Faculty Bibliography

ABSTRACT:A 62-year-old man presented with headache, fever, and malaise. He was diagnosed with Anaplasma phagocytophilum, confirmed by serum polymerase chain reaction, and started on oral doxycycline. After 5 days of treatment, the patient began to experience gait imbalance with frequent falls, as well as myoclonus, and confusion. Examination was notable for opsoclonus-myoclonus-ataxia (OMA) and hypometric saccades. Cerebrospinal fluid (CSF) autoimmune encephalitis panel demonstrated a markedly elevated neuronal intermediate filament (NIF) immunoglobulin G antibody titer of 1:16, with positive neurofilament light- and heavy-chain antibodies. These antibodies were suspected to have been triggered by the Anaplasma infection. Repeat CSF examination 8 days later still showed a positive immunofluorescence assay for NIF antibodies, but the CSF titer was now less than 1:2. Body computed tomography imaging was unrevealing for an underlying cancer. Our patient illustrates a postinfectious mechanism for OMA and saccadic hypometria after Anaplasma infection.

Purpose/UNASSIGNED:Herpes zoster (HZ) has been identified as a potential association with the BNT162b2 COVID-19 vaccination. This study evaluated this possible association in a cohort of patients receiving the vaccination. Methods/UNASSIGNED:Epic electronic health records of adult patients who received at least one COVID-19 vaccination between January 12, 2020 and 9/30/2021 within the NYU Langone Health were reviewed to analyze a new diagnosis of herpes zoster within 3 months before compared to 3 months after vaccination. Results/UNASSIGNED:Of the 596,111 patients who received at least one COVID-19 vaccination, 716 patients were diagnosed with HZ within three months prior to vaccination, compared to 781 patients diagnosed within 3 months afterwards. Using the chi-square test for independence of proportions, there was not a statistically significant difference in frequency of HZ before (proportion: 0.0012, 95% CI: [0.0011, 0.0013]) vs. after vaccination (proportion: 0.0013, 95% CI: [0.0012, 0.0014]); (p = 0.093). Conclusions and importance/UNASSIGNED:This study did not find evidence of an association between COVID-19 vaccination and a new diagnosis of HZ. We encourage health care professionals to strongly recommend COVID-19 vaccinations per Centers for Disease Control (CDC) recommendations and vaccination against HZ according to Food and Drug Administration (FDA) approval for the recombinant zoster vaccine.

BACKGROUND/OBJECTIVES/OBJECTIVE:Little is known about trajectories of recovery 12-months after hospitalization for severe COVID. METHODS:We conducted a prospective, longitudinal cohort study of patients with and without neurological complications during index hospitalization for COVID-19 from March 10, 2020-May 20, 2020. Phone follow-up batteries were performed at 6- and 12-months post-COVID symptom onset. The primary 12-month outcome was the modified Rankin Scale (mRS) comparing patients with or without neurological complications using multivariable ordinal analysis. Secondary outcomes included: activities of daily living (Barthel Index), telephone Montreal Cognitive Assessment (t-MoCA) and Neuro-QoL batteries for anxiety, depression, fatigue and sleep. Changes in outcome scores from 6 to 12-months were compared using non-parametric paired-samples sign test. RESULTS:Twelve-month follow-up was completed in N=242 patients (median age 65, 64% male, 34% intubated during hospitalization) and N=174 completed both 6- and 12-month follow-up. At 12-months 197/227 (87%) had ≥1 abnormal metric: mRS>0 (75%), Barthel<100 (64%), t-MoCA≤18 (50%), high anxiety (7%), depression (4%), fatigue (9%) and poor sleep (10%). 12-month mRS scores did not differ significantly among those with (N=113) or without (N=129) neurological complications during hospitalization after adjusting for age, sex, race, pre-COVID mRS and intubation status (adjusted OR 1.4, 95% CI0.8-2.5), though those with neurological complications had higher fatigue scores (T-score 47 vs 44, P=0.037). Significant improvements in outcome trajectories from 6- to 12-months were observed in t-MoCA scores (56% improved, median difference 1 point, P=0.002), and Neuro-QoL anxiety scores (45% improved, P=0.003). Non-significant improvements occurred in fatigue, sleep and depression scores in 48%, 48% and 38% of patients, respectively. Barthel and mRS scores remained unchanged between 6 and 12-months in >50% of patients. DISCUSSION/CONCLUSIONS:At 12-months post-hospitalization for severe COVID, 87% of patients had ongoing abnormalities in functional, cognitive or Neuro-QoL metrics and abnormal cognition persisted in 50% of patients without a prior history of dementia/cognitive abnormality. Only fatigue severity differed significantly between patients with or without neurological complications during index hospitalization. However, significant improvements in cognitive (t-MoCA) and anxiety (Neuro-QoL) scores occurred in 56% and 45% of patients, respectively, between 6- to 12-months. These results may not be generalizable to those with mild/moderate COVID.

We describe an educational intervention for neurology residents aimed at developing feedback skills. An objective structured clinical examination case was designed to simulate the provision of feedback to a medical student. After the simulated case session, residents received structured, individualized feedback on their performance and then participated in a group discussion about feedback methods. Survey data were collected from the standardized medical student regarding residents' performance and from residents for assessments of their performance and of the OSCE case. This manuscript aims to describe this educational intervention and to demonstrate the feasibility of this approach for feedback skills development.

OBJECTIVE:Rapid automatized naming (RAN) tasks have been utilized for decades to evaluate neurological conditions. Time scores for the Mobile Universal Lexicon Evaluation System (MULES, rapid picture naming) and Staggered Uneven Number (SUN, rapid number naming) are prolonged (worse) with concussion, mild cognitive impairment, multiple sclerosis and Parkinson's disease. The purpose of this investigation was to compare paper/pencil versions of MULES and SUN with a new digitized format, the MICK app. METHODS:Participants (healthy office-based volunteers, professional women's hockey players), completed two trials of the MULES and SUN tests on both platforms (tablet, paper/pencil). The order of presentation of the testing platforms was randomized. Between-platform variability was calculated using the two-way random-effects intraclass correlation coefficient (ICC). RESULTS:Among 59 participants (median age 32, range 22-83), no significant differences were observed for comparisons of mean best scores for the paper/pencil versus MICK app platforms, counterbalanced for order of administration (P = 0.45 for MULES, P = 0.50 for SUN, linear regression). ICCs for agreement between the MICK and paper/pencil tests were 0.92 (95% CI 0.86, 0.95) for MULES and 0.94 (95% CI 0.89, 0.96) for SUN, representing excellent levels of agreement. Inter-platform differences did not vary systematically across the range of average best time score for either test. CONCLUSION/CONCLUSIONS:The MICK app for digital administration of MULES and SUN demonstrates excellent agreement of time scores with paper/pencil testing. The computerized app allows for greater accessibility and scalability in neurological diseases, inclusive of remote monitoring. Sideline testing for sports-related concussion may also benefit from this technology.

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.