Effect of perfusion flow rate on tissue oxygenation in newborn piglets during cardiopulmonary bypass

2003 ◽  
Vol 75 (2) ◽  
pp. 560-565 ◽  
Author(s):  
Gregory Schears ◽  
Steven E Schultz ◽  
Jennifer Creed ◽  
William J Greeley ◽  
David F Wilson ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Tissue Oxygenation ◽  
Newborn Piglets ◽  
Perfusion Flow ◽  
Perfusion Flow Rate

scholarly journals Optimal perfusion flow rate for the brain during deep hypothermic cardiopulmonary bypass at 20° C

1986 ◽  
Vol 92 (6) ◽  
pp. 1065-1070 ◽  
Author(s):  
Katsuhiko Miyamoto ◽  
Yasunaru Kawashima ◽  
Hikaru Matsuda ◽  
Akihiro Okuda ◽  
Serei Maeda ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Hypothermic Cardiopulmonary Bypass ◽  
Perfusion Flow ◽  
The Brain ◽  
Perfusion Flow Rate

scholarly journals Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoshi Miyamoto ◽  
Zu Soh ◽  
Shigeyuki Okahara ◽  
Akira Furui ◽  
Taiichi Takasaki ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Neurological Complications ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Perfusion Flow ◽  
Postsurgical Complication ◽  
Tenfold Cross Validation ◽  
Suction Flow ◽  
The Relationship

AbstractThe need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

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