Predicting hearing recovery following treatment of idiopathic sudden sensorineural hearing loss with machine learning models.

PURPOSE: Idiopathic sudden sensorineural hearing loss (ISSHL) is an emergency otological disease, and its definite prognostic factors remain unclear. This study applied machine learning methods to develop a new ISSHL prognosis prediction model. MATERIALS AND METHODS: This retrospective study reviewed the medical data of 244 patients who underwent combined intratympanic and systemic steroid treatment for ISSHL at a tertiary referral center between January 2015 and October 2019. We used 35 variables to predict hearing recovery based on Siegel's criteria. In addition to performing an analysis based on the conventional logistic regression model, we developed prediction models with five machine learning methods: least absolute shrinkage and selection operator, decision tree, random forest (RF), support vector machine, and boosting. To compare the predictive ability of each model, the accuracy, precision, recall, F-score, and the area under the receiver operator characteristic curves (ROC-AUC) were calculated. RESULTS: Former otological history, ear fullness, delay between symptom onset and treatment, delay between symptom onset and intratympanic steroid injection (ITSI), and initial hearing thresholds of the affected and unaffected ears differed significantly between the recovery and non-recovery groups. While the RF method (accuracy: 72.22%, ROC-AUC: 0.7445) achieved the highest predictive power, the other methods also featured relatively good predictive power. In the RF model, the following variables were identified to be important for hearing-recovery prediction: delay between symptom onset and ITSI or the initial treatment, initial hearing levels of the affected and non-affected ears, body mass index, and a previous history of hearing loss. CONCLUSIONS: The machine learning models predictive of hearing recovery following treatment for ISSHL showed superior predictive power relative to the conventional logistic regression method, potentially allowing for better patient treatment outcomes.

Meteorological Factors and Air Pollutants Contributing to Seasonal Variation of Acute Exacerbation of Atrial Fibrillation: A Population-Based Study.

OBJECTIVE: We investigated seasonal variation of acute exacerbation of atrial fibrillation (AAF) and contributing environmental factors. METHODS: AAF events, meteorological elements, and air pollutants in Seoul between 2013 and 2015 were obtained from the nationwide database. AAF was defined if a patient visited the emergency room due to any AF-relevant symptoms or signs. RESULTS: AAF occurred less frequently in summer than in other seasons (6.71 vs 7.25 events/d, P = 0.005). AAF tended to decrease with an increase of air temperature (r = -0.058). Among air pollutants, NO2 was significantly lower in summer and positively correlated with AAF after adjusting for other variables (ß = 3.197). CONCLUSIONS: The rate of AAF events was the lowest in summer; air temperature and NO2 were contributing factors. The weather and environmental conditions should be considered as risk factors of AAF.