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Updated: Jul 9, 2026
Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss
Published on: January 25, 2016
Predictors of Hearing Recovery in Sudden Sensorineural Hearing Loss: Focus on Lipids, Glucocorticoids, and Predictive
1The Third Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Objective:
To explore the key factors influencing hearing recovery in patients with sudden sensorineural hearing loss (SSNHL) and provide a basis for clinical prognosis evaluation and individualized treatment.
Methods:
Clinical data of 164 inpatients with SSNHL at a tertiary hospital in Fujian Province from January 2022 to December 2024 were retrospectively analyzed, including basic characteristics, hearing features, examination findings, and treatment measures. Univariate analyses (Chi-square test, Kruskal-Wallis H test) and a multivariate logistic regression model were used to identify prognostic factors, and a neural network model was constructed to evaluate its predictive performance.
Results:
Among the 164 patients, 27 (16.46%) showed no response, 61 (37.20%) responded, 32 (19.51%) showed marked response, and 44 (26.83%) were cured. Univariate analysis revealed that age, length of hospital stay, degree of hearing loss, type of hearing loss, brain MRI findings, triglyceride (TG) levels, low-density lipoprotein (LDL-C) levels, and the use of glucocorticoids and batroxobin were associated with treatment outcomes (all P < .05). Multivariate logistic regression indicated that TG (OR = 0.559, P = .002) and LDL-C (OR = 0.352, P = .001) were independent risk factors, while glucocorticoid use was a protective factor (OR = 8.564, P = .004). The neural network model exhibited good predictive performance (AUC = 0.889, accuracy rate 86.5%).
Conclusion:
Advanced age, severe hearing loss (especially total deafness), abnormal brain MRI findings, and elevated TG and LDL-C levels may lead to poor prognosis in SSNHL, while early use of glucocorticoids can improve outcomes. The neural network model can effectively evaluate patients' potential for hearing recovery.
