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

1. Demel, SL, Broderick, JP. Basilar occlusion syndromes: an update. Neurohospitalist 2015; 5: 142–150.
Google Scholar | SAGE Journals | ISI2. Gory, B, Eldesouky, I, Sivan-Hoffmann, R, et al. Outcomes of stent retriever thrombectomy in basilar artery occlusion: an observational study and systematic review. J Neurol Neurosurg Psychiatry 2016; 87: 520–525.
Google Scholar | Crossref | Medline3. Goyal, M, Menon, BK, van Zwam, WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 2016; 387: 1723–1731.
Google Scholar | Crossref | Medline | ISI4. Phan, K, Phan, S, Huo, YR, et al. Outcomes of endovascular treatment of basilar artery occlusion in the stent retriever era: a systematic review and meta-analysis. J Neurointerv Surg 2016; 8: 1107–1115. 2015/11/28.
Google Scholar | Crossref | Medline5. Powers, WJ, Rabinstein, AA, Ackerson, T, et al. 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2018; 49: e46–e110.
Google Scholar | Crossref | Medline6. Ravindren, J, Aguilar Perez, M, Hellstern, V, et al. Predictors of outcome after endovascular thrombectomy in acute basilar artery occlusion and the 6 hr time window to recanalization. Front Neurol 2019; 10: 923.
Google Scholar | Crossref | Medline7. Alexandre, AM, Valente, I, Consoli, A, et al. Posterior circulation endovascular thrombectomy for large-vessel occlusion: predictors of favorable clinical outcome and analysis of first-pass effect. AJNR Am J Neuroradiol 2021; 42: 896–903.
Google Scholar | Crossref | Medline8. Gory, B, Mazighi, M, Blanc, R, et al. Mechanical thrombectomy in basilar artery occlusion: influence of reperfusion on clinical outcome and impact of the first-line strategy (ADAPT vs stent retriever). J Neurosurg 2018; 129: 1482–1491.
Google Scholar | Crossref | Medline9. Gory, B, Mazighi, M, Labreuche, J, et al. Predictors for mortality after mechanical thrombectomy of acute basilar artery occlusion. Cerebrovasc Dis 2018; 45: 61–67.
Google Scholar | Crossref | Medline10. Mahmoudi, M, Dargazanli, C, Cagnazzo, F, et al. Predictors of favorable outcome after endovascular thrombectomy in MRI: selected patients with acute basilar artery occlusion. AJNR Am J Neuroradiol 2020; 41: 1670–1676.
Google Scholar | Medline11. van Os, HJA, Ramos, LA, Hilbert, A, et al. Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front Neurol 2018; 9: 784.
Google Scholar | Crossref | Medline12. Brugnara, G, Neuberger, U, Mahmutoglu, MA, et al. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke 2020; 51: 3541–3551.
Google Scholar | Crossref | Medline13. Nishi, H, Oishi, N, Ishii, A, et al. Deep learning-derived high-level neuroimaging features predict clinical outcomes for large vessel occlusion. Stroke 2020; 51: 1484–1492.
Google Scholar | Crossref | Medline14. Hamann, J, Herzog, L, Wehrli, C, et al. Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy. Eur J Neurol 2020; 28(4): 1234–1243.
Google Scholar15. Teo, YH, Lim, I, Tseng, FS, et al. Predicting clinical outcomes in acute ischemic stroke patients undergoing endovascular thrombectomy with machine learning: a systematic review and meta-analysis. Online ahead of print. Clin Neuroradiol 2021; doi: 10.1007/s00062-020-00990-3.
Google Scholar16. Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26. doi: http://dx.doi.org/10.18637/jss.v028.i05
Google Scholar17. R Core Team . R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2019.
Google Scholar18. Uddin, S, Khan, A, Hossain, ME, et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 2019; 19: 281.
Google Scholar | Crossref | Medline19. Breiman, L. Random forests. Machine Learning 2001; 45: 5–32.
Google Scholar | Crossref | ISI20. Lin, SF, Chen, CI, Hu, HH, et al. Predicting functional outcomes of posterior circulation acute ischemic stroke in first 36 h of stroke onset. J Neurol 2018; 265: 926–932.
Google Scholar | Crossref | Medline21. Jadhav, AP, Desai, SM, Panczykowski, DM, et al. Predicting outcomes after acute reperfusion therapy for basilar artery occlusion. Eur J Neurol 2020; 27: 2176–2184.
Google Scholar | Crossref | Medline22. De Marchis, GM, Kohler, A, Renz, N, et al. Posterior versus anterior circulation strokes: comparison of clinical, radiological and outcome characteristics. J Neurol Neurosurg Psychiatry 2011; 82: 33–37.
Google Scholar | Crossref | Medline23. London, AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep 2019; 49: 15–21.
Google Scholar | Crossref | Medline

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