Development of machine learning-based predictive models for evaluating risk in the contralateral ear among patients with unilateral sudden sensorineural hearing loss.

backgroundSequential bilateral sudden sensorineural hearing loss (Se-BSSHL) may occur after an initial unilateral episode, but the risk factors for contralateral ear involvement remain unclear. This research aims to identify risk factors associated with the occurrence of contralateral ear in patients suffering from sudden sensorineural hearing loss (SSHL). By leveraging machine learning algorithms, the study uses patients' demographic and clinical data to develop a predictive model.

methodsWe conducted an analysis on clinical data from 939 patients admitted to the Ear Department of the Chinese PLA General Hospital from 2008 to 2022, categorizing them into unilateral sudden sensorineural hearing loss (USSHL) and sequential bilateral sudden sensorineural hearing loss (Se-BSSHL) groups. Stratified sampling was executed to maintain a proportional representation of unilateral versus bilateral cases, leading to the creation of seven internal and three external datasets. Variable selection was performed using a decision tree-based recursive feature elimination method. We applied five-fold cross-validation on the internal datasets and conducted model testing on the external datasets. Four machine learning algorithms-random forest, extreme gradient boosting, logistic regression, and support vector machine-were used to build the predictive model and conduct external validation. Model efficacy was primarily appraised using the area under the receiver operating characteristic curve (AUC-ROC), calibration curves, decision curve analysis (DCA), and additional performance metrics including accuracy, precision, recall, and F1-score on the test set. The influence of the variables was illustrated through Shapley value plots. Statistical methods such as the t -test, analysis of variance, and chi-squared test were used.

resultsWe observed a 12.57% (118/939) incidence rate of contralateral ear complications in USSHL patients with otherwise normal hearing. The predictive model incorporated 19 variables, among them, age, hearing loss degree, and sex are the three most significant features influencing the occurrence in the healthy ear of the USSHL group. An increase in age and a more severe degree of hearing loss are risk factors for the occurrence of the disease, and females have a higher risk of disease occurrence. Other high-risk test indicators for disease occurrence can be summarized and categorized into two types of risk factors: lipoprotein dysfunction (total cholesterol, high-density lipoprotein cholesterol, apolipoprotein A1) and inflammation (eosinophils, lymphocyte count, neutrophil count). The AUC of the model on the external test reached 0.77, with an accuracy of 88%, a precision of 84%, a recall of 88%, and an F1 value of 0.85 using the default classification threshold of 0.5, demonstrating robust predictive capabilities.

conclusionThis investigation elucidates the risk factors for contralateral ear disease in USSHL patients and establishes a robust predictive model for such occurrence, which offers substantial reference value for managing and preventing complications in USSHL patients.