{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T08:50:37Z","timestamp":1781859037828,"version":"3.54.5"},"reference-count":32,"publisher":"EDP Sciences","issue":"2","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Extra Corpor Technol","JECT"],"accepted":{"date-parts":[[2025,12,4]]},"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:p>\n                    <jats:italic>Background<\/jats:italic>\n                    : Hemodilution during cardiopulmonary bypass (CPB) is a standard perfusion strategy used to reduce blood viscosity and enhance microcirculatory flow. The hemodilution rate, expressed as hemoglobin (Hb) concentration, is a key control index in CPB and is currently estimated from total blood volume (TBV). The objective of this study was to propose a novel formula to accurately predict Hb concentration at the initiation of CPB (Hb\n                    <jats:sub>CPB<\/jats:sub>\n                    ) by incorporating circulating blood volume, laboratory data, physical measurements, and patient history.\n                    <jats:italic>Methods<\/jats:italic>\n                    : We retrospectively analyzed 577 adult patients who underwent elective CPB at Fujita Health University Hospital from January 2016 to December 2020. Thirty-six preoperative variables \u2013 including demographics, laboratory data, circuit parameters, and indices such as TBV and ideal weight \u2013 were standardized. Categorical variables underwent one-hot encoding. We compared generalized linear models (GLM), support vector regression (SVR), and multilayer perceptron (MLP). Model performance was evaluated using the coefficient of determination (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    ), mean squared error (MSE), and Bland\u2013Altman analysis (bias and 95% limits of agreement [LoA]). Predictions from two conventional TBV-based formulas were used as benchmarks.\n                    <jats:italic>Results<\/jats:italic>\n                    : Of 993 screened cases, 577 met inclusion criteria (447 males, mean age 66.8 \u00b1 11.7 years; 130 females, 69.5 \u00b1 10.6 years). SVR on standardized predictors achieved the highest accuracy (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.498, MSE = 0.517), outperforming GLM (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.429, MSE = 0.797) and MLP (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.332, MSE = 0.669). Conventional formulas showed lower performance (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.325, MSE \u2265 1.48). Bland\u2013Altman analysis for SVR demonstrated minimal bias (\u20130.0028 g\/dL) and narrower LoA (\u20131.42 to 1.41 g\/dL) than conventional methods (bias \u20131.33 g\/dL; LoA \u20133.49 to 0.83 g\/dL).\n                    <jats:italic>Conclusion<\/jats:italic>\n                    : These findings suggest that an SVR-based model improves prediction of Hb\n                    <jats:sub>CPB<\/jats:sub>\n                    over conventional approaches, supporting optimized transfusion management and reduced hemodilution-related risks.\n                  <\/jats:p>","DOI":"10.1051\/ject\/2025071","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:46:42Z","timestamp":1766087202000},"page":"128-138","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of hemoglobin concentration at the initiation of cardiopulmonary bypass using support vector regression"],"prefix":"10.1051","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0634-1805","authenticated-orcid":false,"given":"Harutoyo","family":"Hirano","sequence":"first","affiliation":[{"name":"Department of Medical Equipment Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Takahashi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Graduate School of Medical Science, Fujita Health University","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuya","family":"Kamei","sequence":"additional","affiliation":[{"name":"Fundamental Education Department, Faculty of Medical Sciences, Fujita Health University","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makoto","family":"Hibiya","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"250","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4326\/jjcvs.48.1","volume":"48","author":"Hirata","year":"2019","journal-title":"Jpn J Cardiovasc Surg"},{"issue":"1","key":"R2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.4326\/jjcvs.48.11","volume":"48","author":"Abe","year":"2019","journal-title":"Jpn J Cardiovasc Surg"},{"issue":"1","key":"R3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.4326\/jjcvs.48.18","volume":"48","author":"Shimizu","year":"2019","journal-title":"Jpn J Cardiovasc Surg"},{"issue":"1","key":"R4","doi-asserted-by":"crossref","first-page":"6","DOI":"10.4326\/jjcvs.48.6","volume":"48","author":"Saito","year":"2019","journal-title":"Jpn J Cardiovasc Surg"},{"issue":"1 Pt 1","key":"R5","first-page":"103","volume":"7","author":"Taniguchi","year":"2009","journal-title":"Einstein (Sao Paulo)"},{"issue":"1","key":"R6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.athoracsur.2009.07.078","volume":"89","author":"Ranucci","year":"2010","journal-title":"Ann Thorac Surg"},{"issue":"4","key":"R7","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1016\/j.athoracsur.2005.03.137","volume":"80","author":"Karkouti","year":"2005","journal-title":"Ann Thorac Surg"},{"issue":"8","key":"R8","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s12630-010-9329-x","volume":"57","author":"Tsui","year":"2010","journal-title":"Can J Anaesth"},{"issue":"1","key":"R9","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1053\/j.jvca.2017.06.026","volume":"32","author":"Boer","year":"2018","journal-title":"J Cardiothorac Vasc Anesth"},{"issue":"3","key":"R10","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1016\/j.athoracsur.2010.11.078","volume":"91","author":"Ferraris","year":"2011","journal-title":"Ann Thorac Surg"},{"issue":"5","key":"R11","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1213\/ane.0b013e3181875e2e","volume":"108","author":"Murphy","year":"2009","journal-title":"Anesth Analg"},{"key":"R12","unstructured":"Gravlee GP. Cardiopulmonary bypass: principles and practice. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008: 416-417."},{"key":"R13","unstructured":"Hall JE. Guyton and hall textbook of medical physiology. 14th ed. Philadelphia, PA: Elsevier; 2020: 305\u2013310."},{"issue":"6","key":"R14","doi-asserted-by":"crossref","first-page":"579","DOI":"10.3925\/jjtc.70.579","volume":"70","author":"Sonoki","year":"2024","journal-title":"Jpn J Transfusion Cell Therapy"},{"issue":"4","key":"R15","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1177\/0267659114547250","volume":"30","author":"Hilberath","year":"2015","journal-title":"Perfusion"},{"key":"R16","first-page":"1","volume":"25","author":"Hasegawa","year":"2021","journal-title":"J Artif Organs"},{"issue":"1","key":"R17","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1051\/ject\/200840061","volume":"40","author":"Trowbridge","year":"2008","journal-title":"J Extra Corpor Technol"},{"issue":"6","key":"R18","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1093\/icvts\/ivy173","volume":"27","author":"Muraki","year":"2018","journal-title":"Interact Cardiovasc Thorac Surg"},{"issue":"6","key":"R19","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1177\/0267659115623357","volume":"31","author":"Erpicum","year":"2016","journal-title":"Perfusion"},{"issue":"1","key":"R20","first-page":"1","volume":"45","author":"Hibiya","year":"2018","journal-title":"Jpn J Extra Corpor Technol"},{"issue":"6","key":"R21","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1001\/archinte.1916.00080130010002","volume":"17","author":"Du Bois","year":"1916","journal-title":"Arch Int Med"},{"key":"R22","unstructured":"Japan Society for the Study of Obesity. 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