[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124391-en":3,"doc-seo-124391-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124391,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers","Postoperative atrial fibrillation (POAF) remains a common and clinically consequential complication after coronary artery bypass grafting (CABG), driving worse outcomes and higher healthcare costs. This study develops an integrated risk stratification model by combining clinical features with pharmacogenomic biomarkers. A retrospective cohort of CABG patients with 21-gene pharmacogenetic testing was used to train, test, and independently validate eight machine learning algorithms. Gaussian Naive Bayes showed robust predictive performance and highlighted key predictors including multivessel CABG, heart failure history, rs5219, and prolonged bypass duration.","TYPE Original Research PUBLISHED 11 September 2025 DOI 10.3389/fmed.2025.1650700  \nOPEN ACCESS  \nEDITED BY  \nVibhuti Gupta,  \nMeharry Medical College, United States  \nREVIEWED BY  \nSubash Neupane,  \nMississippi State University, United States Richard Matovu,  \nGannon University, United States  \n*CORRESPONDENCE  \nXiangguang Meng  \n [xiangguangmeng2019@163.com](xiangguangmeng2019@163.com)[ ](xiangguangmeng2019@163.com)†These authors have contributed equally to this work  \nRECEIVED 25 June 2025  \nACCEPTED 29 August 2025  \nPUBLISHED 11 September 2025  \nCITATION  \nHua L, Han J, Zhang S, Li Z, Qiao H, Yang Band Meng X (2025) Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers.  \nFront. Med. 12:1650700 .  \ndoi: 10.3389/fmed.2025.1650700  \nCOPYRIGHT  \n© 2025 Hua, Han, Zhang, Li, Qiao, Yang and Meng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers  \nLei Hua1†, Jingxian Han1†, Siqi Zhang1 , Zhiying Li1 , Hui Qiao2 , Bin Yang1 and Xiangguang Meng3*  \n1 Henan Key Laboratory of Cardiac Remodeling and Transplantation, The 7th People’s Hospital of Zhengzhou, Zhengzhou, Henan, P. R. China, 2 Department of Medical Laboratory, The 7th People’s Hospital of Zhengzhou, Zhengzhou, Henan, P. R. China, 3 Department of Pharmacy, The 7th People’s  \nHospital of Zhengzhou, Zhengzhou, Henan, P. R. China  \nBackground: Postoperative atrial fibrillation (POAF) is a frequent complication following coronary artery bypass grafting (CABG), significantly impacting patient prognosis and healthcare costs. This study aimed to develop an integrated predictive model for POAF risk stratification to optimize clinical management.  \nMethods: We retrospectively analyzed 2,528 patients undergoing 21-gene pharmacogenetic testing for cardiovascular therapy. After stringent data curation, 576 CABG patients were enrolled and randomly allocated into training and test sets. Eight machine learning algorithms were trained using clinical variables and genetic variants. An independent validation set was performed on 61 patients from a subsequent 1,075-patient cohort of 21-gene pharmacogenetic testing.  \nResults: Eight machine learning algorithms were trained, tested, and validated, with the Gaussian Naive Bayes (GNB) model demonstrating robust performance (Accuracy: 0.81 in test set and 0.79 in independent validation set) . SHapley Additive exPlanations analysis identified four key predictors: multivessel CABG (CABGVx ≥ 3), history of heart failure (HFHx), rs5219 (KCNJ11), and prolonged bypass duration (CABGTime) . To facilitate clinical translation, we developed an accessible web-based tool ([https://www.xingyeyard.site/cabg/](https://www.xingyeyard.site/cabg/)) for real-time POAF risk stratification.  \nConclusion: This GNB-based classifier synergistically integrates Pharmacogenomic and clinical predictors to predict POAF risk following CABG. The combination of rigorous validation and user-centered design positions this model as a valuable clinical decision-support tool for optimizing personalized perioperative care.  \nKEYWORDS  \ncoronary artery bypass grafting, postoperative atrial fibrillation, machine learning, prediction model, gaussian naive bayes  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \nHua et al. 10.3389/fmed.2025.1650700  \nGRAPHICAL ABSTRACT  \nGNB, gaussian naive bayes; KNN, K-Nearest Neighbors; LR, logistic regression; MLP, multilayer perceptron; RF, random forest; SVM, support vector machine; Ta","cbCailTWcsqhGHk1","https://ap.wps.com/l/cbCailTWcsqhGHk1","pdf",4891598,1,11,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"Why is predicting postoperative atrial fibrillation after CABG important?\",\"answer\":\"POAF is frequent after CABG and can significantly worsen patient prognosis and increase hospital stay and healthcare costs.\"},{\"question\":\"How was the predictive model developed and validated?\",\"answer\":\"The study retrospectively analyzed CABG patients with 21-gene pharmacogenetic testing, trained eight machine learning models on a training set, evaluated them on a test set, and validated performance on an independent validation cohort.\"},{\"question\":\"Which machine learning approach performed best and what key predictors were identified?\",\"answer\":\"Gaussian Naive Bayes demonstrated robust performance and SHapley Additive exPlanations identified multivessel CABG, heart failure history, rs5219 (KCNJ11), and prolonged bypass duration as key predictors.\"}]","Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers | 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is predicting postoperative atrial fibrillation after CABG important?","Question",{"text":75,"@type":76},"POAF is frequent after CABG and can significantly worsen patient prognosis and increase hospital stay and healthcare costs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model developed and validated?",{"text":80,"@type":76},"The study retrospectively analyzed CABG patients with 21-gene pharmacogenetic testing, trained eight machine learning models on a training set, evaluated them on a test set, and validated performance on an independent validation cohort.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performed best and what key predictors were identified?",{"text":84,"@type":76},"Gaussian Naive Bayes demonstrated robust performance and SHapley Additive exPlanations identified multivessel CABG, heart failure history, rs5219 (KCNJ11), and prolonged bypass duration as key 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