Literature DB >> 33220140

Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy.

Janne Hamann1, Lisa Herzog1,2,3, Carina Wehrli1,4, Tomas Dobrocky5, Andrea Bink4, Marco Piccirelli4, Leonidas Panos6, Johannes Kaesmacher5,7, Urs Fischer6, Christoph Stippich4, Andreas R Luft1, Jan Gralla5, Marcel Arnold6, Roland Wiest5, Beate Sick2,3, Susanne Wegener1.   

Abstract

BACKGROUND AND
PURPOSE: Clinical outcomes vary substantially among individuals with large vessel occlusion (LVO) stroke. A small infarct core and large imaging mismatch were found to be associated with good recovery. The aim of this study was to investigate whether those imaging variables would improve individual prediction of functional outcome after early (<6 h) endovascular treatment (EVT) in LVO stroke.
METHODS: We included 222 patients with acute ischemic stroke due to middle cerebral artery (MCA)-M1 occlusion who received EVT. As predictors, we used clinical variables and region of interest (ROI)-based magnetic resonance imaging features. We developed different machine-learning models and quantified their prediction performance according to the area under the receiver-operating characteristic curves and the Brier score.
RESULTS: The rate of successful recanalization was 78%, with 54% patients having a favorable outcome (modified Rankin scale score 0-2). Small infarct core was associated with favorable functional outcome. Outcome prediction improved only slightly when imaging was added to patient variables. Age was the driving factor, with a sharp decrease in likelihood of favorable functional outcome above the age of 78 years.
CONCLUSIONS: In patients with MCA-M1 occlusion strokes referred to EVT within 6 h of symptom onset, infarct core volume was associated with outcome. However, ROI-based imaging variables led to no significant improvement in outcome prediction at an individual patient level when added to a set of clinical predictors. Our study is in concordance with current practice, where imaging mismatch or collateral readouts are not recommended as factors for excluding patients with MCA-M1 occlusion for early EVT.
© 2020 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.

Entities:  

Keywords:  machine learning; stroke outcome prediction

Year:  2020        PMID: 33220140     DOI: 10.1111/ene.14651

Source DB:  PubMed          Journal:  Eur J Neurol        ISSN: 1351-5101            Impact factor:   6.089


  7 in total

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

Authors:  Ludger Feyen; Peter Schott; Hendrik Ochmann; Marcus Katoh; Patrick Haage; Patrick Freyhardt
Journal:  Neuroradiol J       Date:  2021-10-05

Review 2.  Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.

Authors:  Nathan A Shlobin; Ammad A Baig; Muhammad Waqas; Tatsat R Patel; Rimal H Dossani; Megan Wilson; Justin M Cappuzzo; Adnan H Siddiqui; Vincent M Tutino; Elad I Levy
Journal:  World Neurosurg       Date:  2021-12-08       Impact factor: 2.210

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

Authors:  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
Journal:  Front Neurol       Date:  2022-05-27       Impact factor: 4.086

4.  MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke.

Authors:  Liang Jiang; Chuanyang Zhang; Siyu Wang; Zhongping Ai; Tingwen Shen; Hong Zhang; Shaofeng Duan; Xindao Yin; Yu-Chen Chen
Journal:  Front Aging Neurosci       Date:  2022-03-03       Impact factor: 5.750

5.  Clinical Significance and Dynamic Change of Coagulation Parameters in Ischemic Stroke Patients Treated with Intravenous Thrombolysis.

Authors:  Guangshuo Li; Chuanying Wang; Shang Wang; Yahui Hao; Yunyun Xiong; Xingquan Zhao
Journal:  Clin Appl Thromb Hemost       Date:  2022 Jan-Dec       Impact factor: 3.512

6.  Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.

Authors:  Minyan Zeng; Lauren Oakden-Rayner; Alix Bird; Luke Smith; Zimu Wu; Rebecca Scroop; Timothy Kleinig; Jim Jannes; Mark Jenkinson; Lyle J Palmer
Journal:  Front Neurol       Date:  2022-09-08       Impact factor: 4.086

7.  Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke.

Authors:  Weixiong Zeng; Wei Li; Kaibin Huang; Zhenzhou Lin; Hui Dai; Zilong He; Renyi Liu; Zhaodong Zeng; Genggeng Qin; Weiguo Chen; Yongming Wu
Journal:  Front Neurol       Date:  2022-09-28       Impact factor: 4.086

  7 in total

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