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Machine Learning and Decision Support in Stroke

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurol. | doi: 10.3389/fneur.2018.00784


 Hendrikus J. A. van Os1, 2*, Lucas A. Ramos3, 4, 5, Adam Hilbert3, 5, Matthijs van Leeuwen6,  Marianne A. A. van Walderveen2, 7, Nyika D. Kruyt1, 2, Diederik W. J. Dippel8, 9, Ewout W. Steyerberg2, 9, Irene C. van der Schaaf10, Hester F. Lingsma9, Wouter J. Schonewille11, Charles B. L. M. Majoie5, Silvia D. Olabarriaga9, Koos H. Zwinderman4, 5,  Esmee Venema8, 9, Henk A. Marquering3, 5 and  Marieke J. H. Wermer1, 2
  • 1Neurology, Leiden University Medical Center, Netherlands
  • 2Biomedical Data Sciences, Leiden University Medical Center, Netherlands
  • 3Biomedical Engineering and Physics, Academic Medical Center (AMC), Netherlands
  • 4Clinical Epidemiology and Biostatistics, Academic Medical Center (AMC), Netherlands
  • 5Radiology and Nuclear Medicine, Academic Medical Center (AMC), Netherlands
  • 6Leiden Institute of Advanced Computer Science, Netherlands
  • 7Radiology, Leiden University Medical Center, Netherlands
  • 8Neurology, Erasmus Medical Center, Erasmus University Rotterdam, Netherlands
  • 9Public Health, Erasmus Medical Center, Erasmus University Rotterdam, Netherlands
  • 10Radiology, University Medical Center Utrecht, Netherlands
  • 11St. Antonius Ziekenhuis, Netherlands

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.
Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥2b) and functional independence (modified Rankin Scale ≤2) at 3 months using 1) only baseline variables and 2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed.
Results: We included 1383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC:0.53-0.57), and moderately in predicting 3-month functional independence (range mean AUC:0.77-0.79) using only baseline variables. All models performed well in predicting 3-month functional independence using both baseline and treatment variables (range mean AUC:0.88-0.91) with a negligible difference of mean AUC (0.01;95%CI:0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).
Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-month functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

Keywords: ischemic stroke, prediction, machine learning, endovascular treatment, functional outcome, Reperfusion

Received: 15 May 2018; Accepted: 30 Aug 2018.

Edited by:

David S. Liebeskind, University of California, Los Angeles, United States

Reviewed by:

Muhib Khan, Michigan State University, United States
Mirjam R. Heldner, Universität Bern, Switzerland  

Copyright: © 2018 van Os, Ramos, Hilbert, van Leeuwen, van Walderveen, Kruyt, Dippel, Steyerberg, van der Schaaf, Lingsma, Schonewille, Majoie, Olabarriaga, Zwinderman, Venema, Marquering and Wermer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: MD. Hendrikus J. A. van Os, Leiden University Medical Center, Neurology, Leiden, Netherlands,