Are you suffering from a large arterial occlusion? Please raise your arm!. Issue 4 (3rd September 2018)

• Record Type: Journal Article
• Authors: Cooray, Charith; Mazya, Michael V; Bottai, Matteo; Scheitz, Jan F; Abdul-Rahim, Azmil H; Moreira, Tiago Prazeres; Mikulik, Robert; Krajina, Antonin; Nevsimalova, Miroslava; Toni, Danilo; Wahlgren, Nils; Ahmed, Niaz
• Is Part Of: Stroke and vascular neurology. Volume 3:Issue 4(2018)
• Page Start: 215
• Page End: 221
• Publication Date: 2018-09-03
• Subjects: thrombolysis -- thrombectomy -- stroke; Cerebrovascular disease -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; 616.81005
• DOI: 10.1136/svn-2018-000165
• Languages: English
• ISSNs: 2059-8688

Abstract

Abstract : Background and purpose: Triage tools to identify candidates for thrombectomy are of utmost importance in acute stroke. No prognostic tool has yet gained any widespread use. We compared the predictive value of various models based on National Institutes of Health Stroke Scale (NIHSS) subitems, ranging from simple to more complex models, for predicting large artery occlusion (LAO) in anterior circulation stroke. Methods: Patients registered in the SITS international Stroke Register with available NIHSS and radiological arterial occlusion data were analysed. We compared 2042 patients harbouring an LAO with 2881 patients having no/distal occlusions. Using binary logistic regression, we developed models ranging from simple 1 NIHSS-subitem to full NIHSS-subitems models. Sensitivities and specificities of the models for predicting LAO were examined. Results: The model with highest predictive value included all NIHSS subitems for predicting LAO (area under the curve (AUC) 0.77), yielding a sensitivity and specificity of 69% and 76%, respectively. The second most predictive model (AUC 0.76) included 4-NIHSS-subitems (level of consciousness commands, gaze, facial and arm motor function) yielding a sensitivity and specificity of 67% and 75%, respectively. The simplest model included only deficits in arm motor-function (AUC 0.72) for predicting LAO, yielding a sensitivity and specificity of 67% and 72%, respectively. Conclusions: Although increasingly more complex models yield a higher discriminative performance for predicting LAO, differences between models are not large. Assessing grade of arm dysfunction along with an established stroke-diagnosis model may serve as a surrogate measure of arterial occlusion-status, thereby assisting in triage decisions.