Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder: Onset Following Acute COVID-19 Infection, a Case Series

BACKGROUND AND OBJECTIVES

Acute COVID-19 infection has been associated with neurological involvement. We report a case series of newly diagnosed patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) developed in a short period of time after acute COVID-19 infection.

METHODS

New MS patients developing initial symptoms shortly after an acute COVID-19 infection were diagnosed based on the 2017 McDonald Criteria [Garcia-Ramos et al. in Cells, 2021]. The patients diagnosed with NMOSD met the 2015 International Panel criteria for the diagnosis of NMOSD (IPDN) [Thompson et al. in Lancet Neurol 17:162-173, 2018].

RESULTS FROM THE MS PATIENT GROUP

Ten patients were included who had developed initial MS symptoms after COVID-19 infection. Gender distribution was equal (50% male). The mean age was 28 (range 17-39) years. Average time to neurological presentation was between 2 and 6 weeks following acute COVID-19 infection. Partial transverse myelitis was the initial presentation in 40% of the cases, and 60% of patients had spinal cord lesions present at the moment of diagnosis. All patients showed enhancing lesions on brain magnetic resonance imaging (MRI). The presence of cerebrospinal fluid (CSF) oligoclonal bands was found in all six tested cases. The majority of patients (80%) were unvaccinated for COVID-19. The two vaccinated patients had received two doses of the monovalent COVID-19 messenger ribonucleic acid (mRNA) (Pfizer Biotech) vaccine and no booster, a year prior to contracting COVID-19.

RESULTS FROM THE NMOSD GROUP

Two patients with NMOSD were included. Positive aquoporin-4 protein antibody (AQP-4 Ab) was detected in serum in both cases [one Enzyme Linked immunosorbent assay (ELISA) and one cell based]. Both patients had mild COVID-19 infection prior to presentation, initial neurologic symptoms presented between 3 and 6 weeks after COVID-19 infection. Neither patients were vaccinated. Both responded partially to steroids, one developed a relapse 40 days after diagnosis.

CONCLUSION

COVID-19 infection has been linked to several neurological and immune-driven conditions. This study suggests that in susceptible individuals, acute COVID-19 infection may act as a trigger for developing MS and NMOSD.

Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography/Angiography and Machine Learning Diagnostic Models

Purpose

To establish optical coherence tomography (OCT)/angiography (OCTA) parameter ranges for healthy eyes (HE) and glaucomatous eyes (GE) for a North Texas based population; to develop a machine learning (ML) tool and to identify the most accurate diagnostic parameters for clinical glaucoma diagnosis.

Patients and Methods

In this retrospective cross-sectional study, we included 1371 eligible eyes, 462 HE and 909 GE (377 ocular hypertension, 160 mild, 156 moderate, 216 severe), from 735 subjects. Demographic data and full OCTA parameters were collected. A Kruskal-Wallis test was used to produce the normative database. Models were trained to solve a two-class problem (HE vs GE) and four-class problem (HE vs mild vs moderate vs severe GE). A rigorous nested, stratified, group, 5×10 fold cross-validation strategy was applied to partition the data. Six ML algorithms were compared using classical and deep learning approaches. Over 2500 ML models were optimized using random search, with performance compared using mean validation accuracy. Final performance was reported on held-out test data using accuracy and F1 score. Decision trees and feature importance were produced for the final model.

Results

We found differences across glaucoma severities for age, gender, hypertension, Black and Asian race, and all OCTA parameters, except foveal avascular zone area and perimeter (p<0.05). The XGBoost algorithm achieved the highest test performance for both the two-class (F1 score 83.8%; accuracy 83.9%; standard deviation 0.03%) and four-class (F1 score 62.4%; accuracy 71.3%; standard deviation 0.013%) problem. A set of interpretable decision trees provided the most important predictors of the final model; inferior temporal and inferior hemisphere vessel density and peripapillary retinal nerve fiber layer thickness were identified as key diagnostic parameters.

Conclusion

This study established a normative database for our North Texas based population and created ML tools utilizing OCT/A that may aid clinicians in glaucoma management.

Predictors of Early Onset Glaucoma

Purpose/Relevance

To determine the influence of hypertension (HTN), type 2 diabetes (DM2), migraine, and obstructive sleep apnea (OSA) on the onset of primary open-angle glaucoma (POAG) to enhance predictive accuracy.

Methods

In this cross-sectional study, data for 389 eligible patients with POAG were collected through medical records review and phone surveys. All data were assessed collectively using stepwise multiple regression analysis to determine the relative contribution to age at POAG diagnosis. We used the following groups, based on age at diagnosis, HTN for patients with or without DM2 (model 1), HTN for patients with DM2 (model 2), DM2 for patients with or without HTN (model 3), and DM2 for patients with HTN (model 4).

Results

In model 1, age at HTN diagnosis was associated with age at POAG diagnosis (β = 0.14; 95% CI, 0.01–0.26, p = 0.04). In model 2, age at HTN diagnosis was not associated with age at POAG diagnosis (p > 0.05). In model 3, age at DM2 diagnosis was associated with age at POAG diagnosis (β = 0.37; 95% CI 0.16–0.58, p = 0.001). In model 4, age at DM2 diagnosis was associated with age at POAG diagnosis (β = 0.40; 95% CI 0.00–0.15, p = 0.003). Asian race/ethnicity was associated with early onset of POAG in model 3 (β = −6.44; 95% CI −12.34–0.54, p = 0.033). OSA and migraine did not influence the onset of POAG.

Conclusion

Our study found that the diagnosis of DM2 and HTN at an earlier age is associated with the early onset of POAG.