ArticleInternational journal of cardiology. Heart & vasculature2021
Risk prediction for new-onset atrial fibrillation using the Minnesota code electrocardiography classification system.
Article in International journal of cardiology. Heart & vasculature, 2021. 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, 6 citations in OpenAlex.
- Comprehensive Health Assessment Using Risk Prediction for Multiple Diseases Based on Health Checkup Data.AJPM focus · 2024Article
- Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- Exploring the causal association of rheumatoid arthritis with atrial fibrillation: a Mendelian randomization study.Clinical rheumatology · 2024Article
Corrections and comments
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Authors and funding
18 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFew risk models are available to predict future onset of atrial fibrillation (AF) in workers. We aimed to develop risk prediction models for new-onset AF, using annual health checkup (HC) data with electrocardiogram findings. METHODS AND
resultsWe retrospectively included 56,288 factory or office workers (mean age = 51.5 years, 33.0% women) who underwent a HC at a medical center and fulfilled the following criteria; age
conclusionsWe developed useful risk model with Minnesota code information for predicting new-onset AF from large worker population validated in the original and external cohorts, although study interpretation is limited by small improvement of AUC.
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