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The development of new and more sensitive clinical outcome measures for research in multiple sclerosis (MS) has been fueled by the development of effective therapies. As such, active arm comparison studies that require more sensitive clinical outcome measures are now commonplace. The Kurtzke Expanded Disability Status Scale (EDSS), the most widely used measure of neurologic impairment in MS, is particularly designed for classifying patients with respect to disease severity but has been criticized for its noninterval scaling, emphasis on ambulation status, relatively reduced sensitivity in the mid and upper ranges of scores, and absence of adequate cognitive and visual components. In response to perceived difficulties with the EDSS, the National Multiple Sclerosis Society Clinical Outcomes Assessment Task Force has developed the Multiple Sclerosis Functional Composite (MSFC). The MSFC includes three components that yield objective and quantitative results: 1) the timed 25-ft walk, 2) the nine-hole peg test, and 3) the 3-second paced auditory serial addition test. This scale has the advantages of continuous scoring with a composite Z score, standardized protocols, and high degrees of reliability and validity. Candidate visual function outcome measures for the MSFC, including the low-contrast Sloan letter chart, are currently under investigation. In addition to measures of neurologic impairment, health-related quality of life (HRQOL) measures have gained increasing importance as clinical trial outcome measures. The MS Quality of Life Inventory, a disease-specific HRQOL measure, has been developed to capture self-reported neurologic dysfunction and the impact of MS upon activities of daily living. MS clinical trials of the future, particularly active-arm comparison studies, will require more sensitive clinical outcome measures such as the MSFC. Measures of visual function and HRQOL should also be incorporated to capture the broad scope of neurologic impairment and disability in MS populations. [ABSTRACT FROM AUTHOR]

SUMMARY: A 47-year-old woman presented with headache, acute monocular vision loss, and ipsilateral Horner syndrome. Apart from the optic neuropathy, all cranial nerve function was intact. Magnetic resonance imaging revealed an enlarged pituitary gland with compression of the orbital apex. The surgical specimen was consistent with pituitary apoplexy. The combination of headache, acute visual loss, and ipsilateral Horner syndrome without ophthalmoplegia, which may suggest carotid artery dissection, is evidently an unusual manifestation of pituitary apoplexy. [ABSTRACT FROM AUTHOR]

Background Prior to the COVID-19 pandemic, about half of patients from populations that sought care in neurology tried complementary and integrative therapies (CITs). With the increased utilization of telehealth services, we sought to determine whether patients also increased their use of virtual CITs. Methods We examined datasets from two separate cross-sectional surveys that included cohorts of patients with neurological disorders. One was a dataset from a study that examined patient and provider experiences with teleneurology visits; the other was a study that assessed patients with a history of COVID-19 infection who presented for neurologic evaluation. We assessed and reported the use of virtual (and non-virtual) CITs using descriptive statistics, and determined whether there were clinical characteristics that predicted the use of CITs using logistic regression analyses. Findings Patients who postponed medical treatment for non-COVID-19-related problems during the pandemic were more likely to seek CITs. Virtual exercise, virtual psychotherapy, and relaxation/meditation smartphone applications were the most frequent types of virtual CITs chosen by patients. In both studies, age was a key demographic factor associated with mobile/virtual CIT usage. Interpretations Our investigation demonstrates that virtual CIT-related technologies were utilized in the treatment of neurologic conditions during the pandemic, particularly by those patients who deferred non-COVID-related care.

BackgroundPost-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management.MethodsIn a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL).ResultsAmong 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1-5) lasting a median of 12-months (range 1-15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1-5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1-59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3-7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all PConclusionsWe identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.

Background: Vision encompasses a large component of the brain's pathways, yet is not represented in current sideline testing. Objectives: We performed a meta-analysis of published data for a vision-based test of rapid number naming (King-Devick [K-D] test). Studies & methods: Pooled and meta-analysis of 15 studies estimated preseason baseline K-D scores and sensitivity/specificity for identifying concussed versus nonconcussed control athletes. Result: Baseline K-D (n = 1419) showed a weighted estimate of 43.8 s (95% CI: 40.2, 47.5; I2 = 0.0%; p=0.85 – indicating very little heterogeneity). Sensitivity was 86% (96/112 concussed athletes had K-D worsening; 95% CI: 78%, 92%); specificity was 90% (181/202 controls had no worsening; 95% CI: 85%, 93%). Conclusion: Rapid number naming adds to sideline assessment and contributes a critical dimension of vision to sports-related concussion testing.

Background: Vision encompasses a large component of the brain's pathways, yet is not represented in current sideline testing. Objectives: We performed a meta-analysis of published data for a vision...

Optical coherence tomography (OCT) derived retinal measures, particularly peri-papillary retinal nerve fiber layer (RNFL) thickness, have been proposed as outcome measures in remyelinating and neuroprotective trials in multiple sclerosis (MS). With increasing utilization of multiple centers to improve power, elucidation of the impact of different OCT technologies is crucial to the design and interpretation of such studies. In this study, we assessed relation and agreement between RNFL thickness and total macular volume (in MS and healthy controls) derived from three commonly used OCT devices: Stratus time-domain OCT, and Cirrus HD-OCT and Spectralis, two spectral-domain (SD) OCT devices. OCT was performed on both Cirrus HD-OCT and Stratus in 229 participants and on both Cirrus HD-OCT and Spectralis in a separate cohort of 102 participants. Pearson correlation and Bland-Altman analyses were used to assess correlation and agreement between devices. All OCT retinal measures correlated highly between devices. The mean RNFL thickness was 7.4 µm lower on Cirrus HD-OCT than Stratus, indicating overall poor agreement for this measurement between these machines. Further, the limits of agreement (LOA) between Cirrus HD-OCT and Stratus were wide (-4.1 to 18.9 µm), indicating poor agreement at an individual subject level. The mean RNFL thickness was 1.94 µm (LOA: -5.74 to 9.62 µm) higher on Spectralis compared to Cirrus HD-OCT, indicating excellent agreement for this measurement across this cohort. Although these data indicate that these three devices agree poorly at an individual subject level (evidenced by wide LOA in both study cohorts) precluding their co-utilization in everyday practice, the small difference for mean measurements between Cirrus HD-OCT and Spectralis indicate pooled results from these two SD-devices could be used as outcome measures in clinical trials, provided patients are scanned on the same machine throughout the trial, similar to the utilization of multiple different MRI platforms in MS clinical trials.

Background and Objectives: Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history., Methods: Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT., Results: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model ( p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models., Discussion: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications., Classification of Evidence: This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria., (© 2022 American Academy of Neurology.)