ArticleInternational scholarly research notices2014
Outcome Predictors in First-Ever Ischemic Stroke Patients: A Population-Based Study.
Article in International scholarly research notices, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Interaction between age and atrial fibrillation on ischemic stroke severity: a cross-sectional analysis.Frontiers in stroke · 2026Article
- The Role of Complete Blood Count-Derived Inflammatory Biomarkers as Predictors of Infection After Acute Ischemic Stroke: A Single-Center Retrospective Study.Medicina (Kaunas, Lithuania) · 2024Article
- A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients.Journal of stroke · 2024Article
- Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction.Frontiers in neurology · 2022Article
- Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy.European journal of neurology · 2021Article
- Prediction of the Lethal Outcome of Acute Recurrent Cerebral Ischemic Hemispheric Stroke.Medicina (Kaunas, Lithuania) · 2019Article
- Assessing upper limb function: transcultural adaptation and validation of the Portuguese version of the Stroke Upper Limb Capacity Scale.BMC sports science, medicine & rehabilitation · 2017Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background. There is scant population-based information regarding predictors of stroke severity and long-term mortality for first-ever ischemic strokes. The aims of this study were to determine the characteristics of patients who initially presented with first-ever ischemic stroke and to identify predictors of severity and long-term mortality. Methods. Data were collected from the population-based Cerebrovascular Aosta Registry. Between 2004 and 2008, 1057 patients with first-ever ischemic stroke were included. Variables analysed included comorbidities, sociodemographic factors, prior-to-stroke risk factors, therapy at admission and pathophysiologic and metabolic factors. Multivariate logistic regression models, Kaplan-Meier estimates, and Cox proportional Hazards model were used to assess predictors. Results. Predictors of stroke severity at admission were very old age (odds ratio [OR] 2.98, 95% confidence interval [CI] 1.75-5.06), female gender (OR 1.73, 95% CI 1.21-2.40), atrial fibrillation (OR 2.76, 95% CI 1.72-4.44), low ejection fraction (OR 2.22, CI 95% 1.13-4.32), and cardioembolism (OR 2.0, 95% CI 1.36-2.93). Predictors of long-term mortality were very old age (hazard ratio [HR] 2.02, 95% CI 1.65-2.47), prestroke modified Rankin scale 3-5 (HR 1.82; 95% CI 1.46-2.26), Charlson Index ≥2 (HR 1.97; 95% CI 1.62-2.42), atrial fibrillation (HR 1.43, 95% CI 1.04-1.98), and stroke severity (HR 3.54, 95% CI 2.87-4.36). Conclusions. Very old age and cardiac embolism risk factors are the independent predictors of stroke severity. Moreover, these factors associated with other comorbid medical conditions influence independently long-term mortality after ischemic stroke.
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