ArticleFrontiers in cardiovascular medicine2022
Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 15 citations in OpenAlex.
- The PAFIS score for risk stratification of atrial fibrillation detected after stroke: a multicenter cohort study.International journal of cardiology. Heart & vasculature · 2026Article
- Metabolic disorder-MASLD-cardiovascular disease-kidney disease (MMCK) syndrome: an expansion of the Cardiovascular-Kidney-Metabolic (CKM) framework.Cardiovascular diabetology. Endocrinology reports · 2026Review
- External Validation of the AS5F Score and the Role of Left Atrial Dilatation in Post-Stroke/TIA Atrial Fibrillation Detection.Biomedicines · 2026Article
- Enhanced Prediction of Atrial Fibrillation in Patients With Ischemic Stroke Through Electronic Medical Records and Text Mining: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.EBioMedicine · 2025Article
- Predicting Atrial Fibrillation After Stroke by Combining Polygenic Risk Scores and Clinical Features.Stroke · 2025Article
- Screening for atrial fibrillation after stroke: is targeted patient selection the key?Arquivos de neuro-psiquiatria · 2024Article
- Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study.Journal of neurology · 2023Observational
- Incremental value of the combined brain-cardiac CT protocol on prediction of atrial fibrillation after stroke.European stroke journal · 2023Article
- Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke.International journal of environmental research and public health · 2022Article
- A nomogram for predicting atrial fibrillation detected after acute ischemic stroke.Frontiers in neurology · 2022Article
- Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.Frontiers in cardiovascular medicine · 2022Article
- Utility of the ECG-Based Simple Score for Predicting Post-Stroke Atrial Fibrillation in a Real-World Clinical Setting.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisObservational
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS). Methods: We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C Results: The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, Conclusion: The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.
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