Aims
i) to describe the use of the stroke fast track and the proportion of all those evaluated in the fast track actually treated with intravenous thrombolysis, ii) to identify reasons for not giving thrombolysis in patients with acute stroke symptoms \< 4.5 hours at admission to hospital, iii) to investigate whether or not some of these patients that did not receive thrombolysis actually could have been given thrombolysis, iv) to investigate the outcome of patients in the stroke fast track treated or not treated with thrombolysis (or endovascular thrombectomy), v) to investigate differences between stroke and stroke mimics for patients reaching the hospital within or outside the thrombolysis time window of 4.5 hours, vi) to investigate differences between stroke subtypes (both ischemic and hemorrhagic) and stroke mimics for patients reaching the hospital within or outside the thrombolysis time window of 4.5 hours, vii) to investigate outcomes for patients with acute ischemic stroke, hemorrhagic stroke, transient ischemic attack (TIA) or stroke mimics, viii) to investigate predictors and factors related to functional outcome for patients with acute ischemic stroke, hemorrhagic stroke, TIA or stroke mimics, ix) to describe the epidemiology of large vessel occlusions in a representative hospital population, x) to describe hemorrhagic stroke in a representative hospital population, xi) whether risk factors, acute blood pressure variability or imaging (CT, angiography, perfusion or MRI) may predict diagnosis or outcome at discharge, 3 months, 12 months and 2 years for the ASIST-1 population, xii) whether risk factors, acute blood pressure variability or imaging (CT, angiography, perfusion or MRI) may predict outcome at discharge, 3 months, 12 months and 2 years for different sub-types of stroke, xiii) to investigate readmission until 5 years after initial admission for acute stroke symptoms xiv) whether deep learning-based assessment of acute phase CT, CT perfusion and CT angiography can reliably identify infarct core, penumbra and large-vessel occlusion, estimate reliably collateral score, predict risk of adverse events, or guide target blood pressure during acute and subacute ischemic stroke specialized treatment, xv) whether deep learning-based assessment of acute phase CT, CT perfusion and CT angiography or MRI can predict clinical outcome in different types of stroke, xvi) whether deep learning-based assessment of acute phase CT, CT perfusion, CT angiography or MRI can be used for automatic detection of hematoma volume and localization in hemorrhagic stroke xvii) whether deep learning-based assessment of acute phase CT or MRI can predict risk of new incidents after a hemorrhagic stroke and thus guide the clinicians to whether or not patients should be started/re-started on anti-platelet therapy or anticoagulation xviii) to investigate secondary prevention after different subtypes of stroke and the adherence of statins, anti-platelet therapy, anti-coagulation and blood pressure treatment up to 5 years after stroke, also in relation to readmission rates and long-term mortality xix) to prospectively investigate quality in terms of treatment, complications, prognosis and predictive factors of all patients given thrombolysis and/or thrombectomy at Ahus 2019-2025 xx) to investigate the changes in prehospital delay, the use of stroke fast track, stroke pathways and treatment over time (2012-2025).
