prediction of stroke after the covid-19 infection

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.