Inherited Optic Neuropathies: Real-World Experience in the Paediatric Neuro-Ophthalmology Clinic.
Michael James GilhooleyNaz RaoofPatrick Yu-Wai-ManMariya MoosajeePublished in: Genes (2024)
Inherited optic neuropathies affect around 1 in 10,000 people in England; in these conditions, vision is lost as retinal ganglion cells lose function or die (usually due to pathological variants in genes concerned with mitochondrial function). Emerging gene therapies for these conditions have emphasised the importance of early and expedient molecular diagnoses, particularly in the paediatric population. Here, we report our real-world clinical experience of such a population, exploring which children presented with the condition, how they were investigated and the time taken for a molecular diagnosis to be reached. A retrospective case-note review of paediatric inherited optic neuropathy patients (0-16 years) in the tertiary neuro-ophthalmology service at Moorfields Eye Hospital between 2016 and 2020 identified 19 patients. Their mean age was 9.3 ± 4.6 (mean ± SD) years at presentation; 68% were male, and 32% were female; and 26% had comorbidities, with diversity of ethnicity. Most patients had undergone genetic testing (95% ( n = 18)), of whom 43% ( n = 8) received a molecular diagnosis. On average, this took 54.8 ± 19.5 weeks from presentation. A cerebral MRI was performed in 70% ( n = 14) and blood testing in 75% ( n = 15) of patients as part of their workup. Continual improvement in the investigative pathways for inherited optic neuropathies will be paramount as novel therapeutics become available.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- healthcare
- intensive care unit
- peritoneal dialysis
- genome wide
- primary care
- magnetic resonance
- young adults
- magnetic resonance imaging
- induced apoptosis
- oxidative stress
- optical coherence tomography
- single molecule
- artificial intelligence
- signaling pathway
- blood brain barrier
- endoplasmic reticulum stress
- deep learning
- acute care