Epidemiology of idiopathic sudden sensorineural hearing loss in the era of big data

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery(2022)

引用 2|浏览4
暂无评分
摘要
Objectives Idiopathic sudden sensorineural hearing loss (ISSNHL) is a medical emergency, and delayed treatment can have permanent sequelae. However, the etiology of ISSNHL is diverse and unclear; thus, it is idiopathic. To develop an insight into this condition, patients with ISSNHL must be clearly identified. We propose an operational definition for the unambiguous identification of ISSNHL patients. Patients are identified through suggested definitions, and prevalence and general information are investigated. Methods A retrospective study of patients with ISSNHL was performed using the Health Insurance and Review Assessment-National Patient Sample from 2009 to 2016. To present a new operational definition, a systematic review was conducted for studies on ISSNHL from January 2007 to June 2021. After constructing several operant definitions using the conditions that can specify patients with ISSNHL in big data, we compared each definition to propose an operational definition. Results The important conditions required to classify patients with ISSNHL using big data were the International Classification of Diseases (ICD)-10 code, number of pure tone audiometry (PTA) tests, and whether steroids were prescribed. Among them, those who had undergone PTA tests more than twice could be clearly identified as patients with ISSNHL. Conclusion As the use of big data becomes smoother, research using national medical data is being conducted; however, the results of the studies may vary depending on how a patient with ISSNHL is classified. Clear identification of patients with ISSNHL will be beneficial for better management of this condition.
更多
查看译文
关键词
Epidemiology,Hearing loss,Sensorineural
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要