{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:29:57Z","timestamp":1782487797925,"version":"3.54.5"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective:<\/jats:title><jats:p>This study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), na\u00efve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2022.831390","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T05:04:35Z","timestamp":1651554275000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery"],"prefix":"10.3389","volume":"9","author":[{"given":"Yue","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejia","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Xi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhinong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e19","DOI":"10.1056\/NEJMicm064659","article-title":"Images in clinical medicine. Peripheral artery disease","volume":"357","author":"Kang","year":"2007","journal-title":"N Engl J Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.medin.2018.07.016","article-title":"A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis","volume":"44","author":"Garc\u00eda-Gallo","year":"2020","journal-title":"Med Intensiva."},{"key":"B3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/S1010-7940(99)00134-7","article-title":"European system for cardiac operative risk evaluation (EuroSCORE)","volume":"16","author":"Nashef","year":"1999","journal-title":"Eur J Cardiothorac Surg."},{"key":"B4","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1093\/ejcts\/ezs043","article-title":"EuroSCORE II","volume":"41","author":"Nashef","year":"2012","journal-title":"Eur J Cardiothorac Surg"},{"key":"B5","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1016\/j.athoracsur.2009.05.053","article-title":"The society of thoracic surgeons 2008 cardiac surgery risk models: part 1\u2013coronary artery bypass grafting surgery","volume":"88","author":"Shahian","year":"2009","journal-title":"Ann Thorac Surg."},{"key":"B6","doi-asserted-by":"publisher","first-page":"S23","DOI":"10.1016\/j.athoracsur.2009.05.056","article-title":"The society of thoracic surgeons 2008 cardiac surgery risk models: part 2\u2013isolated valve surgery","volume":"88","author":"O'Brien","year":"2009","journal-title":"Ann Thorac Surg."},{"key":"B7","doi-asserted-by":"publisher","first-page":"S43","DOI":"10.1016\/j.athoracsur.2009.05.055","article-title":"The society of thoracic surgeons 2008 cardiac surgery risk models: part 3\u2013valve plus coronary artery bypass grafting surgery","volume":"88","author":"Shahian","year":"2009","journal-title":"Ann Thorac Surg."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1007\/s11605-019-04338-2","article-title":"Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery","volume":"24","author":"Merath","year":"2020","journal-title":"J Gastrointest Surg."},{"key":"B9","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1097\/SLA.0000000000002956","article-title":"Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER)","volume":"268","author":"Bertsimas","year":"2018","journal-title":"Calculator Ann Surg."},{"key":"B10","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1093\/icvts\/ivt022","article-title":"Comparison of original EuroSCORE, EuroSCORE II and STS risk models in a Turkish cardiac surgical cohort","volume":"16","author":"Kunt","year":"2013","journal-title":"Interact Cardiovasc Thorac Surg."},{"key":"B11","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s00392-009-0010-8","article-title":"EuroSCORE overestimates the risk of cardiac surgery: results from the national registry of the German Society of Thoracic and Cardiovascular Surgery","volume":"98","author":"Gummert","year":"2009","journal-title":"Clin Res Cardiol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1093\/ejcts\/ezw072","article-title":"Comparison of logistic EuroSCORE and EuroSCORE II in predicting operative mortality of 1125 total arterial operations","volume":"50","author":"Kieser","year":"2016","journal-title":"Eur J Cardiothorac Surg."},{"key":"B13","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1097\/EJA.0b013e32833a45de","article-title":"Poor performances of EuroSCORE and CARE score for prediction of perioperative mortality in octogenarians undergoing aortic valve replacement for aortic stenosis","volume":"27","author":"Chhor","year":"2010","journal-title":"Eur J Anaesthesiol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"e0187056","DOI":"10.1371\/journal.pone.0187056","article-title":"Is the EuroSCORE II reliable to estimate operative mortality among octogenarians?","volume":"12","author":"Provench\u00e8re","year":"2017","journal-title":"PLoS ONE."},{"key":"B15","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1016\/j.jtcvs.2014.07.039","article-title":"Performance of the European System for Cardiac Operative Risk Evaluation II: a meta-analysis of 22 studies involving 145,592 cardiac surgery procedures","volume":"148","author":"Guida","year":"2014","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation."},{"key":"B17","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1308\/147870804290","article-title":"Artificial intelligence in medicine","volume":"86","author":"Ramesh","year":"2004","journal-title":"Ann R Coll Surg Engl."},{"key":"B19","doi-asserted-by":"crossref","DOI":"10.1007\/s10877-021-00703-2","article-title":"Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction","volume-title":"J Clin Monit Comput","author":"Nistal-Nu\u00f1o","year":"2021"},{"key":"B20","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1053\/j.jvca.2020.07.029","article-title":"Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery","volume":"35","author":"Fernandes","year":"2021","journal-title":"J Cardiothorac Vasc Anesth."},{"key":"B21","doi-asserted-by":"publisher","first-page":"e0169772","DOI":"10.1371\/journal.pone.0169772","article-title":"A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis","volume":"12","author":"Allyn","year":"2017","journal-title":"PLoS ONE."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.21037\/atm-20-2614","article-title":"Admission oxygen saturation and all-cause in-hospital mortality in acute myocardial infarction patients: data from the MIMIC-III database","volume":"8","author":"Yu","year":"2020","journal-title":"Ann Transl Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"445","DOI":"10.3389\/fmed.2020.00445","article-title":"A machine learning-based prediction of hospital mortality in patients with postoperative sepsis","volume":"7","author":"Yao","year":"2020","journal-title":"Front Med."},{"key":"B24","doi-asserted-by":"publisher","first-page":"6612276","DOI":"10.1155\/2021\/6612276","article-title":"Admission serum ionized and total calcium as new predictors of mortality in patients with cardiogenic shock","volume":"2021","author":"Yu","year":"2021","journal-title":"Biomed Res Int."},{"key":"B25","doi-asserted-by":"publisher","first-page":"7458451","DOI":"10.1155\/2020\/7458451","article-title":"The neutrophil percentage-to-albumin ratio as a new predictor of all-cause mortality in patients with cardiogenic shock","volume":"2020","author":"Yu","year":"2020","journal-title":"Biomed Res Int."},{"key":"B26","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data."},{"key":"B27","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ."},{"key":"B28","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/sim.4067","article-title":"Multiple imputation using chained equations: issues and guidance for practice","volume":"30","author":"White","year":"2011","journal-title":"Stat Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s12916-018-1122-7","article-title":"From hype to reality: data science enabling personalized medicine","volume":"16","author":"Fr\u00f6hlich","year":"2018","journal-title":"BMC Med."},{"key":"B30","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1097\/ACO.0000000000000280","article-title":"The response of the microcirculation to cardiac surgery","volume":"29","author":"Kara","year":"2016","journal-title":"Curr Opin Anaesthesiol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1093\/eurheartj\/ehu278","article-title":"2014 ESC\/EACTS Guidelines on myocardial revascularization: The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI)","volume":"35","author":"Windecker","year":"2014","journal-title":"Eur Heart J."},{"key":"B32","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1053\/j.jvca.2020.04.052","article-title":"Predicting 1-year mortality after cardiac surgery complicated by prolonged critical illness: derivation and validation of a population-based risk model","volume":"34","author":"McDonald","year":"2020","journal-title":"J Cardiothorac Vasc Anesth."},{"key":"B33","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/S0140-6736(13)60108-7","article-title":"Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II","volume":"381","author":"Farooq","year":"2013","journal-title":"Lancet."},{"key":"B34","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ijcard.2015.10.086","article-title":"National administrative data produces an accurate and stable risk prediction model for short-term and 1-year mortality following cardiac surgery","volume":"203","author":"Aktuerk","year":"2016","journal-title":"Int J Cardiol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"e002121","DOI":"10.1161\/CIRCINTERVENTIONS.114.002121","article-title":"Prediction of cardiac and noncardiac mortality after percutaneous coronary intervention","volume":"8","author":"Spoon","year":"2015","journal-title":"Circ Cardiovasc Interv."},{"key":"B36","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.1111\/jocs.14836","article-title":"The Asian system for cardiac operative risk evaluation for predicting mortality after isolated coronary artery bypass graft surgery (ASCORE-C)","volume":"35","author":"Luo","year":"2020","journal-title":"J Card Surg."},{"key":"B37","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1161\/CIRCULATIONAHA.111.055939","article-title":"Risk score for predicting long-term mortality after coronary artery bypass graft surgery","volume":"125","author":"Wu","year":"2012","journal-title":"Circulation."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtcvs.2020.07.105","article-title":"Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis","volume":"160","author":"Benedetto","year":"2020","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B39","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ijcard.2021.07.024","article-title":"Prediction of 1-year mortality after heart transplantation using machine learning approaches: a single-center study from China","volume":"339","author":"Zhou","year":"2021","journal-title":"Int J Cardiol."},{"key":"B40","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtcvs.2021.09.010","article-title":"Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores","volume-title":"J Thorac Cardiovasc Surg","author":"Ong","year":"2021"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1016\/j.athoracsur.2019.09.049","article-title":"Predictive utility of a machine learning algorithm in estimating mortality risk in cardiac surgery","volume":"109","author":"Kilic","year":"2020","journal-title":"Ann Thorac Surg."},{"key":"B42","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/jocs.16072","article-title":"Machine learning models for mitral valve replacement: a comparative analysis with the society of thoracic surgeons risk score","volume":"37","author":"Orfanoudaki","year":"2021","journal-title":"J Card Surg"},{"key":"B43","doi-asserted-by":"publisher","first-page":"e007363","DOI":"10.1161\/CIRCOUTCOMES.120.007363","article-title":"Toward dynamic risk prediction of outcomes after coronary artery bypass graft: improving risk prediction with intraoperative events using gradient boosting","volume":"14","author":"Mori","year":"2021","journal-title":"Circ Cardiovasc Qual Outcomes."},{"key":"B44","doi-asserted-by":"publisher","first-page":"e0199277","DOI":"10.1371\/journal.pone.0199277","article-title":"Predictive performance of six mortality risk scores and the development of a novel model in a prospective cohort of patients undergoing valve surgery secondary to rheumatic fever","volume":"13","author":"Mejia","year":"2018","journal-title":"PLoS ONE."},{"key":"B45","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1016\/j.jtcvs.2014.02.028","article-title":"Two new mathematical models for prediction of early mortality risk in coronary artery bypass graft surgery","volume":"148","author":"Ghavidel","year":"2014","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B46","doi-asserted-by":"publisher","first-page":"81","DOI":"10.2174\/1874192400903010081","article-title":"Artificial neural networks versus multiple logistic regression to predict 30-day mortality after operations for type a ascending aortic dissection","volume":"3","author":"Macrina","year":"2009","journal-title":"Open Cardiovasc Med J."},{"key":"B47","doi-asserted-by":"publisher","first-page":"756","DOI":"10.5114\/aoms.2015.48145","article-title":"Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models","volume":"11","author":"Mendes","year":"2015","journal-title":"Arch Med Sci."},{"key":"B48","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1162\/NECO_a_00793","article-title":"An empirical overview of the no free lunch theorem and its effect on real-world machine learning classification","volume":"28","author":"G\u00f3mez","year":"2016","journal-title":"Neural Comput."},{"key":"B49","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/ac2a8f","article-title":"Cardiovascular risk and mortality prediction in patients suspected of sleep apnea: a model based on an artificial intelligence system","volume-title":"Physiol Meas","author":"Blanchard","year":"2021"},{"key":"B50","doi-asserted-by":"publisher","first-page":"5525118","DOI":"10.1155\/2021\/5525118","article-title":"Prediction of multiple organ failure complicated by moderately severe or severe acute pancreatitis based on machine learning: a multicenter cohort study","volume":"2021","author":"Xu","year":"2021","journal-title":"Mediators Inflamm."},{"key":"B51","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13058-019-1158-4","article-title":"Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models","volume":"21","author":"Ming","year":"2019","journal-title":"Breast Cancer Res."},{"key":"B52","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1093\/eurjhf\/hfp179","article-title":"Red blood cell distribution width and 1-year mortality in acute heart failure","volume":"12","author":"van Kimmenade","year":"2010","journal-title":"Eur J Heart Fail."},{"key":"B53","doi-asserted-by":"publisher","first-page":"288","DOI":"10.21470\/1678-9741-2017-0023","article-title":"Relationship between high red cell distribution width and systemic inflammatory response syndrome after extracorporeal circulation","volume":"32","author":"Seth","year":"2017","journal-title":"Braz J Cardiovasc Surg."},{"key":"B54","doi-asserted-by":"publisher","first-page":"824624","DOI":"10.1155\/2015\/824624","article-title":"The prognostic role of red blood cell distribution width in coronary artery disease: a review of the pathophysiology","volume":"2015","author":"Bujak","year":"2015","journal-title":"Dis Markers."},{"key":"B55","doi-asserted-by":"publisher","first-page":"145","DOI":"10.2459\/JCM.0000000000000747","article-title":"Red blood distribution width and heart transplantation: any predictive role on patient outcome?","volume":"20","author":"Lechiancole","year":"2019","journal-title":"J Cardiovasc Med."},{"key":"B56","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.ijcard.2012.08.029","article-title":"Red blood cell distribution width predicts mortality after coronary artery bypass grafting","volume":"165","author":"Benedetto","year":"2013","journal-title":"Int J Cardiol."},{"key":"B57","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1001\/jama.1993.03510240069035","article-title":"A new Simplified Acute Physiology Score (SAPS II) based on a European\/North American multicenter study","volume":"270","author":"Le Gall","year":"1993","journal-title":"JAMA."},{"key":"B58","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s12871-020-00975-2","article-title":"Mortality prediction by SOFA score in ICU-patients after cardiac surgery; comparison with traditional prognostic-models","volume":"20","author":"Schoe","year":"2020","journal-title":"BMC Anesthesiol."},{"key":"B59","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1097\/01.ASN.0000130340.93930.DD","article-title":"Minimal changes of serum creatinine predict prognosis in patients after cardiothoracic surgery: a prospective cohort study","volume":"15","author":"Lassnigg","year":"2004","journal-title":"J Am Soc Nephrol."},{"key":"B60","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","article-title":"Prediction of the development of acute kidney injury following cardiac surgery by machine learning","volume":"24","author":"Tseng","year":"2020","journal-title":"Crit Care."}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.831390\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T05:04:38Z","timestamp":1651554278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.831390\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":60,"alternative-id":["10.3389\/fcvm.2022.831390"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2022.831390","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]},"article-number":"831390"}}