Faculty Bibliography

BACKGROUND:Persistent cognitive symptoms have been reported following COVID-19 hospitalization. We investigated the relationship between demographics, social determinants of health (SDOH) and cognitive outcomes 6-months after hospitalization for COVID-19. METHODS:We analyzed 6-month follow-up data collected from a multi-center, prospective study of hospitalized COVID-19 patients. Demographic and SDOH variables (age, race/ethnicity, education, employment, health insurance status, median income, primary language, living arrangements, and pre-COVID disability) were compared between patients with normal versus abnormal telephone Montreal Cognitive Assessments (t-MOCA; scores<18/22). Multivariable logistic regression models were constructed to evaluate predictors of t-MoCA. RESULTS:Of 382 patients available for 6-month follow-up, 215 (56%) completed the t-MoCA (n = 109/215 [51%] had normal and n = 106/215 [49%] abnormal results). 14/215 (7%) patients had a prior history of dementia/cognitive impairment. Significant univariate predictors of abnormal t-MoCA included older age, ≤12 years of education, unemployment pre-COVID, Black race, and a pre-COVID history of cognitive impairment (all p < 0.05). In multivariable analyses, education ≤12 years (adjusted OR 5.21, 95%CI 2.25-12.09), Black race (aOR 5.54, 95%CI 2.25-13.66), and the interaction of baseline functional status and unemployment prior to hospitalization (aOR 3.98, 95%CI 1.23-12.92) were significantly associated with abnormal t-MoCA scores after adjusting for age, history of dementia, language, neurological complications, income and discharge disposition. CONCLUSIONS:Fewer years of education, Black race and unemployment with baseline disability were associated with abnormal t-MoCA scores 6-months post-hospitalization for COVID-19. These associations may be due to undiagnosed baseline cognitive dysfunction, implicit biases of the t-MoCA, other unmeasured SDOH or biological effects of SARS-CoV-2.

INTRODUCTION/BACKGROUND:Neurological complications among hospitalized COVID-19 patients may be associated with elevated neurodegenerative biomarkers. METHODS:Among hospitalized COVID-19 patients without a history of dementia (N = 251), we compared serum total tau (t-tau), phosphorylated tau-181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), ubiquitin carboxy-terminal hydrolase L1 (UCHL1), and amyloid beta (Aβ40,42) between patients with or without encephalopathy, in-hospital death versus survival, and discharge home versus other dispositions. COVID-19 patient biomarker levels were also compared to non-COVID cognitively normal, mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia controls (N = 161). RESULTS:Admission t-tau, p-tau181, GFAP, and NfL were significantly elevated in patients with encephalopathy and in those who died in-hospital, while t-tau, GFAP, and NfL were significantly lower in those discharged home. These markers correlated with severity of COVID illness. NfL, GFAP, and UCHL1 were higher in COVID patients than in non-COVID controls with MCI or AD. DISCUSSION/CONCLUSIONS:Neurodegenerative biomarkers were elevated to levels observed in AD dementia and associated with encephalopathy and worse outcomes among hospitalized COVID-19 patients.

BACKGROUND:Visual tests in Alzheimer disease (AD) have been examined over the last several decades to identify a sensitive and noninvasive marker of the disease. Rapid automatized naming (RAN) tasks have shown promise for detecting prodromal AD or mild cognitive impairment (MCI). The purpose of this investigation was to determine the capacity for new rapid image and number naming tests and other measures of visual pathway structure and function to distinguish individuals with MCI due to AD from those with normal aging and cognition. The relation of these tests to vision-specific quality of life scores was also examined in this pilot study. METHODS:Participants with MCI due to AD and controls from well-characterized NYU research and clinical cohorts performed high and low-contrast letter acuity (LCLA) testing, as well as RAN using the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number test, and vision-specific quality of life scales, including the 25-Item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25) and 10-Item Neuro-Ophthalmic Supplement. Individuals also underwent optical coherence tomography scans to assess peripapillary retinal nerve fiber layer and ganglion cell/inner plexiform layer thicknesses. Hippocampal atrophy on brain MRI was also determined from the participants' Alzheimer disease research center or clinical data. RESULTS:Participants with MCI (n = 14) had worse binocular LCLA at 1.25% contrast compared with controls (P = 0.009) and longer (worse) MULES test times (P = 0.006) with more errors in naming images (P = 0.009) compared with controls (n = 16). These were the only significantly different visual tests between groups. MULES test times (area under the receiver operating characteristic curve [AUC] = 0.79), MULES errors (AUC = 0.78), and binocular 1.25% LCLA (AUC = 0.78) showed good diagnostic accuracy for distinguishing MCI from controls. A combination of the MULES score and 1.25% LCLA demonstrated the greatest capacity to distinguish (AUC = 0.87). These visual measures were better predictors of MCI vs control status than the presence of hippocampal atrophy on brain MRI in this cohort. A greater number of MULES test errors (rs = -0.50, P = 0.005) and worse 1.25% LCLA scores (rs = 0.39, P = 0.03) were associated with lower (worse) NEI-VFQ-25 scores. CONCLUSIONS:Rapid image naming (MULES) and LCLA are able to distinguish MCI due to AD from normal aging and reflect vision-specific quality of life. Larger studies will determine how these easily administered tests may identify patients at risk for AD and serve as measures in disease-modifying therapy clinical trials.

OBJECTIVE:To develop evidence-informed, expert consensus research diagnostic criteria for traumatic encephalopathy syndrome (TES), the clinical disorder associated with neuropathologically diagnosed chronic traumatic encephalopathy (CTE). METHODS:April, 2019. Before consensus, panelists reviewed evidence from all published cases of CTE with neuropathologic confirmation, and they examined the predictive validity data on clinical features in relation to CTE pathology from a large clinicopathologic study (n = 298). RESULTS:Consensus was achieved in 4 rounds of the Delphi procedure. Diagnosis of TES requires (1) substantial exposure to repetitive head impacts (RHIs) from contact sports, military service, or other causes; (2) core clinical features of cognitive impairment (in episodic memory and/or executive functioning) and/or neurobehavioral dysregulation; (3) a progressive course; and (4) that the clinical features are not fully accounted for by any other neurologic, psychiatric, or medical conditions. For those meeting criteria for TES, functional dependence is graded on 5 levels, ranging from independent to severe dementia. A provisional level of certainty for CTE pathology is determined based on specific RHI exposure thresholds, core clinical features, functional status, and additional supportive features, including delayed onset, motor signs, and psychiatric features. CONCLUSIONS:New consensus diagnostic criteria for TES were developed with a primary goal of facilitating future CTE research. These criteria will be revised as updated clinical and pathologic information and in vivo biomarkers become available.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

(1) Background: The King-Devick (KD) rapid number naming test is sensitive for concussion diagnosis, with increased test time from baseline as the outcome measure. Eye tracking during KD performance in concussed individuals shows an association between inter-saccadic interval (ISI) (the time between saccades) prolongation and prolonged testing time. This pilot study retrospectively assesses the relation between ISI prolongation during KD testing and cognitive performance in persistently-symptomatic individuals post-concussion. (2) Results: Fourteen participants (median age 34 years; 6 women) with prior neuropsychological assessment and KD testing with eye tracking were included. KD test times (72.6 ± 20.7 s) and median ISI (379.1 ± 199.1 msec) were prolonged compared to published normative values. Greater ISI prolongation was associated with lower scores for processing speed (WAIS-IV Coding, r = 0.72, p = 0.0017), attention/working memory (Trails Making A, r = -0.65, p = 0.006) (Digit Span Forward, r = 0.57, p = -0.017) (Digit Span Backward, r= -0.55, p = 0.021) (Digit Span Total, r = -0.74, p = 0.001), and executive function (Stroop Color Word Interference, r = -0.8, p = 0.0003). (3) Conclusions: This pilot study provides preliminary evidence suggesting that cognitive dysfunction may be associated with prolonged ISI and KD test times in concussion.

BACKGROUND:Short-term outcomes of optic neuritis (ON) have been well characterized. Limited data exists on longer-term visual outcomes in patients who present with ON. The large MSBase registry allows for characterization of long-term visual outcomes after ON. METHODS:Via the MSBase Registry, data on patients from 41 centers was collected during routine clinical and research visits. Physical and visual disability were measured using the expanded disability status scale (EDSS) and the visual function score (VFS). Inclusion criteria for this analysis included age ≥ 18 years, clinically isolated syndrome (CIS), ON-onset, baseline visit within 6 months of onset, and at least one follow-up visit. Survival analysis was used to evaluate the association of disease-modifying treatment with time to conversion to clinically definite MS or sustained EDSS/VFS progression. RESULTS:Data from 60,933 patients were obtained from the MSBase registry in July 2019. Of these, 1317 patients met inclusion criteria; 935 were treated at some point in disease course, while 382 were never treated. At baseline, mean age was 32.3 ± 8.8 years, 74% were female, median EDSS was 2 (IQR 1-2), and median VFS was 1 (IQR 0-2). Median follow-up time was 5.2 years (IQR 2.4-9.3). Treatment was associated with reduced risk and delayed conversion to clinically definite MS (HR = 0.70, p < 0.001), sustained EDSS progression (HR = 0.46, p < 0.0001) and sustained VFS (HR = 0.41, p < 0.001) progression. CONCLUSIONS:In the MSBase cohort, treatment after ON was associated with better visual and neurological outcomes compared to no treatment. These results support early treatment for patients presenting with ON as the first manifestation of MS.

BACKGROUND:The novel coronavirus 2019 (COVID-19) pandemic has transformed health care. With the need to limit COVID-19 exposures, telemedicine has become an increasingly important format for clinical care. Compared with other fields, neuro-ophthalmology faces unique challenges, given its dependence on physical examination signs that are difficult to elicit outside the office setting. As such, it is imperative to understand both patient and provider experiences to continue to adapt the technology and tailor its application. The purpose of this study is to analyze both neuro-ophthalmology physician and patient satisfaction with virtual health visits during the time of the COVID-19 pandemic. METHODS:Across three institutions (NYU Langone Health, Indiana University Health, and Columbia University Medical Center), telemedicine surveys were administered to 159 patients. Neuro-ophthalmologists completed 157 surveys; each of these were linked to a single patient visit. Patient surveys consisted of 5 questions regarding visit preparation, satisfaction, challenges, and comfort. The physician survey included 4 questions that focused on ability to gather specific clinical information by history and examination. RESULTS:Among 159 patients, 104 (65.4%) reported that they were satisfied with the visit, and 149 (93.7%) indicated that they were comfortable asking questions. Sixty-eight (73.9%) patients found the instructions provided before the visit easy to understand. Potential areas for improvement noted by patients included more detailed preparation instructions and better technology (phone positioning, Internet connection, and software). More than 87% (137/157) of neuro-ophthalmologists surveyed reported having performed an examination that provided enough information for medical decision-making. Some areas of the neuro-ophthalmologic examination were reported to be easy to conduct (range of eye movements, visual acuity, Amsler grids, Ishihara color plates, and pupillary examination). Other components were more difficult (saccades, red desaturation, visual fields, convergence, oscillations, ocular alignment, and smooth pursuit); some were especially challenging (vestibulo-ocular reflex [VOR], VOR suppression, and optokinetic nystagmus). Clinicians noted that virtual health visits were limited by patient preparation, inability to perform certain parts of the examination (funduscopy and pupils), and technological issues. CONCLUSIONS:Among virtual neuro-ophthalmology visits evaluated, most offer patients with appointments that satisfy their needs. Most physicians in this cohort obtained adequate clinical information for decision-making. Even better technology and instructions may help improve aspects of virtual health visits.

Background/Objectives/UNASSIGNED:Little is known regarding the prevalence and predictors of prolonged cognitive and psychological symptoms of COVID-19 among community-dwellers. We aimed to quantitatively measure self-reported metrics of fatigue, cognitive dysfunction, anxiety, depression, and sleep and identify factors associated with these metrics among United States residents with or without COVID-19. Methods/UNASSIGNED:We solicited 1000 adult United States residents for an online survey conducted February 3-5, 2021 utilizing a commercial crowdsourcing community research platform. The platform curates eligible participants to approximate United States demographics by age, sex, and race proportions. COVID-19 was diagnosed by laboratory testing and/or by exposure to a known positive contact with subsequent typical symptoms. Prolonged COVID-19 was self-reported and coded for those with symptoms ≥ 1 month following initial diagnosis. The primary outcomes were NIH PROMIS/Neuro-QoL short-form T-scores for fatigue, cognitive dysfunction, anxiety, depression, and sleep compared among those with prolonged COVID-19 symptoms, COVID-19 without prolonged symptoms and COVID-19 negative subjects. Multivariable backwards step-wise logistic regression models were constructed to predict abnormal Neuro-QoL metrics. Results/UNASSIGNED:= 0.047), but there were no significant differences in quantitative measures of anxiety, depression, fatigue, or sleep. Conclusion/UNASSIGNED:Prolonged symptoms occurred in 25% of COVID-19 positive participants, and NeuroQoL cognitive dysfunction scores were significantly worse among COVID-19 positive subjects, even after accounting for demographic and stressor covariates. Fatigue, anxiety, depression, and sleep scores did not differ between COVID-19 positive and negative respondents.