The SARS-CoV-2, a novel virus has shown an association with central nervous system (CNS) symptoms. Initial retrospective studies emerging from China and France, as well as case reports from different parts of the world revealed a spectrum of neurological symptoms ranging from a simple headache to more serious encephalitis and dysexecutive syndromes. Authors have tried to explain this neurotropism of the virus by comparing invasion mechanisms with prior epidemic coronavirus like severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). Concrete evidence on those viruses has been limited. This review attempts to discuss various pathophysiological mechanisms as it relates to neurological complications of SARS-CoV-2. We will also discuss the neurological manifestations seen in various retrospective studies, systemic reviews, and case reports.

Introduction: Our goal was to systematically review contemporary literature comparing the relative effectiveness of two mechanical compression devices (LUCAS and AutoPulse) to manual compression for achieving return of spontaneous circulation (ROSC) in patients undergoing cardiopulmonary resuscitation (CPR) after an out-of-hospital cardiac arrest (OHCA). Methods: We searched medical databases systematically for randomized controlled trials (RCT) and observational studies published between January 1, 2000–October 1, 2020 that compared mechanical chest compression (using any device) with manual chest compression following OHCA. We only included studies in the English language that reported ROSC outcomes in adult patients in non-trauma settings to conduct random-effects metanalysis and trial sequence analysis (TSA). Multivariate meta-regression was performed using preselected covariates to account for heterogeneity. We assessed for risk of biases in randomization, allocation sequence concealment, blinding, incomplete outcome data, and selective outcome reporting. Results: A total of 15 studies (n = 18474), including six RCTs, two cluster RCTs, five retrospective case-control, and two phased prospective cohort studies, were pooled for analysis. The pooled estimates’ summary effect did not indicate a significant difference (Mantel-Haenszel odds ratio = 1.16, 95% confidence interval, 0.97 to 1.39, P = 0.11, I2 = 0.83) between mechanical and manual compressions during CPR for ROSC. The TSA showed firm evidence supporting the lack of improvement in ROSC using mechanical compression devices. The Z-curves successfully crossed the TSA futility boundary for ROSC, indicating sufficient evidence to draw firm conclusions regarding these outcomes. Multivariate meta-regression demonstrated that 100% of the between-study variation could be explained by differences in average age, the proportion of females, cardiac arrests with shockable rhythms, witnessed cardiac arrest, bystander CPR, and the average time for emergency medical services (EMS) arrival in the study samples, with the latter three attaining statistical significance. Conclusion: Mechanical compression devices for resuscitation in cardiac arrests are not associated with improved rates of ROSC. Their use may be more beneficial in non-ideal situations such as lack of bystander CPR, unwitnessed arrest, and delayed EMS response times. Studies done to date have enough power to render further studies on this comparison futile.

Objective The objective of this study was to develop a US‐representative prediction model identifying factors with a greater likelihood of patients leaving without being seen. Methods We conducted a retrospective cohort analysis using a 2016 nationwide emergency department (ED) sample. Patient factors considered for analysis were the following: age, sex, acuity, chronic diseases, weekend visit, quarter of presentation, median household income quartile for patient's zip code, primary/secondary insurance, total charges for the visit, and urban/rural household. Hospital factors considered were urban/rural location, trauma center/teaching hospital, and annual ED volume. Multivariable logistic regression was used to find significant predictors and their interactions. A random forest algorithm was used to determine the order of importance of factors. Results A total of 32,680,232 hospital‐based ED visits with 466,047 incidences of leaving without being seen were included. The cohort comprised 55.5% females, with a median (IQR) age of 37 (21–58) years. Positively associating factors were male sex (odds ratio [OR], 1.22; 99% confidence interval [CI], 1.17–1.26), lower acuity (P < 0.001), and annual ED visits ≥60,000 (OR, 1.44; 99% CI, 1.21–1.7) versus <20,000. Negatively associating factors were primary insurance being Medicare/Tricare or private insurance (P < 0.001); weekend presentations (OR, 0.87; 99% CI, 0.85–0.89); age >64 or <18 years (P < 0.001); and higher median household income for patient's zip code second (OR, 0.86; 99% CI, 0.77–0.97), third (OR, 0.8; 99% CI, 0.7–0.91), and fourth (OR, 0.7; 99% CI, 0.6–0.8) quartiles versus the first quartile. Significant interactions existed between age, acuity, primary insurance, and chronic conditions. Primary insurance was the most predictive. Conclusion Our derivation model reiterated several modifiable and non‐modifiable risk factors for leaving without being seen established previously while rejecting the importance of others.