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1- Ph.D. Candidate – Malek Ashtar University of Technology- Industrial Engineering Faculty, Tehran, Iran.
2- Associate Professor – Malek Ashtar University of Technology- Industrial Engineering Faculty, Tehran, Iran.
3- Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
4- Neurology Resident, Department of Neurology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran.
2- Associate Professor – Malek Ashtar University of Technology- Industrial Engineering Faculty, Tehran, Iran.
3- Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
4- Neurology Resident, Department of Neurology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran.
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.
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.
Type of Study: Original |
Subject:
Computational Neuroscience
Received: 2021/07/30 | Accepted: 2022/03/8
Received: 2021/07/30 | Accepted: 2022/03/8
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Basic and Clinical Neuroscience Journal (BCN)
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