{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T13:08:11Z","timestamp":1784725691187,"version":"3.55.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T00:00:00Z","timestamp":1781913600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Brazilian Journal of Anesthesiology (English Edition)"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bjane.2026.844782","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:17:50Z","timestamp":1782404270000},"page":"844782","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in the prediction of intraoperative red blood cell transfusion in cardiac surgery: a systematic review and diagnostic test accuracy meta-analysis"],"prefix":"10.1016","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9698-6138","authenticated-orcid":false,"given":"Filipe Giordano","family":"Val\u00e9rio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2643-6074","authenticated-orcid":false,"given":"Amanda Carneiro","family":"Rodrigues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8426-4821","authenticated-orcid":false,"given":"Davi Ricardo Soares Gama","family":"de Amorim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0763-338X","authenticated-orcid":false,"given":"Roberta Esterque","family":"Cantarino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5629-4888","authenticated-orcid":false,"given":"Glauco Martins","family":"de Araujo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3051-335X","authenticated-orcid":false,"given":"Jimmy","family":"Yusuf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8430-4395","authenticated-orcid":false,"given":"Lecticia Vianna Leal Soares","family":"Bessa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0557-0866","authenticated-orcid":false,"given":"Millena Mendon\u00e7a Andrade Paes","family":"Leme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4469-6515","authenticated-orcid":false,"given":"Marcella Freire de Campos","family":"Euzebio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7160-0818","authenticated-orcid":false,"given":"Fl\u00e1vio Luiz","family":"Seixas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-6079","authenticated-orcid":false,"given":"Alexandra Rezende","family":"Assad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4413-5770","authenticated-orcid":false,"given":"Luis Antonio dos Santos","family":"Diego","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bjane.2026.844782_bib0001","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2024.1344170","article-title":"Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort","volume":"11","author":"Zhou","year":"2024","journal-title":"Front Cardiovasc Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0002","doi-asserted-by":"crossref","DOI":"10.21470\/1678-9741-2023-0212","article-title":"Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population","author":"Cunha","year":"2024","journal-title":"Braz J Cardiovasc Surg"},{"key":"10.1016\/j.bjane.2026.844782_bib0003","doi-asserted-by":"crossref","first-page":"709","DOI":"10.7754\/Clin.Lab.2023.230930","article-title":"Machine Learning-Based Prediction of Intraoperative Red Blood Cell Transfusion in Aortic Valve Replacement Surgery","volume":"70","author":"Chen","year":"2024","journal-title":"Clin Lab"},{"key":"10.1016\/j.bjane.2026.844782_bib0004","article-title":"Machine learning models to predict red blood cell transfusion in patients undergoing mitral valve surgery","volume":"9","author":"Liu","year":"2021","journal-title":"Ann Transl Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0005","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1111\/j.1365-2044.2011.06819.x","article-title":"The prevalence and association with transfusion, intensive care unit stay and mortality of pre-operative anaemia in a cohort of cardiac surgery patients*","volume":"66","author":"Hung","year":"2011","journal-title":"Anaesthesia"},{"key":"10.1016\/j.bjane.2026.844782_bib0006","first-page":"98","article-title":"Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery","volume":"150","author":"Sun","year":"2025","journal-title":"Cardiology"},{"key":"10.1016\/j.bjane.2026.844782_bib0007","doi-asserted-by":"crossref","DOI":"10.21470\/1678-9741-2024-0205","article-title":"Patient Blood Management Program Implementation: Comprehensive Recommendations and Practical Strategies","volume":"39","author":"C\u00e9spedes","year":"2024","journal-title":"Braz J Cardiovasc Surg"},{"key":"10.1016\/j.bjane.2026.844782_bib0008","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1213\/ANE.0000000000005873","article-title":"A Global Definition of Patient Blood Management","volume":"135","author":"Shander","year":"2022","journal-title":"Anesth Analg"},{"key":"10.1016\/j.bjane.2026.844782_bib0009","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1053\/j.jvca.2017.06.026","article-title":"2017 EACTS\/EACTA Guidelines on patient blood management for adult cardiac surgery","volume":"32","author":"Boer","year":"2018","journal-title":"J Cardiothorac Vasc Anesth"},{"key":"10.1016\/j.bjane.2026.844782_bib0010","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s43057-025-00162-4","article-title":"Assessing operative mortality risk in cardiothoracic surgery: analysis of STS scores \u2013 a retrospective study","volume":"33","author":"Hassan","year":"2025","journal-title":"Cardiothorac Surg"},{"issue":"4","key":"10.1016\/j.bjane.2026.844782_bib0011","doi-asserted-by":"crossref","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":"10.1016\/j.bjane.2026.844782_bib0012","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1111\/j.1537-2995.2006.00860.x","article-title":"Development and validation of Transfusion Risk Understanding Scoring Tool (TRUST) to stratify cardiac surgery patients according to their blood transfusion needs","volume":"46","author":"Alghamdi","year":"2006","journal-title":"Transfusion (Paris)."},{"key":"10.1016\/j.bjane.2026.844782_bib0013","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2022.893452","article-title":"Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study","volume":"16","author":"Chen","year":"2022","journal-title":"Front Neuroinformatics"},{"key":"10.1016\/j.bjane.2026.844782_bib0014","series-title":"Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy","author":"Deeks","year":"2023"},{"key":"10.1016\/j.bjane.2026.844782_bib0015","series-title":"Cochrane Handbook for Systematic Reviews of Interventions","author":"Higgins","year":"2019"},{"key":"10.1016\/j.bjane.2026.844782_bib0016","doi-asserted-by":"crossref","first-page":"m2632","DOI":"10.1136\/bmj.m2632","article-title":"Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.bjane.2026.844782_bib0017","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan-a web and mobile app for systematic reviews","volume":"5","author":"Ouzzani","year":"2016","journal-title":"Syst Rev"},{"key":"10.1016\/j.bjane.2026.844782_bib0018","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0019","series-title":"Cochrane handbook for systematic reviews of interventions","first-page":"375","article-title":"GRADE handbook for grading quality of evidence and strength of recommendations","author":"Sch\u00fcnemann","year":"2019"},{"key":"10.1016\/j.bjane.2026.844782_bib0020","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1177\/0962280216669183","article-title":"Optimally estimating the sample mean from the sample size, median, mid-range, and\/or mid-quartile range","volume":"27","author":"Luo","year":"2018","journal-title":"Stat Methods Med Res"},{"key":"10.1016\/j.bjane.2026.844782_bib0021","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/1471-2288-14-135","article-title":"Estimating the sample mean and standard deviation from the sample size, median, range and\/or interquartile range","volume":"14","author":"Wan","year":"2014","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.bjane.2026.844782_bib0022","series-title":"RStudio: Integrated Development Environment for R. Posit Software","year":"2025"},{"key":"10.1016\/j.bjane.2026.844782_bib0024","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1136\/ebmental-2019-300117","article-title":"How to perform a meta-analysis with R: a practical tutorial","volume":"22","author":"Balduzzi","year":"2019","journal-title":"Evid Based Ment Health"},{"key":"10.1016\/j.bjane.2026.844782_bib0025","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.bjane.2026.844782_bib0026","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1002\/sim.942","article-title":"A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations","volume":"20","author":"Rutter","year":"2001","journal-title":"Stat Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0027","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1093\/biostatistics\/kxl004","article-title":"A unification of models for meta-analysis of diagnostic accuracy studies","volume":"8","author":"Harbord","year":"2007","journal-title":"Biostatistics"},{"key":"10.1016\/j.bjane.2026.844782_bib0028","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1002\/sim.6115","article-title":"Statistics for quantifying heterogeneity in univariate and bivariate meta-analyses of binary data: the case of meta-analyses of diagnostic accuracy","volume":"33","author":"Zhou","year":"2014","journal-title":"Stat Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0029","doi-asserted-by":"crossref","DOI":"10.1111\/cts.70056","article-title":"Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development","volume":"17","author":"Ponce-Bobadilla","year":"2024","journal-title":"Clin Transl Sci"},{"key":"10.1016\/j.bjane.2026.844782_bib0030","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1002\/ansa.202200018","article-title":"Cross-validated permutation feature importance considering correlation between features","volume":"3","author":"Kaneko","year":"2022","journal-title":"Anal Sci Adv"},{"key":"10.1016\/j.bjane.2026.844782_bib0031","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.cvfa.2005.12.006","article-title":"Using and interpreting diagnostic tests","volume":"22","author":"McKenna","year":"2006","journal-title":"Vet Clin North Am Food Anim Pract"},{"key":"10.1016\/j.bjane.2026.844782_bib0032","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/17474086.2018.1502081","article-title":"Impact and management of iron deficiency and iron deficiency anemia in women\u2019s health","volume":"11","author":"Mirza","year":"2018","journal-title":"Expert Rev Hematol"},{"key":"10.1016\/j.bjane.2026.844782_bib0033","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1053\/j.jvca.2019.05.040","article-title":"Validation Study of the Transfusion Risk and Clinical Knowledge (TRACK) Tool in Cardiac Surgery Patients: A Retrospective Analysis","volume":"33","author":"Leff","year":"2019","journal-title":"J Cardiothorac Vasc Anesth"},{"key":"10.1016\/j.bjane.2026.844782_bib0034","doi-asserted-by":"crossref","first-page":"249.e1","DOI":"10.1016\/j.jtcvs.2011.04.007","article-title":"Patient blood management during cardiac surgery: Do we have enough evidence for clinical practice?","volume":"142","author":"Ranucci","year":"2011","journal-title":"J Thorac Cardiovasc Surg"},{"key":"10.1016\/j.bjane.2026.844782_bib0035","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1002\/bjs.9898","article-title":"Patient blood management to reduce surgical risk","volume":"102","author":"Clevenger","year":"2015","journal-title":"Br J Surg"},{"key":"10.1016\/j.bjane.2026.844782_bib0036","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/tme.12476","article-title":"Cornerstones of patient blood management in surgery","volume":"28","author":"Butcher","year":"2018","journal-title":"Transfus Med"},{"key":"10.1016\/j.bjane.2026.844782_bib0037","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12916-014-0241-z","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement","volume":"13","author":"Collins","year":"2015","journal-title":"BMC Med"}],"container-title":["Brazilian Journal of Anesthesiology (English Edition)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S010400142600062X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S010400142600062X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:59:24Z","timestamp":1784725164000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S010400142600062X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S010400142600062X"],"URL":"https:\/\/doi.org\/10.1016\/j.bjane.2026.844782","relation":{},"ISSN":["0104-0014"],"issn-type":[{"value":"0104-0014","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in the prediction of intraoperative red blood cell transfusion in cardiac surgery: a systematic review and diagnostic test accuracy meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Brazilian Journal of Anesthesiology (English Edition)","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bjane.2026.844782","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Sociedade Brasileira de Anestesiologia. Published by Elsevier Espa\u00f1a, S.L.U.","name":"copyright","label":"Copyright"}],"article-number":"844782"}}