PROSPECTIVE STUDY ON PROGNOSTIC FACTORS OF SUDDEN SENSORINEURAL HEARING LOSS USING MULTIVARIATE LOGISTIC REGRESSION ANALYSIS IN A TERTIARY CARE CENTRE IN KOLKATA

Dr. Sohini Chakrabarti; Dr. Monoj Mukherjee; Dr. Biswajit Sikder

Volume : X, Issue : VIII, August - 2020

PROSPECTIVE STUDY ON PROGNOSTIC FACTORS OF SUDDEN SENSORINEURAL HEARING LOSS USING MULTIVARIATE LOGISTIC REGRESSION ANALYSIS IN A TERTIARY CARE CENTRE IN KOLKATA

Dr. Sohini Chakrabarti, Dr. Monoj Mukherjee, Dr. Biswajit Sikder

Abstract :

Sudden sensorineural heå loss (SSNHL) is a rare disease of the inner ear (5 to 20 cases per 100,000 persons per year) of unknown cause and unexpected clinical course that leads to heå disability. Sudden sensorineural heå loss (SSNHL) is a symptom of cochlear injury. It is characterized by sudden onset and, within a few hours, reaches its maximum peak. It may be accompanied by vertigo and tinnitus. Many hypotheses have been put forward to explain its etiology: vascular diseases, viral inflammation, trauma, or other lesions. In most cases, the cause of SSNHL cannot be identified. Because of the problematic identification of the cause, many regimens have been used to treat SSNHL. There is probably no other disease for which such a large number of different treatment regimens have been proposed. The prognosis of SSNHL is variously reported as ranging from cured to no change. Thus, determining the prognosis for Sudden Sensorineural Heå Loss is difficult. In previous reports, the prognosis of SSNHL was affected by various factors, such as age, the number of days from onset until presentation, initial heå level, and the presence of vestibular symptoms , the method used for treatment etc. When more than one factor may affect outcome, multivariate analyses, which can adjust for the effects of other factors, are necessary. Logistic regression analysis provides a model for multivariate analyses that can examine at regular intervals the relation between several factors and disease (or all events) after adjusting for the influence of other factors. We used multiple logistic regression analysis to determine which factors are most strongly related to outcome in patients with SSNHL.