Purpose: Various studies have identified prognostic factors for a favorable outcome of endovascular treatment in posterior circulation. We evaluated various machine learning algorithms in their ability to classify between patients with favorable (defined as 0-2 points on the modified Rankin scale [mRS]), unfavorable (mRS 3-6), poor (mRS 5-6), and nonpoor (mRS 0-4) outcomes at dismissal.
Methods: We retrospectively analyzed data from 415 patients that were treated between 2018 and 2021 from the multicentric DGNR registry. Five models (random forest, support vector machine, k-nearest neighbor, neural network [NN], and generalized linear model [GLM]) were trained with clinical input variables and evaluated with a test dataset of 82 patients. The model with the highest accuracy on the training dataset was defined as the best model.
Results: A total of 132 patients showed poor and 162 patients showed favorable outcome. All baseline variables except sex were highly significantly different between patients with favorable and unfavorable outcomes. The variables NIHSS, the presence of wake-up stroke, the administration of IV-thrombolysis and mRS pretreatment were significantly different between patients with poor and nonpoor outcomes. The best-performing NN achieved a sensitivity of 0.56, a specificity of 0.86 and an area under the curve (AUC) of 0.77 on the test dataset in the classification analysis between favorable and unfavorable outcomes. The best-performing GLM achieved a sensitivity of 0.65, a specificity of 0.91 and an AUC of 0.81 in the classification analysis between poor and nonpoor outcomes.
Conclusion: Short-term favorable and poor outcomes in patients with acute ischemic stroke of the posterior circulation can be predicted prior to thrombectomy with moderate sensitivity and high specificity with machine learning models.
Keywords: Machine learning; ischemic stroke; posterior circulation; thrombectomy; wake-up stroke.