ABSTRACT
COVID-19, a novel coronavirus disease, is still causing concern all over the world. Recently, researchers have been concentrating their efforts on understanding the complex dynamics of this widespread illness. Mathematics plays a big role in understanding the mechanism of the spread of this disease by modeling it and trying to find approximate solutions. In this study, we implement a new technique for an approximation of the analytic series solution called the multistep Laplace optimized decomposition method for solving fractional nonlinear systems of ordinary differential equations. The proposed method is a combination of the multistep method, the Laplace transform, and the optimized decomposition method. To show the ability and effectiveness of this method, we chose the COVID-19 model to apply the proposed technique to it. To develop the model, the Caputo-type fractional-order derivative is employed. The suggested algorithm efficacy is assessed using the fourth-order Runge-Kutta method, and when compared to it, the results show that the proposed approach has a high level of accuracy. Several representative graphs are displayed and analyzed in two dimensions to show the growth and decay in the model concerning the fractional parameter α values. The central processing unit computational time cost in finding graphical results is utilized and tabulated. From a numerical viewpoint, the archived simulations and results justify that the proposed iterative algorithm is a straightforward and appropriate tool with computational efficiency for several coronavirus disease differential model solutions. © 2022 the author(s), published by De Gruyter.
ABSTRACT
Background and purpose: Coronavirus disease 2019 (COVID-19) is associated with a small but clinically significant risk of stroke, the cause of which is frequently cryptogenic. In a large multinational cohort of consecutive COVID-19 patients with stroke, we evaluated clinical predictors of cryptogenic stroke, short-term functional outcomes and in-hospital mortality among patients according to stroke etiology. Methods: We explored clinical characteristics and short-term outcomes of consecutively evaluated patients 18 years of age or older with acute ischemic stroke (AIS) and laboratory-confirmed COVID- 19 from 31 hospitals in 4 countries (3/1/20-6/16/20). Results: Of the 14.483 laboratory-confirmed patients with COVID-19, 156 (1.1%) were diagnosed with AIS. Sixty-one (39.4%) were female, 84 (67.2%) white, and 88 (61.5%) were between 60-79 years of age. The most frequently reported etiology of AIS was cryptogenic (55/129, 42.6%), which was associated with significantly higher white blood cell count, c-reactive protein, and D-dimer levels than non-cryptogenic AIS patients (p</=0.05 for all comparisons). In a multivariable backward stepwise regression model estimating the odds of in-hospital mortality, cryptogenic stroke mechanism was associated with a fivefold greater odds in-hospital mortality than strokes due to any other mechanism (adjusted OR 5.16, 95%CI 1.41-18.87, p=0.01). In that model, older age (aOR2.05 per decade, 95%CI 1.35-3.11, p<0.01) and higher baseline NIHSS (aOR 1.12, 95%CI 1.02-1.21, p=0.01) were also independently predictive of mortality. Conclusions: Our findings suggest that cryptogenic stroke among COVID-19 patients may berelated to more severe disease and carries a significant risk of early mortality.
ABSTRACT
Background: Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has been associated with a significant risk of thrombotic events in critically ill patients. Aims: To summarize the findings of a multinational observational cohort of patients with SARS-CoV- 2 and cerebrovascular disease. Methods: Retrospective observational cohort of consecutive adults evaluated in the emergency department and/or admitted with coronavirus disease 2019 (COVID-19) across 31 hospitals in 4 countries (2/1/2020 - 06/16/2020). The primary outcome was the incidence rate of cerebrovascular events, inclusive of acute ischemic stroke, intracranial hemorrhages (ICH), and cortical vein and/or sinus thrombosis (CVST). Results: Of the 14,483 patients with laboratory-confirmed SARS-CoV-2, 172 were diagnosed with an acute cerebrovascular event (1.13% of cohort;1130/100,000 patients, 95%CI 970- 1320/100,000), 68/171 (40.5%) of whom were female and 96/172 (55.8%) were between the ages 60-79 years. Of these, 156 had acute ischemic stroke (1.08%;1080/100,000 95%CI 920- 1260/100,000), 28 ICH (0.19%;190/100,000 95%CI 130 - 280/100,000) and 3 with CVST (0.02%;20/100,000, 95%CI 4-60/100,000). The in-hospital mortality rate for SARS-CoV-2-associated stroke was 38.1% and for ICH 58.3%. After adjusting for clustering by site and age, baseline strokeseverity, and all predictors of in-hospital mortality found in univariate regression (p<0.1: male sex,tobacco use, arrival by emergency medical services, lower platelet and lymphocyte counts, andintracranial occlusion), cryptogenic stroke mechanism (aOR 5.01, 95%CI 1.63-15.44, p<0.01), olderage (aOR 1.78, 95%CI 1.07-2.94, p=0.03), and lower lymphocyte count on admission (aOR 0.58,95%CI 0.34-0.98 p=0.04) were the only independent predictors of mortality among patients withstroke and COVID-19. Conclusions: COVID-19 is associated with a small but significant risk of clinically relevantcerebrovascular events, particularly ischemic stroke. The mortality rate is high for COVID-19associated cerebrovascular complications, therefore aggressive monitoring and early interventionshould be pursued to mitigate poor outcomes.