Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke.

Background and Purpose: Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0-2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.
Methods: We retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.
Results: In 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.
Conclusions: Our results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.
Competing Interests: VM reported receiving personal fees from ai4medicine outside the submitted work. AH reported receiving personal fees from ai4medicine outside the submitted work. DF reported receiving grants from the European Commission Horizon2020 PRECISE4Q No. 777107, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. There is no connection, commercial exploitation, transfer, or association between the projects of ai4medicine and the results presented in this work. SN received unrelated fees for consultancy from Brainomix and Boehringer Ingelheim, payment for lectures including service on speakers' bureaus from Pfizer, Medtronic, and Bayer AG. MB received unrelated grants from Siemens, grants and personal fees from Novartis, grants from Stryker, grants from DFG, personal fees from Merck, personal fees from Bayer, personal fees from Teva, grants and personal fees from Guerbet, personal fees from Boehringer, personal fees from Vascular Dynamics, personal fees from Grifols, and grants from the European Union, all outside the submitted work. MMö received unrelated Board Membership from Codman; consultancy from Medtronic, MicroVention, and Stryker; payment for lectures including service on speakers bureaus' from Medtronic, MicroVention, and Stryker. PR received unrelated grants for consultancy from Boehringer and lecture fees from Bayer, Boehringer Ingelheim, BMS, Daichii Sankyo, and Pfizer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Mutke, Madai, Hilbert, Zihni, Potreck, Weyland, Möhlenbruch, Heiland, Ringleb, Nagel, Bendszus and Frey.)