ArticleHealth science reports2025
Prognostic Models of Mortality Following First-Ever Acute Ischemic Stroke: A Population-Based Retrospective Cohort Study.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Clinical guideline-guided prognostic models of functional disability after acute ischemic stroke: a retrospective cohort study.BMC neurology · 2026Article
- Development and validation of a population-based prediction model for prevalent hypertension: evidence from Qatar biobank.Frontiers in cardiovascular medicine · 2026Article
- Direct medical cost of first-ever acute ischemic stroke in malaysia: a retrospective cohort study.Scientific reports · 2025Article
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Authors and funding
11 authors.
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Abstract
Background and Aims: There is a lack of population-based studies focusing on guideline-based prognostic models for stroke. This study aimed to develop and validate a prognostic model that predicts mortality following a first-ever acute ischemic stroke. Methods: The study included 899 adult patients ( ≥ 18 years) with confirmed diagnosis of first-ever acute ischemic stroke enrolled in the Malaysian National Stroke Registry (NSR) from January 2009 to December 2019. The primary outcome was mortality within 90 days post-stroke (266 events [29.6%]). The prognostic model was developed using logistic regression (75%, Results: The final model includes factors associated with increased risk of mortality, such as age (adjusted odds ratio, aOR 1.06 [95% confidence interval, CI 1.03, 1.10; Conclusion: The study developed a validated prognostic model that excellently predicts mortality after a first-ever acute ischemic stroke with potential clinical utility in acute stroke care decision-making. The predictors could be valuable for creating risk calculators and aiding healthcare providers and patients in making well-informed clinical decisions during the stroke care process.
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