ArticleJournal of current glaucoma practice
Neuro-opthalmic Conditions Misdiagnosed as Glaucoma: A Retrospective Study of Cases and Contributing Factors in a Developing Setting.
Article in Journal of current glaucoma practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Purpose: The aims are to study the prevalence, causes, and challenges of neuro-ophthalmic disorders being misdiagnosed as glaucoma in a tertiary care setting and to identify strategies for improving diagnostic skills in resource-constrained settings. Design: Single-center, retrospective, cross-sectional study over a period of 6 months, that is, July-December 2024, at a tertiary care hospital in Central India. Methodology: Medical records of 53 patients who presented to the glaucoma clinic during the study period were reviewed. All of them had already been diagnosed with glaucoma at other facilities. Seventeen such patients, who were later established to have nonglaucomatous (neuro-ophthalmic) conditions, were analyzed. Patients who were proven to have either primary or secondary glaucoma after comprehensive ophthalmic and neuroimaging evaluation were excluded. The demographic data, clinical findings, investigation results, and causes of misdiagnosis were analyzed. Results: The proportion of misdiagnosis was 32% (17/53). Compressive optic neuropathy was the most common mimicker, followed by demyelinating optic neuropathy at 23.5% and ischemic optic neuropathy at 17.6%. Major contributing factors to diagnostic error included falsely elevated intraocular pressure (IOP), optic disk cupping with pallor, family history of glaucoma, and lack of access to central corneal thickness assessment, visual field analysis, and neuroimaging. Visual field analysis and magnetic resonance imaging (MRI) were key investigations in diagnosis. Conclusion: Neuro-ophthalmic conditions mistaken for glaucoma lead to delayed and inappropriate management. The introduction of structured diagnostic algorithms, multimodal imaging, and clinician training, supported by telemedicine and artificial intelligence (AI)-assisted tools, may lead to a significant reduction in misdiagnosis and promote earlier diagnosis, especially in resource-constrained healthcare settings.
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