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

Matthias A Mutke; Vince I Madai; Adam Hilbert; Esra Zihni; Arne Potreck; Charlotte S Weyland; Markus A Möhlenbruch; Sabine Heiland; Peter A Ringleb; Simon Nagel; Martin Bendszus; Dietmar Frey

doi:10.3389/fneur.2022.737667

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

Front Neurol. 2022 May 27:13:737667. doi: 10.3389/fneur.2022.737667. eCollection 2022.

Authors

Affiliations

¹ Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
² Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany.
³ QUEST (Quality, Ethics, Open Science, Translation) Center for Responsible Research at Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany.
⁴ School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom.
⁵ School of Computing, Technological University Dublin, Dublin, Ireland.
⁶ Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.

Abstract

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.

Keywords: MRI; machine learning; mechanical thrombectomy; mismatch; outcome prediction; perfusion imaging; stroke.