Persons with Co-Existing Neurological Disorders: Risk Analysis, Considerations and Management in COVID-19 Pandemic.

Increasing reports of neurological symptoms in COVID-19 patient's warrant clinicians to adopt and define the standardized diagnostic and managing protocols in order to investigate the linkage of neurological symptoms in COVID-19. Encephalitis, anosmia, acute cerebrovascular disease and ageusia are some of the emerging neurological manifestations which are reported in several cohort studies on hospitalized patients with COVID-19. Although the COVID-19 pandemic is primarily associated with infection of the respiratory tract system, but measures like lockdown and restricted physical movements to control the spread of this infection will certainly have neurobehavioural implications. Additionally, some of the patients with pre-existing neurological manifestations like epilepsy, Parkinson's and Alzheimer's disease are more prone to infection and demand extra care as well as improvised treatment. In this review, we have focused on the neurovirological clinical manifestations associated with the COVID-19 pandemic. Although the prevalence of neurovirological manifestations is rare increasing reports cannot be ignored and needs to be discussed thoroughly with respect to risk analysis and considerations for developing a management strategy. This also helps in defining the burden of neurological disorders associated with COVID-19 patients.

Biosafety during a pandemic: shared resource laboratories rise to the challenge.

Biosafety has always been an important aspect of daily work in any research institution, particularly for cytometry Shared Resources Laboratories (SRLs). SRLs are common-use spaces that facilitate the sharing of knowledge, expertise, and ideas. This sharing inescapably involves contact and interaction of all those within this working environment on a daily basis. The current pandemic caused by SARS-CoV-2 has prompted the re-evaluation of many policies governing the operations of SRLs. Here we identify and review the unique challenges SRLs face in maintaining biosafety standards, highlighting the potential risks associated with not only cytometry instrumentation and samples, but also the people working with them. We propose possible solutions to safety issues raised by the COVID-19 pandemic and provide tools for facilities to adapt to evolving guidelines and future challenges.

Improving Health Risk Assessment as a Basis for Public Health Decisions in the 21st Century.

One-fifth of the way through the 21st century, a commonality of factors with those of the last 50 years may offer the opportunity to address unfinished business and current challenges. The recommendations include: (1) Resisting the tendency to oversimplify scientific assessments by reliance on single disciplines in lieu of clear weight-of-evidence expressions, and on single quantitative point estimates of health protective values for policy decisions; (2) Improving the separation of science and judgment in risk assessment through the use of clear expressions of the range of judgments that bracket protective quantitative levels for public health protection; (3) Use of comparative risk to achieve the greatest gains in health and the environment; and (4) Where applicable, reversal of the risk assessment and risk management steps to facilitate timely and substantive improvements in public health and the environment. Lessons learned and improvements in the risk assessment process are applied to the unprecedented challenges of the 21st century such as, pandemics and climate change. The beneficial application of the risk assessment and risk management paradigm to ensure timely research with consistency and transparency of assessments is presented. Institutions with mandated stability and leadership roles at the national and international levels are essential to ensure timely interdisciplinary scientific assessment at the interface with public policy as a basis for organized policy decisions, to meet time sensitive goals, and to inform the public.

Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The ongoing COVID-19 pandemic is disproportionately affecting patients with comorbidities. Therefore, thorough comorbidities assessment can help establish risk stratification of patients with COVID-19, upon hospital admission. Charlson Comorbidity Index (CCI) is a validated, simple, and readily applicable method of estimating the risk of death from comorbid disease and has been widely used as a predictor of long-term prognosis and survival. METHODS: We performed a systematic review and meta-analysis of CCI score and a composite of poor outcomes through several databases. RESULTS: Compared to a CCI score of 0, a CCI score of 1-2 and CCI score of ≥3 was prognostically associated with mortality and associated with a composite of poor outcomes. Per point increase of CCI score also increased mortality risk by 16%. Moreover, a higher mean CCI score also significantly associated with mortality and disease severity. CONCLUSION: CCI score should be utilized for risk stratifications of hospitalized COVID-19 patients.

Acceptability of a COVID-19 vaccine among adults in the United States: How many people would get vaccinated?

BACKGROUND: Coronavirus disease 2019 (COVID-19) was declared a pandemic in March 2020. Several prophylactic vaccines against COVID-19 are currently in development, yet little is known about people's acceptability of a COVID-19 vaccine. METHODS: We conducted an online survey of adults ages 18 and older in the United States (n = 2,006) in May 2020. Multivariable relative risk regression identified correlates of participants' willingness to get a COVID-19 vaccine (i.e., vaccine acceptability). RESULTS: Overall, 69% of participants were willing to get a COVID-19 vaccine. Participants were more likely to be willing to get vaccinated if they thought their healthcare provider would recommend vaccination (RR = 1.73, 95% CI: 1.49-2.02) or if they were moderate (RR = 1.09, 95% CI: 1.02-1.16) or liberal (RR = 1.14, 95% CI: 1.07-1.22) in their political leaning. Participants were also more likely to be willing to get vaccinated if they reported higher levels of perceived likelihood getting a COVID-19 infection in the future (RR = 1.05, 95% CI: 1.01-1.09), perceived severity of COVID-19 infection (RR = 1.08, 95% CI: 1.04-1.11), or perceived effectiveness of a COVID-19 vaccine (RR = 1.46, 95% CI: 1.40-1.52). Participants were less likely to be willing to get vaccinated if they were non-Latinx black (RR = 0.81, 95% CI: 0.74-0.90) or reported a higher level of perceived potential vaccine harms (RR = 0.95, 95% CI: 0.92-0.98). CONCLUSIONS: Many adults are willing to get a COVID-19 vaccine, though acceptability should be monitored as vaccine development continues. Our findings can help guide future efforts to increase COVID-19 vaccine acceptability (and uptake if a vaccine becomes available).

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.