Hearing recovery prediction and prognostic factors of idiopathic sudden sensorineural hearing loss: a retrospective analysis with a deep neural network model

Abstract Objective Idiopathic Sudden Sensorineural Hearing Loss (ISSHL) is an otologic emergency, and an early prediction of prognosis may facilitate proper treatment. Therefore, we investigated the prognostic factors for predicting the recovery in patients with ISSHL treated with combined treatment method using machine learning models. Methods We retrospectively reviewed the medical records of 298 patients with ISSHL at a tertiary medical institution between January 2015 and September 2020. Fifty-two variables were analyzed to predict hearing recovery. Recovery was defined using Siegel's criteria, and the patients were categorized into recovery and non-recovery groups. Recovery was predicted by various machine learning models. In addition, the prognostic factors were analyzed using the difference in the loss function. Results There were significant differences in variables including age, hypertension, previous hearing loss, ear fullness, duration of hospital admission, initial hearing level of the affected and unaffected ears, and post-treatment hearing level between recovery and non-recovery groups. The deep neural network model showed the highest predictive performance (accuracy, 88.81%; area under the receiver operating characteristic curve, 0.9448). In addition, initial hearing level of affected and non-affected ear, post-treatment (2-weeks) hearing level of affected ear were significant factors for predicting the prognosis. Conclusion The deep neural network model showed the highest predictive performance for recovery in patients with ISSHL. Some factors with prognostic value were identified. Further studies using a larger patient population are warranted. Level of evidence: Level 4.

A case of left main NSTEMI with ST segment elevation in lead aVR / 대한내과학회지

The 12-lead electrocardiogram (ECG) is an inexpensive bedside tool that most physicians use to make rapid diagnoses such as acute myocardial infarction (AMI), arrhythmia, and conduction abnormalities. Although each lead in the ECG represents electronic information from specific portions of the heart, lead aVR, an augmented unipolar limb lead, is frequently ignored. The aVR lead provides excellent information from the right portion of the heart, including the outflow tract of the right ventricle and basal portion of the septum. In this report, we discuss ST segment elevation changes in lead aVR of serial ECGs in emergency room patients with chief complaints of syncope and chest discomfort.