Faculty Bibliography

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.

BACKGROUND:The coronavirus disease 2019 (COVID-19) is a systemic entity that frequently implies neurologic features at presentation and complications during the disease course. We aimed to describe the characteristics and predictors for developing in-hospital neurologic manifestations in a large cohort of hospitalized patients with COVID-19 in Mexico City. METHODS:We analyzed records from consecutive adult patients hospitalized from March 15 to June 30, 2020, with moderate to severe COVID-19 confirmed by reverse transcription real-time polymerase chain reaction (rtRT-PCR) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Neurologic syndromes were actively searched by a standardized structured questionnaire and physical examination, confirmed by neuroimaging, neurophysiology of laboratory analyses, as applicable. RESULTS:We studied 1,072 cases (65% men, mean age 53.2±13 years), 71 patients had pre-existing neurologic diseases (diabetic neuropathy: 17, epilepsy: 15, history of ischemic stroke: eight, migraine: six, multiple sclerosis: one, Parkinson disease: one), and 163 (15.2%) developed a new neurologic complication. Headache (41.7%), myalgia (38.5%), dysgeusia (8%), and anosmia (7%) were the most common neurologic symptoms at hospital presentation. Delirium (13.1%), objective limb weakness (5.1%), and delayed recovery of mental status after sedation withdrawal (2.5%), were the most common new neurologic syndromes. Age, headache at presentation, preexisting neurologic disease, invasive mechanical ventilation, and neutrophil/lymphocyte ratio ≥9 were independent predictors of new in-hospital neurologic complications. CONCLUSIONS:Even after excluding initial clinical features and pre-existing comorbidities, new neurologic complications in hospitalized patients with COVID-19 are frequent and can be predicted from clinical information at hospital admission.

Background: Hemodialysis (HD) can reduce amyloid-beta (Abeta) species in wholebody circulation by 30 to 50%. Due to the dynamic exchange of Abeta between the brain and the blood, we hypothesized that HD might lower Abeta levels in the cerebrospinal fluid (CSF).
Method(s): In a dialysis network with over 160,000 patients, we identified three maintenance HD patients (age 36+/-9 years) with ventriculo-peritoneal (VP) shunts who were subsequently recruited for this IRB-approved research study. Study subjects were dialyzed on Monday, Wednesday, and Friday. Plasma samples were collected at 6 timepoints during the 3 HD sessions. One subject was withdrawn over safety concern related to the VP shunt tap procedure. Two subjects further underwent VP shunt taps for CSF sample collection before and after the Wednesday and Friday HD sessions, and once on interdialytic days (Tuesday, Thursday). Abeta1-42 and Abeta1-40 were quantified by Neuro 3-Plex SIMOA assays (Quanterix, MA, USA).
Result(s): HD effectively reduced plasma Abeta1-40 by 41% and Abeta1-42 by 34% (Fig 1a and 1b, p < 0.01). In CSF, levels of Abeta increased after Wednesday HD sessions in subject 1 (Abeta1-40: 4.2-fold, Abeta1-42: 5.5-fold) and subject 2 (Abeta1-40 and Abeta1-42: 1.06-fold), while Abeta decreased after Friday HD sessions in both subject 1 (Abeta1-40: 0.1-fold, Abeta1-42: 0.1-fold) and 2 (Abeta1-40: 0.7-fold, Abeta1-42: 0.7-fold) shown in Figure 1c-f.
Conclusion(s): This is the first report of Abeta dynamics in the CSF and plasma of HD patients. While plasma levels were in similar ranges, we found high inter-individual variations of CSF levels. Different plasma-to-CSF ratios after HD may reflect individual brain Abeta pools that are accessed by HD. We corroborate previous reports demonstrating the removal of Abeta from the blood compartment by HD. (Figure Presented)

BACKGROUND:There is an increased effort to better understand neuropsychiatric symptoms of Alzheimer's disease (AD) as an important feature of symptomatic burden as well as potential modi- fiable factors of the disease process. Anxiety is one of the most common neuropsychiatric symptoms in Alzheimer's disease (AD). A growing body of work has emerged that addresses the epidemiology and biological correlations of anxiety in AD. OBJECTIVE AND METHODS/OBJECTIVE:Here, we review human studies in research and clinical cohorts that examined anxiety in AD. We focused on work related to prevalence across AD stages, correlation with established biomarkers, relationship with AD neuropathology and genetic risk factors, and impact on progression. RESULTS:Anxiety is prominent in the early stages and increases across the spectrum of functional stages. Biomarker relationships are strongest at the level of FDG-PET and amyloid measured via PET or cerebrospinal fluid analysis. Neuropathologically, anxiety emerges with early Braak stage tau pathology. The presence of the apolipoprotein E e4 allele is associated with increased anxiety at all stages, most notably at mild cognitive impairment. Anxiety portended a faster progression at all pre-dementia stages. CONCLUSION/CONCLUSIONS:This body of work suggests a close biological relationship between anxiety and AD that begins in early stages and influences functional decline. As such, we discuss future work that would improve our understanding of this relationship and test the validity of anxiolytic treatment as disease modifying therapy for AD.

Introduction: Quantifying upper extremity (UE) motor impairment after stroke is impractical, limiting our ability to tailor rehabilitation training in real time. The current gold-standard measure of impairment, the Fugl-Meyer Assessment (FMA), is time-consuming and requires a trained assessor. The FMA furthermore does not assess functional motions in real-world contexts, which is exactly where we aim our rehabilitation interventions. Here, we took initial steps to develop an approach to automatically quantify UE motor impairment during functional task performance.
Method(s): We studied 51 chronic stroke patients (28F:23M; 57.7 (21.3-84.3) years old; 28L:23R paretic; FMA 43.1 (8-65)).We recorded upper body motion with 9 inertial measurement units (IMUs) while patients performed the FMA and a functional task (moving an object on a horizontal 8-target array). We trained a long short-term memory (LSTM) deep learning model to estimate FMA scores from the recorded motion (training set n=40; test set n=11; 4 LSTM layers with between-layer batch normalization; IMU data windows of 4s with slide of 1s). LSTM-generated impairment scores were computed from FMA motions or from functional motions. To ascertain the accuracy of the approach, we calculated the root mean square error (RMSE) and the Spearman correlation coefficient (rho) between the LSTM scores and the FMA scores from a trained expert. We also examined whether the performance of particular classes of functional primitives (i.e. reach, transport, or reposition) would be sufficient to accurately estimate impairment.
Result(s): Using motions from the FMA performance, our approach estimated FMA scores within 1.1 points of a trained assessor. Using motions from the functional task performance, our approach estimated FMA scores within 1.6 points. Correlation values between the FMA scores and LSTM scores were rho = 0.98 for FMA motions and rho = 0.96 for functional motions. Among the three functional primitives, reaches were the most informative for estimating the impairment scores (RMSE: 1.9 points), followed by transports (RMSE: 2.1 points), and repositions (RMSE: 2.8 points).
Discussion(s): We present a new approach that uses sensor-based motion capture and deep learning to automatically estimate UE motor impairment. This approach has high accuracy and shows high concurrent validity with the FMA, even when it assesses unrelated functional motions. Thus, it may be possible to directly measure impairment from performance of real-world functional tasks, which the FMA does not offer. Estimating impairment during stroke rehabilitation would enable clinicians to tailor treatment strategy in real time.

BACKGROUND:The initial limited supply of COVID-19 vaccine in the U.S. presented significant allocation, distribution, and delivery challenges. Information that can assist health officials, hospital administrators and other decision makers with readily identifying who and where to target vaccine resources and efforts can improve public health response. OBJECTIVE:The objective of this project was to develop a publicly available geographical information system (GIS) web mapping tool that would assist North Carolina health officials readily identify high-risk, high priority population groups and facilities in the immunization decision making process. METHODS:Publicly available data were used to identify 14 key health and socio-demographic variables and 5 differing themes (social and economic status; minority status and language; housing situation; at risk population; and health status). Vaccine priority population index (VPI) scores were created by calculating a percentile rank for each variable over each N.C. Census tract. All Census tracts (N = 2,195) values were ranked from lowest to highest (0.0 to 1.0) with a non-zero population and mapped using ArcGIS. RESULTS:The VPI tool was made publicly available (https://enchealth.org/) during the pandemic to readily assist with identifying high risk population priority areas in N.C. for the planning, distribution, and delivery of COVID-19 vaccine. DISCUSSION/CONCLUSIONS:While health officials may have benefitted by using the VPI tool during the pandemic, a more formal evaluation process is needed to fully assess its usefulness, functionality, and limitations. CONCLUSION/CONCLUSIONS:When considering COVID-19 immunization efforts, the VPI tool can serve as an added component in the decision-making process.