Estimation of hemoglobin concentration at the initiation of cardiopulmonary bypass using support vector regression.

Harutoyo Hirano, Shun Takahashi, Tetsuya Kamei, Makoto Hibiya

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Article in The journal of extra-corporeal technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Harutoyo Hirano *Department of Medical Equipment Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan.ORCID 0000-0002-0634-1805

Shun Takahashi *Department of Biomedical Engineering, Graduate School of Medical Science, Fujita Health University, Toyoake, Aichi, Japan.

Tetsuya KameiFundamental Education Department, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan.

Makoto HibiyaDepartment of Clinical Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan.

Funding

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6 · The paper itself

Abstract

backgroundHemodilution 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

methodsWe retrospectively analyzed 577 adult patients who underwent elective CPB at Fujita Health University Hospital from January 2016 to December 2020. Thirty-six preoperative variables - including demographics, laboratory data, circuit parameters, and indices such as TBV and ideal weight - 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 (R

resultsOf 993 screened cases, 577 met inclusion criteria (447 males, mean age 66.8 ± 11.7 years; 130 females, 69.5 ± 10.6 years). SVR on standardized predictors achieved the highest accuracy (R

conclusionThese findings suggest that an SVR-based model improves prediction of Hb

Indexed as

Cardiopulmonary BypassHemoglobinsSupport Vector MachineAgedFemaleHemodilutionHumansMaleMiddle AgedRetrospective StudiesHemoglobinsCardiopulmonary bypassHemodilutionHemoglobinMachine learningSupport vector regression

Identifiers

PMID42319101

DOI10.1051/ject/2025071

PMCPMC13281317

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