Till Örebro universitet

BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore crucial. PURPOSE: This study aimed to evaluate different machine learning models to predict persistent dysphonia after ACDF.

STUDY DESIGN: A retrospective review of the nationwide Swedish spine registry (Swespine) PATIENT SAMPLE: All adults in the Swespine registry who underwent elective ACDF between 2006 and 2020.

OUTCOME MEASURES: The primary outcome was self-reported dysphonia lasting at least one month after surgery. Predictive performance was assessed using discrimination and calibration metrics.

METHODS: Patients with missing dysphonia data at the one-year follow-up were excluded. Data preprocessing involved one-hot encoding categorical variables, scaling continuous variables, and imputing missing values. Four machine learning models (logistic regression, random forest (RF), gradient boosting, K-nearest neighbor) were employed. The models were trained and tested using an 80:20 data split and 5-fold cross-validation, with performance metrics guiding the selection of the best model for predicting persistent dysphonia.

RESULTS: In total, 2,708 were included in the study. Twelve key predictors were identified. Four machine learning models were tested, with the RF model achieving the best performance (AUC = 0.794). The most significant predictors across models included preoperative NDI, EQ5Dindex, preoperative neurology, number of operated levels, and use of a fusion cage. The RF model, chosen for its superior performance, showed high sensitivity and consistent accuracy, but a low specificity and positive predictive value.

CONCLUSIONS: In this study, machine learning models were employed to identify predictors of persistent dysphonia following ACDF. Among the models tested, the RF classifier demonstrated superior performance, with an AUC value of 0.790. The RF model identified NDI, EQ5Dindex, and number of fused vertebrae as key variables. These findings underscore the potential of machine learning models in identifying patients at increased risk for dysphonia persisting for more than one month after surgery.