{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:59:52Z","timestamp":1773824392190,"version":"3.50.1"},"reference-count":34,"publisher":"Georg Thieme Verlag KG","issue":"05","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Chongqing Clinical Pharmacy Key Specialties Construction Project","award":["51561Z23772"],"award-info":[{"award-number":["51561Z23772"]}]},{"name":"Chongqing municipal Education Commission Science and Technology Research Program","award":["KJZD-M202212801"],"award-info":[{"award-number":["KJZD-M202212801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Thromb Haemost"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Oral anticoagulation (OAC) following catheter ablation (CA) of nonvalvular atrial fibrillation (NVAF) is essential for the prevention of thrombosis events. Inappropriate application of OACs does not benefit stroke prevention but may be associated with a higher risk of bleeding. Therefore, this study aims to develop clinical data-driven machine learning (ML) methods to predict the risk of thrombosis and bleeding to establish more precise anticoagulation strategies for patients with NVAF.<\/jats:p><jats:p>Patients with NVAF who underwent CA therapy were enrolled from Southwest Hospital from 2015 to 2023. This study compared eight ML algorithms to evaluate the predictive power for both thrombosis and bleeding. Model interpretations were recognized by feature importance and SHapley Additive exPlanations methods. With potential essential risk factors, simplified ML models were proposed to improve the feasibility of the tool.<\/jats:p><jats:p>A total of 1,055 participants were recruited, including 105 patients with thrombosis and 252 patients with bleeding. The models based on XGBoost achieved the best performance with accuracies of 0.740 and 0.781 for thrombosis and bleeding, respectively. Age, BNP, and the duration of heparin are closely related to the high risk of thrombosis, whereas the anticoagulation strategy, BNP, and lipids play a crucial role in the occurrence of bleeding. The optimized models enrolling crucial risk factors, RF-T for thrombosis and Xw-B for bleeding, achieved the best recalls of 0.774 and 0.780, respectively.<\/jats:p><jats:p>The optimized models will have a great application potential in predicting thrombosis and bleeding among patients with NVAF and will form the basis for future score scales.<\/jats:p>","DOI":"10.1055\/a-2385-1452","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T23:17:20Z","timestamp":1723591040000},"page":"492-504","source":"Crossref","is-referenced-by-count":5,"title":["Harnessing Risk Assessment for Thrombosis and Bleeding to Optimize Anticoagulation Strategy in Nonvalvular Atrial Fibrillation"],"prefix":"10.1055","volume":"125","author":[{"given":"Yue","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Ya","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Pharmacy, Army Medical Center, Army Medical University (Third Military Medical University), Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Fan","family":"Huang","sequence":"additional","affiliation":[{"name":"Medical Big Data and Artificial Intelligence Center, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Medical Big Data and Artificial Intelligence Center, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"issue":"01","key":"ref1","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/europace\/euz235","article-title":"The safety of discontinuation of oral anticoagulation therapy after apparently successful atrial fibrillation ablation: a report from the Chinese Atrial Fibrillation Registry study","volume":"22","author":"W-Y Yang","year":"2020","journal-title":"Europace"},{"issue":"31","key":"ref2","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1093\/eurheartj\/ehw087","article-title":"Catheter ablation for atrial fibrillation is associated with lower incidence of stroke and death: data from Swedish health registries","volume":"37","author":"L Friberg","year":"2016","journal-title":"Eur Heart J"},{"issue":"47","key":"ref3","doi-asserted-by":"crossref","first-page":"4899","DOI":"10.1093\/eurheartj\/ehac587","article-title":"Bleeding and ischaemic events after first bleed in anticoagulated atrial fibrillation patients: risk and timing","volume":"43","author":"P B Meyre","year":"2022","journal-title":"Eur Heart J"},{"issue":"20","key":"ref4","first-page":"2141","article-title":"Major bleeding in patients with atrial fibrillation receiving apixaban or warfarin: the ARISTOTLE Trial (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation): predictors, characteristics, and clinical outcomes","volume":"63","author":"E M Hylek","year":"2014","journal-title":"J Am Coll Cardiol"},{"issue":"08","key":"ref5","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1093\/europace\/euaa365","article-title":"Long-term oral anticoagulant after catheter ablation for atrial fibrillation","volume":"23","author":"D Chew","year":"2021","journal-title":"Europace"},{"issue":"13","key":"ref6","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1001\/jama.2019.0692","article-title":"Effect of catheter ablation vs medical therapy on quality of life among patients with atrial fibrillation: the CABANA randomized clinical trial","volume":"321","author":"D B Mark","year":"2019","journal-title":"JAMA"},{"issue":"05","key":"ref7","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1056\/NEJMoa1707855","article-title":"Catheter ablation for atrial fibrillation with heart failure","volume":"378","author":"N F Marrouche","year":"2018","journal-title":"N Engl J Med"},{"issue":"05","key":"ref8","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/eurheartj\/ehaa612","article-title":"2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC","volume":"42","author":"G Hindricks","year":"2021","journal-title":"Eur Heart J"},{"issue":"04","key":"ref9","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1007\/s10237-023-01731-4","article-title":"Computational modeling of hemodynamics and risk of thrombosis in the left atrial appendage using patient-specific blood viscosity and boundary conditions at the mitral valve","volume":"22","author":"Z Zhang","year":"2023","journal-title":"Biomech Model Mechanobiol"},{"issue":"03","key":"ref10","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.hrthm.2019.10.015","article-title":"Prospective comparison of periprocedural coagulation markers among uninterrupted anticoagulants for atrial fibrillation ablation","volume":"17","author":"T Nagao","year":"2020","journal-title":"Heart Rhythm"},{"issue":"18","key":"ref11","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1016\/j.jacc.2007.11.083","article-title":"Acute onset human atrial fibrillation is associated with local cardiac platelet activation and endothelial dysfunction","volume":"51","author":"J G Akar","year":"2008","journal-title":"J Am Coll Cardiol"},{"issue":"06","key":"ref12","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1161\/CIRCULATIONAHA.118.038312","article-title":"Performance of the ABC scores for assessing the risk of stroke or systemic embolism and bleeding in patients with atrial fibrillation in ENGAGE AF-TIMI 48","volume":"139","author":"D D Berg","year":"2019","journal-title":"Circulation"},{"issue":"17","key":"ref13","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1093\/eurheartj\/ehab141","article-title":"Serial assessment of biomarkers and the risk of stroke or systemic embolism and bleeding in patients with atrial fibrillation in the ENGAGE AF-TIMI 48 trial","volume":"42","author":"K Oyama","year":"2021","journal-title":"Eur Heart J"},{"issue":"06","key":"ref14","doi-asserted-by":"crossref","first-page":"e004851","DOI":"10.1161\/JAHA.116.004851","article-title":"Repeated measurements of cardiac biomarkers in atrial fibrillation and validation of the ABC stroke score over time","volume":"6","author":"Z Hijazi","year":"2017","journal-title":"J Am Heart Assoc"},{"issue":"03","key":"ref15","doi-asserted-by":"crossref","first-page":"e24000","DOI":"10.1097\/MD.0000000000024000","article-title":"CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores in stroke with atrial fibrillation: a nationwide multicenter registry study","volume":"100","author":"I Yu","year":"2021","journal-title":"Medicine (Baltimore)"},{"issue":"05","key":"ref16","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1378\/chest.10-0134","article-title":"A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey","volume":"138","author":"R Pisters","year":"2010","journal-title":"Chest"},{"issue":"01","key":"ref17","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13040-023-00330-4","article-title":"A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare","volume":"16","author":"T Kosolwattana","year":"2023","journal-title":"BioData Min"},{"issue":"07","key":"ref18","doi-asserted-by":"crossref","first-page":"3068","DOI":"10.1109\/JBHI.2022.3151084","article-title":"Pathogeny detection for mild cognitive impairment via weighted evolutionary random forest with brain imaging and genetic data","volume":"26","author":"X-A Bi","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"issue":"05","key":"ref19","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1109\/JBHI.2019.2931997","article-title":"Inferring gene regulatory networks of metabolic enzymes using gradient boosted trees","volume":"24","author":"Y Zhang","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"A Natekin","year":"2013","journal-title":"Front Neurorobot"},{"issue":"09","key":"ref21","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1109\/JBHI.2021.3066347","article-title":"Machine learning for real-time heart disease prediction","volume":"25","author":"D Bertsimas","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"issue":"18","key":"ref22","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","article-title":"Logistic regression","volume":"117","author":"M P LaValley","year":"2008","journal-title":"Circulation"},{"issue":"01","key":"ref23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41377-022-00714-x","article-title":"Deep learning in optical metrology: a review","volume":"11","author":"C Zuo","year":"2022","journal-title":"Light Sci Appl"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jbiotec.2017.07.028","article-title":"KNIME for reproducible cross-domain analysis of life science data","volume":"261","author":"A Fillbrunn","year":"2017","journal-title":"J Biotechnol"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"107555","DOI":"10.1016\/j.compbiomed.2023.107555","article-title":"The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review","volume":"166","author":"S Ali","year":"2023","journal-title":"Comput Biol Med"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"106126","DOI":"10.1016\/j.compbiomed.2022.106126","article-title":"Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation","volume":"150","author":"J Lu","year":"2022","journal-title":"Comput Biol Med"},{"issue":"09","key":"ref27","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.jacc.2012.06.019","article-title":"Performance of the HEMORR(2)HAGES, ATRIA, and HAS-BLED bleeding risk-prediction scores in patients with atrial fibrillation undergoing anticoagulation: the AMADEUS (evaluating the use of SR34006 compared to warfarin or acenocoumarol in patients with atrial fibrillation) study","volume":"60","author":"S Apostolakis","year":"2012","journal-title":"J Am Coll Cardiol"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.thromres.2023.01.001","article-title":"Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation","volume":"223","author":"Y Zhao","year":"2023","journal-title":"Thromb Res"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"105209","DOI":"10.1016\/j.ijmedinf.2023.105209","article-title":"Cardiometabolic risk estimation using exposome data and machine learning","volume":"179","author":"A Atehort\u00faa","year":"2023","journal-title":"Int J Med Inform"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","article-title":"Tabular data: deep learning is not all you need","volume":"81","author":"R Shwartz-Ziv","year":"2022","journal-title":"Inf Fusion"},{"issue":"38","key":"ref31","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1093\/eurheartj\/ehab588","article-title":"Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome","volume":"42","author":"E Prifti","year":"2021","journal-title":"Eur Heart J"},{"issue":"05","key":"ref32","doi-asserted-by":"crossref","first-page":"824","DOI":"10.2174\/0929867324666170718124742","article-title":"Biomarkers associated with bleeding risk in the setting of atrial fibrillation","volume":"26","author":"S Sideris","year":"2019","journal-title":"Curr Med Chem"},{"issue":"02","key":"ref33","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/S0735-1097(99)00545-8","article-title":"Blood glucose and platelet-dependent thrombosis in patients with coronary artery disease","volume":"35","author":"M Shechter","year":"2000","journal-title":"J Am Coll Cardiol"},{"issue":"02","key":"ref34","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.amjmed.2016.08.017","article-title":"Impact of body mass index and genetics on warfarin major bleeding outcomes in a community setting","volume":"130","author":"R Hart","year":"2017","journal-title":"Am J Med"}],"container-title":["Thrombosis and Haemostasis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2385-1452.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:31:37Z","timestamp":1745965897000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2385-1452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":34,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2025,4,29]]},"published-print":{"date-parts":[[2025,5]]}},"URL":"https:\/\/doi.org\/10.1055\/a-2385-1452","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0340-6245","2567-689X"],"issn-type":[{"value":"0340-6245","type":"print"},{"value":"2567-689X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,8,13]]},"published":{"date-parts":[[2024,8,13]]}}}