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Prediction of Stroke After the COVID-19 Infection
Mahsa Babaee1, Karim Atashgar1, Ali Amini Harandi2
1Faculty of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.
Basic and Clinical Neuroscience
|September 18, 2024
Summary
COVID-19 patients face a high risk of ischemic stroke, a serious complication. This study developed a predictive model using clinical factors, achieving 97.5% accuracy in identifying stroke risk.
Area of Science:
- Neurology
- Infectious Diseases
- Medical Informatics
Background:
- Ischemic stroke is a significant complication in COVID-19 patients, often leading to mortality.
- Understanding risk factors is crucial for early diagnosis and treatment of stroke in COVID-19 survivors.
Purpose of the Study:
- To develop a predictive model for identifying stroke incidence in COVID-19 patients.
- To identify key clinical and laboratory factors associated with stroke in COVID-19 patients.
Main Methods:
- A retrospective analysis of 128 COVID-19 patients' data (March-September 2020).
- Development of a logistic regression model to predict stroke incidence.
- Evaluation of model performance using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), accuracy, sensitivity, and specificity.
Main Results:
- The logistic regression model achieved 93.8% accuracy and 97.5% ROC-AUC.
- Ventilator dependence, cardiac ejection fraction, and lactate dehydrogenase (LDH) were identified as significant predictors of stroke.
- The model effectively predicted stroke occurrence in COVID-19 patients.
Conclusions:
- The developed model demonstrates high efficacy in predicting stroke in COVID-19 patients.
- Key factors like ventilator dependence, cardiac ejection fraction, and LDH levels are critical indicators for stroke risk assessment.
- The model can aid physicians in clinical decision-making for early stroke diagnosis and management in COVID-19 patients.
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