Functional visual loss: a systematic review and meta-analysis of epidemiology, prognosis and treatment
Abstract
Functional visual loss is a common issue in neuro-ophthalmology settings. There are many studies focused on diagnosis, but there is limited literature on associated epidemiology, phenomenology, prognosis and treatment. We carried out the first systematic review and meta-analysis assessing these domains. We systematically reviewed PubMed from 1951 for studies reporting clinical data on adults and children with functional visual loss. Data on variables relevant to epidemiology, phenomenology, comorbidity, risk factors, prognosis and treatment were extracted. Random effects meta-analyses were performed where appropriate. Forty-four studies of functional visual loss (n = 2284, 67% female) were included. The pooled median age was 43 years (IQR 12.5-43 years). Common associations included facial/ocular trauma (19%, n = 288), ocular comorbidity (25%, n = 470), headache (39%, n = 124) and other functional symptom/disorder (10-29%). Good quality data on psychiatric comorbidity and adverse childhood experience were unavailable. Prognosis data (n = 1019, 13 studies) were heterogeneous, but outcome appeared more favourable in paediatric studies. Most studies did not distinguish between symptomatic visual loss and functional visual signs which are asymptomatic (such as spiralled fields) or disproportionate to symptoms (such as severe acuity loss in someone with minor visual symptoms). There are very limited data on treatment. This systematic review highlights some basic epidemiological…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 2,284 participants or records.
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.