ArticleFrontiers in neurology2022
A nomogram for predicting atrial fibrillation detected after acute ischemic stroke.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Serum biomarkers insights into occult atrial fibrillation related to embolic strokes of undetermined source: a narrative review.BMC neurology · 2026Pooled it
- Risk Factors and Outcomes Associated with Atrial Fibrillation Detected after Intracerebral Hemorrhage.Neurocritical care · 2026Article
- Atrial fibrillation detected after ischemic stroke (AFDAS) diagnosed by short-term monitoring: the importance of frontal hypoperfusion.Journal of thrombosis and thrombolysis · 2026Article
- C-reactive protein elevation and subsequent atrial fibrillation detection after ischemic stroke treated with mechanical thrombectomy: An inverse probability weighting analysis.International journal of stroke : official journal of the International Stroke Society · 2025Observational
- Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.EBioMedicine · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Stroke-heart syndrome: current progress and future outlook.Journal of neurology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS). Methods: This study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort ( Results: We found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ Conclusion: Predictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS.
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