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Babaee M, Atashgar K, Amini Harandi A, Yousefi A. Prediction of Stroke After the COVID-19 Infection. BCN 2024; 15 (1) :89-100
URL: http://bcn.iums.ac.ir/article-1-2247-en.html

Introduction: Although several studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports have indicated 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 from 128 patients between March and September 2020, including their demographic information, clinical characteristics, and laboratory parameters were collected and analyzed statistically. A logistic regression model was developed to identify the significant variables in predicting stroke incidence in patients with COVID-19. 
Results: Clinical characteristics and laboratory parameters for 128 patients (including 76 males and 52 females; with a mean age of 57.109±15.97 years) 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 lactate dehydrogenase (LDH). Receiver operating characteristic–area under the curve (ROC-AUC), accuracy, sensitivity, and specificity were considered indices to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve was 97.5% with a 95% CI.
Conclusion: The findings showed that ventilator dependence, cardiac ejection fraction, and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.