Related Experiment Video
Updated: Jun 8, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre
Jie Jian1,2, Lingqin Zhang3, Songtao He1
1College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Insights
This study developed a Bayesian network model to predict atrial fibrillation (AF) in patients with coronary artery disease (CAD). The model shows high accuracy, offering a potential new tool for large-scale AF screening in this population.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Coexistence of atrial fibrillation (AF) and coronary artery disease (CAD) is common and associated with poor patient outcomes.
- Current international guidelines do not recommend systematic AF screening due to cost and uncertain benefits.
- Early detection of AF in CAD patients is crucial for timely intervention and improved prognosis.
Purpose of the Study:
- To develop and validate a Bayesian network (BN) model for assessing the pre-test probability (PTP) of AF in patients with CAD.
- To explore the potential of BN as a tool for large-scale AF screening in CAD patients.
Main Methods:
- A cohort of 12,552 patients with CAD was analyzed, divided into AF and non-AF groups.
- Risk factors for AF were identified using univariate analysis and LASSO regression.
- A BN model was constructed using the MMHC algorithm, with predictive performance evaluated through internal and external validation, including 10-fold cross-validation.
Main Results:
- Fourteen key indicators were incorporated into the BN model, including age, blood pressure, lipid profiles, liver function tests, cardiac function grade, and symptoms like palpitations and dyspnea.
- The BN model demonstrated strong predictive performance, achieving an AUC of 0.90 on the test set and 0.89 (±0.01) in 10-fold cross-validation.
- The BN model's performance was comparable to four other machine learning algorithms.
Conclusions:
- The developed BN-based prediction model for AF in CAD patients exhibits high predictive accuracy.
- This model shows promise as a novel tool for facilitating large-scale screening of AF in individuals with coronary artery disease.
- Further implementation could aid in early detection and management of AF in high-risk populations.
Background:
Atrial 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.
Methods:
A 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.
Results:
Fourteen 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).
Conclusion:
The 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.
