Data_Sheet_1_Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.docx

IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.ResultsSixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).ConclusionsConventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524</p

Table_1_Rapid outpatient transient ischemic attack clinic and stroke service activity during the SARS-CoV-2 pandemic: a multicenter time series analysis.docx

Background and aimRapid outpatient evaluation and treatment of TIA in structured clinics have been shown to reduce stroke recurrence. It is unclear whether short-term downtrends in TIA incidence and admissions have had enduring impact on TIA clinic activity. This study aims to measure the impact of the pandemic on hospitals with rapid TIA clinics.MethodsRelevant services were identified by literature search and contacted. Three years of monthly data were requested – a baseline pre-COVID period (April 2018 to March 2020) and an intra-COVID period (April 2020 to March 2021). TIA presentations, ischemic stroke presentations, and reperfusion trends inclusive of IV thrombolysis (IVT) and endovascular thrombectomy (EVT) were recorded. Pandemic impact was measured with interrupted time series analysis, a segmented regression approach to test an effect of an intervention on a time-dependent outcome using a defined impact model.ResultsSix centers provided data for a total of 6,231 TIA and 13,191 ischemic stroke presentations from Australia (52.1%), Canada (35.0%), Italy (7.6%), and England (5.4%). TIA clinic volumes remained constant during the pandemic (2.9, 95% CI –1.8 to 7.6, p = 0.24), as did ischemic stroke (2.9, 95% CI –7.8 to 1.9, p = 0.25), IVT (−14.3, 95% CI −36.7, 6.1, p ConclusionThis suggests that the pandemic has not had an enduring effect on TIA clinic or stroke service activity for these centers. Furthermore, the disproportionate decrease in IVT suggests that patients may be presenting outside the IVT window during the pandemic – delays in seeking treatment in this group could be the target for public health intervention.</p

sj-docx-1-eso-10.1177_23969873231202363 – Supplemental material for Endovascular treatment of cerebral sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia

Supplemental material, sj-docx-1-eso-10.1177_23969873231202363 for Endovascular treatment of cerebral sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia by Johannes Weller, Katarzyna Krzywicka, Anita van de Munckhof, Franziska Dorn, Katharina Althaus, Felix J Bode, Monica Bandettini di Poggio, Brian Buck, Timothy Kleinig, Charlotte Cordonnier, Vanessa Dizonno, Jiangang Duan, Ahmed Elkady, Beng Lim Alvin Chew, Carlos Garcia-Esperon, Thalia S Field, Catherine Legault, Mar Morin Martin, Dominik Michalski, Johann Pelz, Silvia Schoenenberger, Simon Nagel, Marco Petruzzellis, Nicolas Raposo, Mona Skjelland, Domenico Sergio Zimatore, Sanjith Aaron, Mayte Sanchez van Kammen, Diana Aguiar de Sousa, Erik Lindgren, Katarina Jood, Adrian Scutelnic, Mirjam R Heldner, Sven Poli, Antonio Arauz, Adriana B Conforto, Jukka Putaala, Turgut Tatlisumak, Marcel Arnold, Jonathan M Coutinho, Albrecht Günther, Julian Zimmermann and José M Ferro in European Stroke Journal</p