Machine Learning–Based Identification of Target Groups for Thrombectomy in Acute Stroke

Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical practice (N = 5235, German-Stroke-Registry) and in patients treated with EVT or best medical management from RCTs (N = 1488, Virtual-International-Stroke-Trials-Archive). The importance of reperfusion level on outcome prediction in an RCT-like real-world cohort equaled the importance of EVT treatment allocation for outcome prediction in RCT data and was higher compared to an unselected real-world population. The importance of reperfusion level was magnified in patient groups underrepresented in RCTs, including patients with lower NIHSS scores (0–10), M2 occlusions, and lower ASPECTS (0–5 and 6–8). Reperfusion level was equally important in patients with vertebrobasilar as with anterior LVO stroke. The importance of reperfusion level for outcome prediction identifies patient target groups who likely benefit from EVT, including vertebrobasilar stroke patients and among patients underrepresented in RCT patients with low NIHSS scores, low ASPECTS, and M2 occlusions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12975-022-01040-5.