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

Hamann, Janne; Herzog, Lisa; Wehrli, Carina; Dobrocky, Tomas; Bink, Andrea; Piccirelli, Marco; Panos, Leonidas; Kaesmacher, Johannes; Fischer, Urs; Stippich, Christoph; Luft, Andreas R; Gralla, Jan; Arnold, Marcel; Wiest, Roland; Sick, Beate; Wegener, Susanne (2021). Machine learning based outcome prediction in stroke patients with MCA-M1 occlusions and early thrombectomy. European journal of neurology, 28(4), pp. 1234-1243. Wiley 10.1111/ene.14651

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BACKGROUND

Clinical outcome varies substantially between individuals with large vessel occlusion (LVO) stroke. A small infarct core and large mismatch were found to be associated with good recovery. We investigated if those imaging variables improve individual prediction of functional outcome after early (< 6h) 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 (MRI) features. We developed different machine learning models and quantified their prediction performance by the area under the curve (AUC) of receiver operator characteristics (ROC) curves and the Brier score.

RESULTS

Successful recanalization rate was 78%, with 54% patients having a favorable outcome (modified Rankin scale, mRS 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 of likelihood for favorable functional outcome beyond 78 years of age.

CONCLUSIONS

In patients with MCA-M1 occlusion strokes referred to EVT within 6 hours of symptom onset, infarct core volume was associated with outcome. However, ROI based imaging parameters led to no significant improvement in outcome prediction on individual patient level when added to a set of clinical predictors. Our study is in concordance with the current practice, where mismatch imaging or collateral readouts are not recommended for excluding patients with MCA-M1 occlusion for early EVT.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology 04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology 04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
UniBE Contributor:	Dobrocky, Tomas, Kaesmacher, Johannes, Fischer, Urs Martin, Gralla, Jan, Arnold, Marcel, Wiest, Roland Gerhard Rudi
Subjects:	600 Technology > 610 Medicine & health
ISSN:	1468-1331
Publisher:	Wiley
Language:	English
Submitter:	Martin Zbinden
Date Deposited:	17 Dec 2020 14:57
Last Modified:	02 Mar 2023 23:34
Publisher DOI:	10.1111/ene.14651
PubMed ID:	33220140
URI:	https://boris.unibe.ch/id/eprint/149646