Value of machine learning to predict functional outcome of endovascular treatment for acute ischaemic stroke of the posterior circulation

Purpose: Clinical outcomes vary considerably among individuals with vessel occlusion of the posterior circulation. In the present study we evaluated machine learning algorithms in their ability to discriminate between favourable and unfavourable outcomes in patients with endovascular treatment of acute ischaemic stroke of the posterior circulation. Methods: This retrospective study evaluated three algorithms (generalised linear model, K-nearest neighbour and random forest) to predict functional outcomes at dismissal of 30 patients with acute occlusion of the basilar artery who were treated with thrombectomy. Input variables encompassed baseline as well as peri and postprocedural data. Favourable outcome was defined as a modified Rankin scale score of 0-2 and unfavourable outcome was defined as a modified Rankin scale score of 3-6. The performance of the algorithms was assessed with the area under the receiver operating curve and with confusion matrixes. Results: Successful reperfusion was achieved in 83%, with 30% of the patients having a favourable outcome. The area under the curve was 0.93 for the random forest model, 0.86 for the K-nearest neighbour model and 0.78 for the generalised linear model. The accuracy was 0.69 for the generalised linear model and 0.84 for the random forest and the K nearest neighbour models. Conclusion: Favourable and unfavourable outcomes at dismissal of patients with acute ischaemic stroke of the posterior circulation can be predicted immediately after the follow-up non-enhanced computed tomography using machine learning.