ArticleAnnals of medicine2024
The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network.
Article in Annals of medicine, 2024. 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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Who cites it
3 citing papers in PubMed.
- Repurposing Coronary Risk Scores to Identify Increased Likelihood of Atrial Fibrillation in Chronic Coronary Syndrome.Medical sciences (Basel, Switzerland) · 2026Article
- Development and validation of an interpretable machine learning model for predicting atrial fibrillation risk in middle-aged and older patients with coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- A non-invasive prediction model for coronary artery stenosis severity based on multimodal data.Frontiers in physiology · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrial fibrillation (AF) coexisting with coronary artery disease (CAD) remains a prevailing issue that often results in poor short- and long-term patient outcomes. Screening has been proposed as a method to increase AF detection rates and reduce the incidence of poor prognosis through early intervention. Nevertheless, due to the cost implications and uncertainty over the benefits of a systematic screening programme, the International Task Force currently recommends against screening. This study is to employ Bayesian networks (BN) for assessing the pre-test probability (PTP) of AF in patients with CAD.
methodsA total of 12,552 patients with CAD were divided into the CAD patients with AF group (CHD-AF group) and the CAD patients without AF group (non-AF group). Univariate analysis and LASSO regression method were used to screen for potential risk factors. The maximum-minimum climb (MMHC) algorithm was used to construct the directed acyclic graph (DAG) of BN. Predictive power was tested using internal validation, external validation and 10-fold internal cross-validation. Finally, the generated BN model was compared with four machine learning algorithms.
resultsFourteen indicators were included in the BN, including age, gender, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), serum uric acid (UA), gamma-glutamyltransferase (GGT), direct bilirubin (DBIL), lipoproteins [LP(a)], NYHA cardiac function grading, diabetes mellitus and hypertension, palpitation, dyspnoea and the left atrial diameter. The BN model performs well on both the test set (AUC = 0.90) and internal 10-fold cross-validation (AUC = 0.89 ± 0.01).
conclusionThe prediction model of AF with CAD constructed based on BN has high prediction performance and may provide a new tool for large-scale AF screening.
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