. Therefore, the effect of each intervention will not be substitutive but complementary to the other. METHODS: Patients who received AA and IL were enrolled (N = 43). Mean age was 60.1 ± 12.7 years. Objective and subjective voice parameters including maximum phonation time (MPT), jitter, shimmer, noise to harmonic ratio (NHR), grade of dysphonia (G), and voice handicap index (VHI)-30 were collected preoperatively and 6 months postoperatively. AA and IL were sequentially performed with time interval; 28 (65.1%) patients received IL first followed by AA (IL+AA group) and 15 (34.9%) had AA followed by IL (AA+IL group). Time interval between first and second procedures was 9.9 ± 14.6 months. RESULTS: MPT, jitter, shimmer, NHR, G and VHI-30 significantly improved by both first and second procedures (P < 0.001). When we evaluated IL+AA group and AA+IL group separately, the final outcomes of MPT, jitter, G, and VHI-30 between the two groups were not significantly different. When the overall effects of IL and AA were compared, MPT significantly improved with AA than with IL (P < 0.001). CONCLUSION: In patients with unilateral vocal fold paralysis, sequential AA and IL (or IL and AA) provided additional improvement of subjective and objective voice parameters. Final outcomes of the two combined procedures resulted in similar degree of voice improvement regardless of the order of procedure.

Prediction of risk factors for pharyngo-cutaneous fistula after total laryngectomy using artificial intelligence.

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.