Abstract WP239: Development and Validation of The California Stroke Subtype (CASS) Scale for Prehospital Identification of Intracerebral Hemorrhage

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Background: We sought to develop a clinical scale identifying intracerebral hemorrhage (ICH) using prospectively collected data elicited by paramedics in the field.

Methods: Subjects were enrolled in the Field Administration of Stroke Therapy Magnesium (FAST-MAG) trial of prehospital neuroprotective therapy. Data obtained by paramedics in the field including vital signs, examination, demographic information, and medications. Subjects were randomly put into training (n=1133) and validation (n=567) observations. Logistic regression model using all 26 potential predictors as candidates was fit to the training data using backward stepwise variable selection with a liberal p < 0.10 retention criterion. A classification tree model using 26 potential predictors as candidates was used with a GINI equivalent to a p < 0.10 splitting criterion.

Results: 1700 cases were assessed by paramedics a median of 23 (IQR 14-42) minutes after symptom onset and 23% had ICH. Of the 26 candidates, 12 variables were retained in the logistic model. Holding the other factors constant, increasing Los Angeles Motor Score (LAMS), Glasgow Coma Scale (GCS) verbal sub-score, history of hypertension, Hispanic ethnicity, field systolic BP and taking anti platelet medication are associated with increasing risk of ICH. Increasing GCS eyes sub-score, diabetes, atrial fibrillation, valvular heart disease, age, female gender, and Black race are associated with decreasing risk of ICH. The training set model accuracy is 74%, sensitivity 65%, specificity 83% with C= 0.81 and the validation set accuracy is 67%, sensitivity 76%, specificity 58% with C= 0.73. For the classification tree seven predictors are retained: prehospital systolic BP, diastolic BP, Hispanic, ethnicity, history of atrial fibrillation, GCS verbal sub-score, age and Los Angeles Motor Score (LAMS). The tree forms 10 prediction groups (terminal nodes). Five of these ten predict ICH (“positive”) and the other five predict no ICH (“negative”). The training set accuracy for the tree was 71.9% with C=0.782 and the validation set accuracy was 63.2% with C= 0.632.

Conclusion: Paramedics can identify ICH in the field with moderate accuracy, allowing the opportunity to develop targeted prehospital therapeutics and care delivery.

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