Abstract
Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model.
Highlights
The prevalence of transienti ischemic attack (TIA) is estimated to be 103.3 per 100,000 in the Chinese population [1]
The performance of artificial neural network (ANN) models in identifying patients with recurrent ischemic stroke was better than that of Support Vector Machine (SVM) and Naïve Bayes classifiers (NBC) algorithm (Table 2). This pilot study demonstrated the feasibility of using ANN to predict the risk of recurrent stroke within 1 year after a TIA or minor stroke, based on parameters that are readily available in clinical practice
Under the modern stroke service system, timely attention and management for TIA and minor stroke patients are becoming more readily available, which has significantly reduced the risk of stroke relapse in these patients
Summary
The prevalence of transienti ischemic attack (TIA) is estimated to be 103.3 per 100,000 in the Chinese population [1]. Other factors have been considered to supplement the ABCD2 score, for instance, presence of new infarct(s) and carotid arterial stenosis and dual TIA, to form the ABCD3-I score [9] These new scoring systems have been well validated in other populations, reporting the c-statistics of 0.60–0.64 in predicting the recurrent stroke within 3 month following TIA. New scores such as ABCD3-I score have not been recommended for risk stratification in such patients by the guidelines by far [10, 11]. We investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients
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