Faculty Bibliography

BACKGROUND:There is paucity of data on long-term functional and cognitive outcomes after COVID-19 compared to COVID-negative controls. METHODS:We conducted an observational cohort study of patients ≥ 1 year after COVID-19 compared to contemporaneous COVID-19 negative controls (SARS-CoV-2 nucleocapsid IgG negative with no history of COVID-19). Functional (modified Rankin Scale [mRS], Barthel Index), cognitive (telephone MoCA [t-MoCA]), and patient-reported neuropsychiatric symptoms were compared between groups using multivariable logistic regression analysis. In a subgroup of COVID-19 patients who were followed longitudinally, trajectories of recovery were assessed using the paired samples Sign test. RESULTS:Of 145 participants, N = 115 COVID-19 patients (median age 62, 51 % female, 33 % hospitalized for COVID-19, median 2.9 years from index infection), and N = 30 non-COVID-19 controls (median age 75, 70 % female) were enrolled. Neuropsychiatric symptoms were reported in 76 % of COVID-19 patients versus 7 % of controls (aOR 15.0, 95 %CI 3.09-72.47, P < 0.001). Abnormal mRS> 0 occurred in 42 % of COVID-19 patients compared to 11 % of controls (P = 0.002). However, this difference was not significant after adjusting for age, sex, COVID-19 hospitalization and history of mood disorder (aOR 2.10, 95 %CI 0.52-8.51). Rates of abnormal t-MoCA≤ 18 (40 % of COVID-19 versus 41 % of controls, P = 1.00) and Barthel scores< 100 (19 % of COVID-19 versus 14 % in controls, P = 0.785) were similar. Among N = 26 COVID-19 patients with repeated measures, mRS significantly improved between 6-months to 3-years post-COVID (+1.3 points, p = 0.004), while no changes were observed in t-MoCA or Barthel. CONCLUSIONS:Three years after COVID-19, neuropsychiatric symptoms were significantly more frequent compared to controls, however no differences in functional or cognitive status were detected.

BACKGROUND AND OBJECTIVES/OBJECTIVE:While reductions in optical coherence tomography (OCT) pRNFL and ganglion cell-inner plexiform layer thicknesses have been shown to be associated with brain atrophy in adult-onset MS (AOMS) cohorts, the relationship between OCT and brain MRI measures is less established in pediatric-onset MS (POMS). Our aim was to examine the associations of OCT measures with volumetric MRI in a cohort of patients with POMS to determine whether OCT measures reflect CNS neurodegeneration in this patient population, as is seen in AOMS cohorts. METHODS:This was a cross-sectional study with retrospective ascertainment of patients with POMS evaluated at a single center with expertise in POMS and neuro-ophthalmology. As part of routine clinical care, patients with POMS are evaluated by a POMS expert and undergo volumetric brain MRI, including whole-brain (WB), subregional, and gray matter (GM) volume analyses. Patients with POMS are routinely referred to neuro-ophthalmology for evaluation that includes high-contrast visual acuity, color vision testing, and OCT. Generalized estimating equation (GEE) models, accounting for within-patient, intereye correlations (both eyes of each patient were included), MS disease duration, and disease-modifying therapy efficacy, were used to determine the relationship between visual pathway structure and function and volumetric MRI measures. RESULTS:= 0.015, respectively). DISCUSSION/CONCLUSIONS:Our results demonstrate that changes in visual pathway structures are associated with reductions in overall brain volume and GM volumes, as well as greater lesion and black hole burden. Collectively, our results emphasize the importance of visual assessment in POMS and suggest that OCT reflects overall CNS neurodegeneration in this cohort.

BACKGROUND AND OBJECTIVES/UNASSIGNED:Burnout is a pervasive occupational hazard for neurologists-undermining their well-being, jeopardizing patient safety and satisfaction, limiting access to care, and inflating health care costs. Well-designed appreciation and recognition practices may help mitigate some of its key drivers. This pilot study evaluates faculty perspectives on appreciation strategies in an academic neurology department. We used the Moffitt Provider Appreciation Assessment (MPAA), which assesses the types of appreciation methods respondents value, regardless of whether those practices are currently implemented in their workplace. METHODS/UNASSIGNED:A cross-sectional survey was conducted among full-time clinical faculty in the Department of Neurology at NYU Grossman School of Medicine. The survey included demographics, the MPAA, the single-item Mini-Z burnout inventory to assess self-reported burnout levels, and an intent-to-leave question. MPAA responses were analyzed for frequencies, and the association between burnout and intent to leave was examined. RESULTS/UNASSIGNED:< 0.00001). Because the scores for self-reported burnout and intent to leave reflect current work conditions while MPAA scores capture enduring personal values, MPAA rankings cannot be compared directly with burnout or turnover metrics. DISCUSSION/UNASSIGNED:Neurology clinical faculty prioritized appreciation methods that directly address clinical work, underscoring the value of implementing tailored recognition practices that may reduce burnout. The methodology used in this pilot study can be adapted for broader application in other settings. After identifying faculty preferences, health care organizations can implement meaningful, transparent, and inclusive appreciation strategies that have the potential to strengthen physician relationships, promote well-being, and support a sustainable workforce.

Effective mild cognitive impairment (MCI) screening requires accessible testing. This study compared two tests for distinguishing MCI patients from controls: Rapid Automatized Naming (RAN) for naming speed and Low Contrast Letter Acuity (LCLA) for sensitivity to low contrast letters. Two RAN tasks were used: the Mobile Universal Lexicon Evaluation System (MULES, picture naming) and Staggered Uneven Number test (SUN, number naming). Both RAN tasks were administered on a tablet and in a paper/pencil format. The tablet format was administered using the Mobile Integrated Cognitive Kit (MICK) application. LCLA was tested at 2.5% and 1.25% contrast. Sixty-four participants (31 MCI, 34 controls; mean age 73.2 ± 6.8 years) were included. MCI patients were slower than controls for paper/pencil (75.0 vs. 53.6 sec, p < 0.001), and tablet MULES (69.0 sec vs. 50.2 sec, p = 0.01). The paper/pencil SUN showed no significant difference (MCI: 59.5 sec vs. controls: 59.9 sec, p = 0.07), nor did tablet SUN (MCI: 59.3 sec vs. controls: 55.7 sec, p = 0.36). MCI patients had worse performance on LCLA testing at 2.5% contrast (33 letters vs. 36, p = 0.04*) and 1.25% (0 letters vs. 14. letters, p < 0.001). Receiver operating characteristic (ROC) analysis showed similar performance of paper/pencil and tablet MULES in distinguishing MCI from controls (AUC = 0.77), outperforming both SUN (AUC = 0.63 paper, 0.59 tablet) and LCLA (2.5% contrast: AUC = 0.65, 1.25% contrast: AUC = 0.72). The MULES, in both formats, may be a valuable screening tool for MCI.

RGS8-associated paraneoplastic neurologic syndrome (PNS) is a recently-described disorder associated with lymphomas and typically presenting with severe, rapidly-progressing cerebellar dysfunction. We describe a patient who presented with mild signs of cerebellar dysfunction, including ocular motor abnormalities and impaired tandem gait. CSF showed elevated protein and a neural-restricted antibody pattern. Mesenteric lymphadenopathy on abdominal CT was biopsied and diagnosed as follicular B-cell lymphoma. After four years, the previously-detected antibody pattern was identified as RGS8 antibodies. This case describes the first RGS8-PNS patient presenting with a subtle and ocular motor predominant cerebellar syndrome with low-grade lymphoma.

BACKGROUND:Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury. METHODS:We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing. RESULTS:Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES (P < 0.001, rs = -0.67) and SUN (P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age (P > 0.05, rs = -0.2). CONCLUSIONS:Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age.

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. CONCLUSIONS: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.