Red ear syndrome: a case series and review of the literature
Abstract
BACKGROUND: Red Ear Syndrome is a burning sensation and erythema of the ear, associated with a various number of disorders including migraine, trigeminal neuralgia, autoimmune disorders etc. Theories for RES pathophysiology have developed from current understandings of comorbid conditions. Characterizing the underlying mechanism of RES is crucial for defining effective treatments.
CASE PRESENTATION: Three caucasian patients, ages 15, 47, and 67 years, with migraine, one with erythromelalgia are reported in this manuscript. RES pathophysiology is not fully understood due to its variable clinical presentation and numerous comorbid conditions, making it difficult to identify effective treatments.
CONCLUSION: RES seems to be largely treatment-resistant, and most options involve treating the associated disorders and minimizing pain. Further investigation of future cases should lead to a more comprehensive understanding of the fundamental cause of RES and, hopefully, successful treatments.
How strong is the evidence?
Evidence Hub reads this as a case report, done in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.