Application of the International Interocular Difference Thresholds into Practice: Localising the Patient Experience.

Demyelinating diseases of the central nervous system (CNS) often have neuro-ophthalmological manifestations, and retinal examination can be helpful in making the diagnosis. The latest iteration of optical coherence tomography (OCT)-based criteria for optic neuritis in multiple sclerosis has been developed in the research realm, but its application to clinical practice, and to the more uncommon demyelinating diseases requires further study. The ability to use OCT data to distinguish between various CNS demyelinating disorders could provide additional paraclinical tools to accurately diagnose patients. Furthermore, neuro-ophthalmological testing can define the extent of inflammatory damage in the CNS, independent of patient-reported history. New referrals for OCT at a tertiary multiple sclerosis and neuro-immunology referral centre (n = 167) were analysed retrospectively for the self-reporting of optic neuritis, serological test results, and diagnosis. Only approximately 30% of patients with a clinical history of unilateral optic neuritis solely had a unilateral optic neuropathy, nearly 40% of those subjects actually having evidence of bilateral optic neuropathies. Roughly 30% of patients reporting a history of bilateral optic neuritis did not have any evidence of structural disease, with 20% of these patients having a separate, intervenable diagnosis noted on macular scans. OCT is a useful adjunct diagnostic tool in the evaluation of demyelinating disease and has the ability to aid in a more accurate diagnosis for patients. Application of the international interocular difference thresholds to a clinical patient population generally reproduces the original results, emphasising their appropriateness. The analysis distinguishing the demyelinating diseases needs to be replicated in a blinded, multi-centre setting.

Generative artificial intelligence versus clinicians: Who diagnoses multiple sclerosis faster and with greater accuracy?

BACKGROUND: Those receiving the diagnosis of multiple sclerosis (MS) over the next ten years will predominantly be part of Generation Z (Gen Z). Recent observations within our clinic suggest that younger people with MS utilize online generative artificial intelligence (AI) platforms for personalized medical advice prior to their first visit with a specialist in neuroimmunology. The use of such platforms is anticipated to increase given the technology driven nature, desire for instant communication, and cost-conscious nature of Gen Z. Our objective was to determine if ChatGPT (Generative Pre-trained Transformer) could diagnose MS in individuals earlier than their clinical timeline, and to assess if the accuracy differed based on age, sex, and race/ethnicity. METHODS: People with MS between 18 and 59 years of age were studied. The clinical timeline for people diagnosed with MS was retrospectively identified and simulated using ChatGPT-3.5 (GPT-3.5). Chats were conducted using both actual and derivatives of their age, sex, and race/ethnicity to test diagnostic accuracy. A Kaplan-Meier survival curve was estimated for time to diagnosis, clustered by subject. The p-value testing for differences in time to diagnosis was accomplished using a general Wilcoxon test. Logistic regression (subject-specific intercept) was used to capture intra-subject correlation to test the accuracy prior to and after the inclusion of MRI data. RESULTS: The study cohort included 100 unique people with MS. Of those, 50 were members of Gen Z (38 female; 22 White; mean age at first symptom was 20.6 years (y) (standard deviation (SD)=2.2y)), and 50 were non-Gen Z (34 female; 27 White; mean age at first symptom was 37.0y (SD=10.4y)). In addition, a total of 529 people that represented digital simulations of the original cohort of 100 people (333 female; 166 White; 136 Black/African American; 107 Asian; 120 Hispanic, mean age at first symptom was 31.6y (SD=12.4y)) were generated allowing for 629 scripted conversations to be analyzed. The estimated median time to diagnosis in clinic was significantly longer at 0.35y (95% CI=[0.28, 0.48]) versus that by ChatGPT at 0.08y (95% CI=[0.04, 0.24]) (p<0.0001). There was no difference in the diagnostic accuracy between ages and by race/ethnicity prior to the inclusion of MRI data. However, prior to including the MRI data, males had a 47% less likely chance of a correct diagnosis relative to females (p=0.05). Post-MRI data inclusion within GPT-3.5, the odds of an accurate diagnosis was 4.0-fold greater for Gen Z participants, relative to non-Gen Z participants (p=0.01) with the diagnostic accuracy being 68% less in males relative to females (p=0.009), and 75% less for White subjects, relative to non-White subjects (p=0.0004). CONCLUSION: Although generative AI platforms enable rapid information access and are not principally designed for use in healthcare, an increase in use by Gen Z is anticipated. However, the obtained responses may not be generalizable to all users and bias may exist in select groups.

Disease associations of excessive daytime sleepiness in multiple sclerosis: A prospective study.

Background: Excessive daytime sleepiness (EDS) in multiple sclerosis (MS) can be a significant source of disability. Despite this, its prevalence as a patient-reported outcome in this condition has not been well established, and its causes are not well understood. Methods: We prospectively assessed EDS as part of an observational study for patients referred for diagnostic neuro-ophthalmological testing. EDS was evaluated by the Epworth Sleepiness Scale (ESS), and visual data were also collected as part of a research protocol. Analysis with patient data was performed following the exclusion of patients with known primary sleep disorders. Results: A total of 69 patients with MS were included in the analysis. The mean ESS was 6.5 with a SD of 4.3. ESS ≥ 10 was present in 23% of the cohort even in the presence of minimal mean neurological disability (Patient Determined Disease Steps (PDDS) = 1.5). The ESS score was not associated with age, sex, disease-related disability, retinal nerve fiber layer (RNFL), or optic neuritis (ON), but displayed an association with visual dysfunction. Conclusions: There is an increased prevalence of EDS in MS. The increased values of the ESS are not explained by other sleep disorders, suggesting separate mechanisms. Further study of the underlying mechanisms is warranted.