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Early differentiation of cardioembolic from atherothrombotic cerebral infarction: a multivariate analysis
A Arboix1, M Oliveres, J Massons
1Acute Stroke Unit, Department of Neurology, Hospital del Sagrat Cor, Barcelona, Spain.
Insights
Clinical features at stroke onset can predict subtype. Atrial dysrhythmia predicts cardioembolic stroke, while hypertension and diabetes predict atherothrombotic stroke, aiding early diagnosis.
Area of Science:
- Neurology
- Cardiovascular Medicine
- Epidemiology
Background:
- Cerebral infarction is a leading cause of disability and death.
- Accurate subtype classification is crucial for effective treatment and prevention.
- Distinguishing between cardioembolic and atherothrombotic stroke is clinically significant.
Purpose of the Study:
- To identify clinical predictors of cerebral infarction subtypes.
- To differentiate between cardioembolic and atherothrombotic stroke using early clinical data.
- To develop a predictive model for stroke mechanism.
Main Methods:
- Analysis of a prospective stroke registry including 231 cardioembolic and 369 atherothrombotic infarctions.
- Comparison of demographic, anamnestic, risk factor, and clinical data.
- Logistic regression modeling using 16 clinical variables to assess predictors.
Main Results:
- Atrial dysrhythmia and sudden onset predicted cardioembolic stroke.
- Hypertension, COPD, diabetes, hyperlipidemia, and age predicted atherothrombotic stroke.
- The model achieved 76% sensitivity, 87% specificity, and 83% correct classification.
Conclusions:
- Clinical features at stroke onset are valuable for differentiating stroke subtypes.
- Early identification of stroke mechanism can guide therapeutic decisions.
- This approach offers a non-invasive method for stroke subtyping.
Abstract:
The aim of this study was to determine factors predictive of cerebral infarction subtype from clinical data collected within 48 h of neurologic deficit. All cardioembolic (n = 231) and atherothrombotic infarctions (n = 369) included in prospective stroke registry of the Sagrat Cor-Alianza Hospital of Barcelona were analysed. Demographic characteristics, anamnestic findings, cerebrovascular risk factors and clinical data of patients with embolic stroke and patients with thrombotic infarction were compared. Predictors of stroke subtype were assessed by means of a logistic regression model based on 16 clinical variables. After multivariate analysis, atrial dysrhythmia and sudden onset to maximal deficit were significant predictors of embolic stroke, whereas hypertension, chronic obstructive pulmonary disease, diabetes, hypercholesterolemia and/or hypertriglyceridemia and age were independent predictive factors of atherothrombotic stroke. Setting a cut-off point of 0.50 for predicting mechanism of stroke on admission resulted in a sensitivity of 76%, specificity of 87% and total correct classification of 83%. Clinical features alone that are observed at stroke onset can help to distinguish cardioembolic from atherothrombotic infarctions.