{
    "abstract": "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.\nA 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.\nFourteen 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\u2009\u00b1\u20090.01).\nThe 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.",
    "authors": [
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.\nMedical Data Science Academy, Chongqing Medical University, Chongqing, China.",
            "firstname": "Jie",
            "initials": "J",
            "lastname": "Jian"
        },
        {
            "affiliation": "Bishan Hospital of Chongqing Medical University, Chongqing, China.",
            "firstname": "Lingqin",
            "initials": "L",
            "lastname": "Zhang"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.",
            "firstname": "Songtao",
            "initials": "S",
            "lastname": "He"
        },
        {
            "affiliation": "Department of Medical Services, The Third Affiliated Hospital of Chongqing, Medical University, Chongqing, China.",
            "firstname": "Wenjuan",
            "initials": "W",
            "lastname": "Wu"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.\nMedical Data Science Academy, Chongqing Medical University, Chongqing, China.",
            "firstname": "Yang",
            "initials": "Y",
            "lastname": "Zhang"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.\nMedical Data Science Academy, Chongqing Medical University, Chongqing, China.",
            "firstname": "Chang",
            "initials": "C",
            "lastname": "Jian"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.\nMedical Data Science Academy, Chongqing Medical University, Chongqing, China.",
            "firstname": "Mingxuan",
            "initials": "M",
            "lastname": "Xie"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.\nMedical Data Science Academy, Chongqing Medical University, Chongqing, China.",
            "firstname": "Tingting",
            "initials": "T",
            "lastname": "Wang"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.",
            "firstname": "Bo",
            "initials": "B",
            "lastname": "Liang"
        },
        {
            "affiliation": "College of Medical Informatics, Chongqing Medical University, Chongqing, China.",
            "firstname": "Xingliang",
            "initials": "X",
            "lastname": "Xiong"
        }
    ],
    "conclusions": "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.",
    "copyrights": null,
    "doi": "10.1080/07853890.2024.2423789\n10.31083/j.rcm.2019.01.53\n10.3390/ijms23010006\n10.1016/j.jacc.2019.07.020\n10.31128/AJGP-12-18-4787\n10.1161/CIRCULATIONAHA.113.005119\n10.2147/CLEP.S47385\n10.1007/s00392-011-0357-5\n10.1016/j.clinthera.2014.08.010\n10.3389/fcvm.2022.1036609\n10.1007/s11886-021-01625-w\n10.1093/europace/euac088\n10.3390/ijerph19116860\n10.1093/eurheartj/ehz834\n10.3390/jpm12081325\n10.1016/j.compbiomed.2022.105740\n10.1016/j.cmpb.2015.12.010\n10.1177/0272989X19879095\n10.15420/aer.2021.07\n10.1186/s13104-015-1488-y\n10.1080/0886022X.2020.1810068\n10.1016/j.jval.2019.01.006\n10.1038/s41598-022-11125-8\n10.3390/medicina57060605\n10.1136/openhrt-2016-000573\n10.1097/HJH.0b013e3283522a51\n10.1161/CIRCRESAHA.117.311402\n10.1161/JAHA.114.001723\n10.11909/j.issn.1671-5411.2019.09.004\n10.1136/heartjnl-2014-306463\n10.1161/HYPERTENSIONAHA.115.05728\n10.7326/0003-4819-61-5-888\n10.1016/j.cca.2016.12.003\n10.1080/07853890.2017.1377846\n10.1177/0003319712448247\n10.1161/CIRCULATIONAHA.115.018614\n10.1161/CIRCGEN.120.003271\n10.1177/03000605221109387\n10.1161/JAHA.118.011433\n10.1093/eurheartj/ehx144\n10.1093/cvr/cvab017\n10.1093/europace/eux013\n10.1161/01.cir.89.2.724\n10.12659/MSM.936446\n10.3389/fcvm.2022.1004178\n10.3389/fonc.2022.939226",
    "journal": "Annals of medicine",
    "keywords": [
        "Bayesian network",
        "Coronary artery disease",
        "atrial fibrillation",
        "disease prediction"
    ],
    "methods": null,
    "publication_date": "2024-11-12",
    "pubmed_id": "39508083\n31613540\n31184093\n35008432\n31439220\n31569325\n24345399\n24966695\n21910032\n25234549\n36568540\n34993746\n35983729\n35682443\n31811716\n36013274\n35779477\n26777431\n31619130\n34401177\n26423575\n32838613\n30975395\n35534641\n34208175\n28123767\n22495138\n29348255\n26206736\n31645851\n27126397\n26056336\n14233810\n27939920\n28879775\n22649112\n26746177\n34279996\n35850541\n31137992\n28444290\n33483737\n28444307\n8313561\n35614578\n36211576\n35992828",
    "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\u2009\u00b1\u20090.01).",
    "title": "The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network.",
    "xml": "<Element 'PubmedArticle' at 0x788505afeb10>"
}