Prediction of Stroke After the COVID-19 Infection

Abstract

Background: Although several different studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports indicate that in many cases, the incidence of stroke in patients with COVID-19 leads to death. Objectives: The obtained mathematical equation in this study can help physicians’ decision-making about treatment and identification of influential clinical factors for early diagnosis. Methods: In this retrospective study, data of 128 patients between March and September 2020 including demographic information, clinical characteristics and laboratory parameters of patients were collected and analyzed statistically. A logistic regression (LR) model was developed to identify the significant variables for the prediction of stroke incidence in patients with COVID-19. Results: Clinical characteristics and laboratory parameters for 128 patients (including 76 males, 52 females; with mean age 57.109 ± 15.97years) were considered as the inputs that included: ventilator dependence, comorbidities and laboratory tests including WBC, Neutrophil, lymphocyte, platelet count, C-Reactive Protein, Blood Urea Nitrogen, Alanine transaminase (ALT), Aspartate transaminase (AST) and LDH. The indexes such as receiver operating characteristic–area under the curve (ROC-AUC) and accuracy, sensitivity, and specificity were considered to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve indicated 97.5% with a 95% confidence interval. Conclusion: The findings showed that ventilator dependence and Cardiac Ejection Fraction and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.