Characteristics of Suspected COVID-19 Discharged Emergency Department Patients Who Returned During the First Wave.

INTRODUCTION: Limited information exists on patients with suspected coronavirus disease 2019 (COVID-19) who return to the emergency department (ED) during the first wave. In this study we aimed to identify predictors of ED return within 72 hours for patients with suspected COVID-19. METHODS: Incorporating data from 14 EDs within an integrated healthcare network in the New York metropolitan region from March 2-April 27, 2020, we analyzed this data on predictors for a return ED visit-including demographics, comorbidities, vital signs, and laboratory results. RESULTS: In total, 18,599 patients were included in the study. The median age was 46 years old [interquartile range 34-58]), 50.74% were female, and 49.26% were male. Overall, 532 (2.86%) returned to the ED within 72 hours, and 95.49% were admitted at the return visit. Of those tested for COVID-19, 59.24% (4704/7941) tested positive. Patients with chief complaints of "fever" or "flu" or a history of diabetes or renal disease were more likely to return at 72 hours. Risk of return increased with persistently abnormal temperature (odds ratio [OR] 2.43, 95% CI 1.8-3.2), respiratory rate (2.17, 95% CI 1.6-3.0), and chest radiograph (OR 2.54, 95% CI 2.0-3.2). Abnormally high neutrophil counts, low platelet counts, high bicarbonate values, and high aspartate aminotransferase levels were associated with a higher rate of return. Risk of return decreased when discharged on antibiotics (OR 0.12, 95% CI 0.0-0.3) or corticosteroids (OR 0.12, 95% CI 0.0-0.9). CONCLUSION: The low overall return rate of patients during the first COVID-19 wave indicates that physicians' clinical decision-making successfully identified those acceptable for discharge.

Outcomes of COVID-19 infection in multiple sclerosis and related conditions: One-year pandemic experience of the multicenter New York COVID-19 Neuroimmunology Consortium (NYCNIC).

OBJECTIVE: To determine outcomes of COVID-19 in patients with Multiple Sclerosis (MS) and related conditions, and to determine predictors of these outcomes. METHODS: This was a multicenter, observational cohort study of patients with MS or related CNS autoimmune disorders who developed confirmed or highly suspected COVID-19 infection from 2/1/2020 to 12/31/2020. MAIN OUTCOME AND MEASURE: The primary outcome measure was hospitalization status due to COVID-19. Severity of infection was measured using a 4-point ordinal scale: 1. home care; 2. hospitalization without mechanical ventilation; 3. hospitalization and mechanical ventilation, and 4. death. RESULTS: Of 474 patients in the study, 63.3% had confirmed COVID-19 infection and 93.9% were diagnosed with an MS phenotype. Mean age was 45 ± 13 (mean±SD) years, 72% were female, and 86% were treated with a DMT at the time of infection. 58 patients (12.2%) were hospitalized. 24 patients (5.1%) were critically ill (requiring ICU care or outcome of death), of which 15 patients (3.2%) died. Higher neurological disability and older age independently predicted hospitalization. 85% (102/120) of patients with known antibody results not treated with anti-CD20 therapies were seropositive while only 39.5% (17/43) of patients treated with anti-CD20 demonstrated seropositivity (p < 0.0001). Only 25% (2/8) of patients with PCR-confirmed COVID-19 being treated with anti-CD20 therapies demonstrated seropositivity. CONCLUSIONS: Neurological disability and older age independently predicted hospitalization due to COVID-19. Additionally, the results demonstrate that anti-CD20 therapies significantly blunt humoral responses post-infection, a finding that carries implications with regards to natural or vaccine-mediated immunity.

Sociodemographic predictors and transportation patterns of COVID-19 infection and mortality.

BACKGROUND: The United States Centers for Disease Control and Prevention (CDC)-sanctioned prevention strategies have included frequent handwashing with soap and water, covering the mouth and nose with a mask when around others, cleaning and disinfecting maintaining a distance of at least 6 feet from others, etc. Although many of these recommendations are based upon observation and past infection control practices, it is important to combine and explore public data sets to identify predictors of infection, morbidity and mortality to develop more finely honed interventions, based on sociodemographic factors. METHOD: Cross-sectional study of both states in the US and counties in NY state. RESULTS: Population density was found to be significantly associated with state-level coronavirus infection and mortality rate (b = 0.49, 95% confidence interval (CI): 0.34, 0.64, P < .0001). States that have lower socioeconomic status, lower mean age and denser populations are associated with higher incidence rates. In regard to NY state, counties with a higher percentage of minority residents had higher COVID-19 mortality rates (b = 2.61, 95% CI: 0.36, 4.87, P = 0.023). Larger population cohorts were associated with lower COVID-19 mortality rates after adjusting for other variables in the model (b = -1.39, 95% CI: -2.07, -0.71, P < 0.001). Population density was not significantly associated with COVID-19 mortality rates after adjustment across counties in the NY state. Public ridership was not indicative of cases or mortality across states in the USA; however, it is a significant factor associated with incidence (but not mortality) in NY counties. CONCLUSION: Population density was the only significant predictor of mortality across states in the USA. Lower mean age, lower median household incomes and more densely populated states were at higher risk of COVID-19 infection. Population density was not found to be a significant independent variable compared to minority status and socioeconomic factors in the New York epicenter. Meanwhile, public ridership was found to be a significant factor associated with incidence in New York counties.