PT  - JOURNAL ARTICLE
AU  - Alessandro Davoli
AU  - Caterina Motta
AU  - Giacomo Koch
AU  - Marina Diomedi
AU  - Simone Napolitano
AU  - Angela Giordano
AU  - Marta Panella
AU  - Daniele Morosetti
AU  - Sebastiano Fabiano
AU  - Roberto Floris
AU  - Roberto Gandini
AU  - Fabrizio Sallustio
TI  - Pretreatment predictors of malignant evolution in patients with ischemic stroke undergoing mechanical thrombectomy
AID  - 10.1136/neurintsurg-2017-013224
DP  - 2017 Aug 05
TA  - Journal of NeuroInterventional Surgery
PG  - neurintsurg-2017-013224
4099  - http://jnis.bmj.com/content/early/2017/08/05/neurintsurg-2017-013224.short
4100  - http://jnis.bmj.com/content/early/2017/08/05/neurintsurg-2017-013224.full
AB  - Background Few data exist on malignant middle cerebral artery infarction (MMI) among patients with acute ischemic stroke (AIS) after endovascular treatment (ET). Numerous predictors of MMI evolution have been proposed, but a comprehensive research of patients undergoing ET has never been performed. Our purpose was to find a practical model to determine robust predictors of MMI in patients undergoing ET.Methods Patients from a prospective single-center database with AIS secondary to large intracranial vessel occlusion of the anterior circulation, treated with ET, were retrospectively analyzed. We investigated demographic, clinical, and radiological data. Multivariate regression analysis was used to identify clinical and imaging predictors of MMI.Results 98 patients were included in the analysis, 35 of whom developed MMI (35.7%). No differences in the rate of successful reperfusion and time from stroke onset to reperfusion were found between the MMI and non-MMI groups. The following parameters were identified as independent predictors of MMI: systolic blood pressure (SBP) on admission (p=0.008), blood glucose (BG) on admission (p=0.024), and the CTangiography (CTA) Alberta Stroke Program Early CT Score (ASPECTS) (p=0.001). A scoreof ≤5 in CTA ASPECTS was the best cut-off to predict MMI evolution (sensitivity 46%; specificity 97%; positive predictive value 78%; negative predictive value 65%).Conclusions in our study a clinical and radiological features-based model was strongly predictive of MMI evolution in AIS. High SBP and BG on admission and, especially, a CTA ASPECTS ≤5 may help to make decisions quickly, regardless of time to treatment and successful reperfusion.