Original Article

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

¹ Department of Medical Equipment Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
² Department of Biomedical Engineering, Graduate School of Medical Science, Fujita Health University, Toyoake, Aichi, Japan
³ Fundamental Education Department, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
⁴ Department of Clinical Engineering, Clinical and Educational Collaboration Unit, Faculty of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 5 August 2025
Accepted: 4 December 2025

Abstract

Background: 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_CPB) by incorporating circulating blood volume, laboratory data, physical measurements, and patient history. Methods: 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 – 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 (R²), mean squared error (MSE), and Bland–Altman analysis (bias and 95% limits of agreement [LoA]). Predictions from two conventional TBV-based formulas were used as benchmarks. Results: Of 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 (R² = 0.498, MSE = 0.517), outperforming GLM (R² = 0.429, MSE = 0.797) and MLP (R² = 0.332, MSE = 0.669). Conventional formulas showed lower performance (R² = 0.325, MSE ≥ 1.48). Bland–Altman analysis for SVR demonstrated minimal bias (–0.0028 g/dL) and narrower LoA (–1.42 to 1.41 g/dL) than conventional methods (bias –1.33 g/dL; LoA –3.49 to 0.83 g/dL). Conclusion: These findings suggest that an SVR-based model improves prediction of Hb_CPB over conventional approaches, supporting optimized transfusion management and reduced hemodilution-related risks.