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A cardiovascular model for renal perfusion during cardiopulmonary bypass surgery

Lee, C-JORCID: 0000-0002-9360-0923, Gardiner, B.S. and Smith, D.W. (2020) A cardiovascular model for renal perfusion during cardiopulmonary bypass surgery. Computers in Biology and Medicine, 119 . Article 103676.

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Acute kidney injury (AKI) is a major complication following cardiac surgery requiring cardiopulmonary bypass (CPB). It is likely that poor renal perfusion contributes to the occurrence of AKI, via renal hypoxia, so it is imperative to maintain optimal renal perfusion during CPB. We have developed a straightforward cardiovascular perfusion model with parameter values calibrated against experimental and/or clinical data from several independent studies of CPB in humans and animals. Following model development and calibration, we performed a one-at-a-time parametric study to investigate the response of renal perfusion to several variables during CPB, namely pump flow (denoted CO for ‘cardiac output’), renal vascular resistance, and non-renal vascular resistance. From the parametric study, we have found that all three parameters had a similarly strong influence on renal perfusion. We simulated three potential strategies for maintaining optimum renal perfusion during CPB and tested their effectiveness. The strategies were: (1) increasing the pump flow; (2) administrating noradrenaline (vasopressor); and (3) administrating fenoldopam (renal vasodilator). Simulations have revealed that administration of fenoldopam is likely to be the most effective of the three strategies. Other findings from our simulations are that increasing pump flow is less effective when central venous pressure is elevated. Further, renal autoregulation is likely inoperative during CPB, as evidenced by an unchanging renal vascular resistance with increasing CO and blood pressure. The cardiac-renal perfusion model developed in this study can be linked with other kidney models to simulate the changes in renal oxygenation during CPB.

Item Type: Journal Article
Murdoch Affiliation(s): College of Science, Health, Engineering and Education
Publisher: Elsevier
Copyright: © 2020 Elsevier Ltd.
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