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:Early neurorehabilitation improves outcomes in patients with disorders of consciousness (DoC) after brain injury, but its applicability in COVID-19 is unknown. We describe our experience implementing an early neurorehabilitation protocol for patients with COVID-19-associated DoC in the intensive care unit (ICU) and evaluate factors associated with recovery. METHODS:During the initial COVID-19 surge in New York City between March 10 and May 20, 2020, faced with a disproportionately high number of ICU patients with prolonged unresponsiveness, we developed and implemented an early neurorehabilitation protocol, applying standard practices from brain injury rehabilitation care to the ICU setting. Twenty-one patients with delayed recovery of consciousness after severe COVID-19 participated in a pilot early neurorehabilitation program that included serial Coma Recovery Scale-Revised (CRS-R) assessments, multimodal treatment, and access to clinicians specializing in brain injury medicine. We retrospectively compared clinical features of patients who did and did not recover to the minimally conscious state (MCS) or better, defined as a CRS-R total score (TS) ≥ 8, before discharge. We additionally examined factors associated with best CRS-R TS, last CRS-R TS, hospital length of stay, and time on mechanical ventilation. RESULTS:Patients underwent CRS-R assessments a median of six (interquartile range [IQR] 3-10) times before discharge, beginning a median of 48 days (IQR 40-55) from admission. Twelve (57%) patients recovered to MCS after a median of 8 days (IQR 2-14) off continuous sedation; they had lower body mass index (p = 0.009), lower peak serum C-reactive protein levels (p = 0.023), higher minimum arterial partial pressure of oxygen (p = 0.028), and earlier fentanyl discontinuation (p = 0.018). CRS-R scores fluctuated over time, and the best CRS-R TS was significantly higher than the last CRS-R TS (median 8 [IQR 5-23] vs. 5 [IQR 3-18], p = 0.002). Earlier fentanyl (p = 0.001) and neuromuscular blockade (p = 0.015) discontinuation correlated with a higher last CRS-R TS. CONCLUSIONS:More than half of our cohort of patients with prolonged unresponsiveness following severe COVID-19 recovered to MCS or better before hospital discharge, achieving a clinical benchmark known to have relatively favorable long-term prognostic implications in DoC of other etiologies. Hypoxia, systemic inflammation, sedation, and neuromuscular blockade may impact diagnostic assessment and prognosis, and fluctuations in level of consciousness make serial assessments essential. Early neurorehabilitation of these patients in the ICU can be accomplished but is associated with unique challenges. Further research should evaluate factors associated with longer-term neurologic recovery and benefits of early rehabilitation in patients with severe COVID-19.

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.

BACKGROUND:Disruptions of the inferior longitudinal fasciculus (ILF) in the nondominant temporal lobe can lead to the rare but significant higher visual-processing disturbance of prosopagnosia. Here, the authors describe a 57-year-old right hand-dominant female with a large breast cancer brain metastasis in the right temporal lobe who underwent resection and subsequent Gamma Knife radiosurgery. She presented with difficulty with facial recognition, but following surgical intervention, the prosopagnosia became more profound. OBSERVATIONS/METHODS:Even in nondominant cortex, significant deficits can arise when operating near higher visual-processing centers, including the ILF. LESSONS/CONCLUSIONS:This case highlights the utility of imaging-based tractography obtained from preoperative imaging for resective surgical planning even when operating in areas that do not involve what is traditionally considered elegant areas of the brain. To optimize neurological outcomes in metastatic tumor resection, awareness and diffusion tensor imaging of neighboring, displaced white matter tracts may prevent permanent deficits in higher visual processing.