Shaia JK, Rock JR, Chu JY
… +5 more, Trinh IP, Alam TA, Singh RP, Talcott KE, Cohen DA
J Neuroophthalmol
· 2025 Feb · PMID 39930564
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BACKGROUND: Fulminant idiopathic intracranial hypertension (IIH) is a rare presentation of IIH characterized by rapid visual loss. Little is known regarding the cause of fulminant IIH and how to treat this condition. We...BACKGROUND: Fulminant idiopathic intracranial hypertension (IIH) is a rare presentation of IIH characterized by rapid visual loss. Little is known regarding the cause of fulminant IIH and how to treat this condition. We conducted an analytical study aimed at characterizing disease, treatment, and visual outcomes while evaluating the time and frequency of recurrence of papilledema. METHODS: This retrospective cohort study used records at a single tertiary institution screened between June 1, 2012, and September 31, 2023, for having an international classification of diseases (ICD) code of IIH or papilledema. After meeting the revised Dandy diagnostic criteria and excluding secondary causes of IIH using clinical review, fulminant IIH was determined by rapid visual loss occurring within 1 month of symptom onset. Demographics, treatment, and visual outcomes data were collected with final visual outcomes reported between 3 months and 1 year after diagnosis. Surgical intervention and relapse of papilledema were noted. All analyses were performed in R Studio and Excel, with a P value <0.05 being significant. RESULTS: Of the 731 IIH patients who were evaluated during this interval, 3.2% had fulminant IIH (n = 24). Patients had a mean age of 27.6 years and presented with an average visual field mean deviation of -19.55 dB. Overall, 95.8% were overweight/obese, 87.5% female, and 50% were Black. In total, 62.5% of patients with fulminant IIH received optic nerve sheath fenestrations. At follow-up, 41.7% of patients were legally blind (with 20/400 vision or worse), including 13% having no light perception. Within our cohort, 42% had a significant visual deficit (visual field mean deviation worse than -7 dB or best-corrected visual acuity between 20/80 and 20/200). Although not statistically significant, patients who underwent cerebral venous sinus stenting had the smallest visual deficit. On average, patients recovered from their initial papilledema after 7.90 months. One quarter (25%) of patients had a recurrence at 4.5 months (SD ± 4.1) after the initial resolution of papilledema. CONCLUSIONS: Fulminant IIH is a rare and blinding variant of IIH. Owing to the recurrence time after initial papilledema resolution, we recommend all such patients be monitored until 8 months after initial papilledema resolution. Future studies should evaluate optimal surgical interventions for preserving vision.
BACKGROUND: In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated...BACKGROUND: In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS: Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS: The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSION S: ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.
BACKGROUND: Patients with craniosynostosis are at high risk of developing elevated intracranial pressure (ICP) causing papilledema and secondary optic atrophy. Diagnosing and monitoring optic neuropathy is challenging be...BACKGROUND: Patients with craniosynostosis are at high risk of developing elevated intracranial pressure (ICP) causing papilledema and secondary optic atrophy. Diagnosing and monitoring optic neuropathy is challenging because of multiple causes of vision loss including exposure keratopathy, amblyopia, and cognitive delays that limit examination. Peripapillary hyperreflective ovoid mass-like structures (PHOMS) are an optical coherence tomography (OCT) finding reported in association with papilledema and optic neuropathy. We investigated the prevalence of PHOMS in patients with syndromic craniosynostosis and the relationship with known risk factors of optic neuropathy. METHODS: This was a cross-sectional retrospective study was performed at Boston Children's Hospital and included 118 eyes (60 patients) with syndromic craniosynostosis that had at least one good quality optic nerve OCT volumetry scan between January 2010 and December 2023. Testing was reviewed by 2 neuro-ophthalmologists to determine the presence of PHOMS. Information collected included demographics, and clinical course including possible Chiari malformation, obstructive sleep apnea (OSA), history of elevated ICP, best-corrected visual acuity (VA), spherical equivalent (SE), retinal nerve fiber layer thickness, macular ganglion cell layer volume, and funduscopic examination. Our primary outcome measure was presence of PHOMS, and secondary outcome measure was the relationship between clinical characteristics and the presence of PHOMS. RESULTS: Mean age at imaged OCT was 9.8 ± 5.2 years and 41/60 (68.3%) patients were female. The prevalence of PHOMS was 17/60 patients (28.3%) or 26/118 eyes (22.0%), higher than previously reported in children without craniosynostosis ( P < 0.001). PHOMS were significantly associated with a history of elevated ICP (odds ratio [OR] 14.4, 95% CI: 1.9-111.0, P < 0.001) and with concurrent papilledema (OR 40.4, 95% CI: 6.6-248.0, P < 0.001). OSA, Chiari malformation, best-corrected VA, craniosynostosis variant, and SE were not independently associated with PHOMS. CONCLUSIONS: Eyes in children with craniosynostosis had a higher prevalence of PHOMS than reported in children without craniosynostosis. PHOMS were significantly more common with a history of elevated ICP and with concurrent papilledema. PHOMS may serve as a clinically useful indicator of optic neuropathy, and of recurrence of papilledema in patients with craniosynostosis and in other populations characterized by multiple sources of vision loss and cognitive changes that limit evaluation.
BACKGROUND: In the aftermath of an adverse event, the first priority is to provide care for the patient, known as the first victim . However, the experiences of healthcare professionals (HCPs) involved in these events, k...BACKGROUND: In the aftermath of an adverse event, the first priority is to provide care for the patient, known as the first victim . However, the experiences of healthcare professionals (HCPs) involved in these events, known as "second victims", have been largely overlooked. This review aims to consolidate existing knowledge on second victim syndrome (SVS), explore its unique implications for neuro-ophthalmologists, and suggest support strategies to increase awareness and meet the needs of affected colleagues. EVIDENCE ACQUISITION: A comprehensive review of the literature was conducted using PubMed, analyzing peer-reviewed articles and reports on SVS across medical specialties. The review focused on the prevalence, risk factors, outcomes, and strategies for addressing SVS at both the individual and system-wide levels. RESULTS: SVS is alarmingly prevalent among HCPs, with estimates ranging from 10% to 43%, and nearly half of all HCPs are expected to experience SVS at least once in their careers. Although direct evidence is lacking, neuro-ophthalmologists may be particularly vulnerable due to the complexity of their patients, frequent diagnostic challenges, and the risk of poor outcomes. Effective coping strategies include peer support at the institutional level, with an urgent need for healthcare systems to transition toward a "Just Culture" that fosters openness and collective learning. CONCLUSIONS: Neuro-ophthalmologists are likely among several specialty fields suffering in silence following medical errors. To reduce these errors and optimize patient care, we must focus on enhancing support for the physicians involved. Sustainable improvements in healthcare require both targeted clinician support and comprehensive system-level changes to foster resilience and improve quality of care. Key efforts should include formal peer support programs, targeted educational initiatives, and a deliberate shift toward a "Just Culture." These initiatives are essential for fostering clinician resilience, promoting effective recovery, and ultimately improving the broader healthcare system and quality of care.
Oertel FC, Zimmermann HG, Motamedi S
… +22 more, Bereuter C, Asseyer ES, Chien C, Marignier R, Cobo-Calvo A, Leocani L, Pisa M, Radaelli M, Villoslada P, Sanchez-Dalmau B, Martinez-Lapiscina EH, Lana-Peixoto MA, Fontenelle MA, Aktas O, Ringelstein M, Albrecht P, Green AJ, Yeaman MR, Smith TJ, Cook L, Paul F, Brandt AU
BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery ir...BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON. METHODS: This was a retrospective study of patients with ON seen at our institution between 2007 and 2022. Fundus photographs (1,599) were retrospectively collected from a total of 321 patients classified into 2 groups: MS ON (262 patients; 1,114 photographs) and non-MS ON (59 patients; 485 photographs). The dataset was divided into training and holdout test sets with an 80%/20% ratio, using stratified sampling to ensure equal representation of MS ON and non-MS ON patients in both sets. Model hyperparameters were tuned using 5-fold cross-validation on the training dataset. The overall performance and generalizability of the model was subsequently evaluated on the holdout test set. RESULTS: The receiver operating characteristic (ROC) curve for the developed model, evaluated on the holdout test dataset, yielded an area under the ROC curve of 0.83 (95% confidence interval [CI], 0.72-0.92). The model attained an accuracy of 76.2% (95% CI, 68.4-83.1), a sensitivity of 74.2% (95% CI, 55.9-87.4) and a specificity of 76.9% (95% CI, 67.6-85.0) in classifying images as non-MS-related ON. CONCLUSIONS: This study provides preliminary evidence supporting a role for AI in differentiating non-MS ON subtypes from MS ON. Future work will aim to increase the size of the dataset and explore the role of combining clinical and paraclinical measures to refine deep learning models over time.
BACKGROUND: Autosomal Dominant Optic Atrophy (ADOA) is a hereditary optic neuropathy characterized by retinal ganglion cell degeneration and optic nerve fiber loss. This study examined the correlation between clinical an...BACKGROUND: Autosomal Dominant Optic Atrophy (ADOA) is a hereditary optic neuropathy characterized by retinal ganglion cell degeneration and optic nerve fiber loss. This study examined the correlation between clinical and structural parameters in patients with ADOA using optical coherence tomography (OCT) and explored potential clinical biomarkers. METHODS: A cross-sectional, case-control observational study included 27 patients with ADOA and 27 age- and sex-matched healthy controls. Clinical examinations, OCT imaging, and OCT angiography (OCTA) were performed. Statistical analyses were conducted to establish correlations between clinical and OCT parameters. RESULTS: Patients with ADOA exhibited gradual bilateral vision loss, central scotomas, and optic disc pallor. Structural OCT analysis revealed significant reductions in central macular thickness, macular volume, ganglion cell complex (GCC), and peripapillary retinal nerve fiber layer compared with controls. Correlation analysis demonstrated associations between worsening clinical parameters (best corrected visual acuity, Sloan Letters Low Contrast Chart 25%, Pseudoisochromatic Test) and increased OCT damage (structural and OCTA). GCC emerged, at least at exploratory terms, as the most important clinical biomarker in patients with ADOA given its multiple positive functional associations, while OCTA parameters correlated with visual field defects. CONCLUSIONS: Our study revealed significant correlations between clinical and structural parameters in patients with ADOA, highlighting the importance of OCT in assessing disease severity. GCC measurement shows promise as a clinical biomarker, aiding in disease monitoring. OCTA parameters offer potential early biomarkers for vascular changes. These findings contribute to understanding ADOA pathophysiology and may improve patient diagnosis and management. Further research is warranted to validate these findings and explore potential therapeutic interventions.