Lean Flow: Optimizing Cardiopulmonary Bypass Equipment and Flow for Obese Patients-A Technique Article

Abstract

The goal of this chart review was to investigate the use of down-sized cardiopulmonary bypass (CPB) circuits for obese patients. The effects of transitioning from larger to smaller oxygenators, reservoirs, and arteriovenous tubing loops were evaluated through a retrospective review of 2,816 adult non-congenital procedure perfusion records. This technique report and case series is a continuation of our original prescriptive CPB circuit quality improvement project. An algorithm was derived to adjust body surface area (BSA) to lower body mass index (BMI) to provide down-sized extracorporeal circuit components capable of meeting the metabolic needs of the patient. As a result of using smaller circuits, decreased priming volumes led to significantly increased hemoglobin (HB) nadirs (p < .05) leading to significant decreases in homologous donor blood product exposures (p < .05). Patients with large BSAs were supported safely with smaller circuits by using lean body mass (LBM)-adjusted BSA and target blood flow algorithm. Based on this case series, large BMI patients may be safely supported with smaller circuits selected based on BSAs adjusted more toward LBM. Use of smaller circuits in high BMI patients led to higher HB nadirs and less donor blood components during the surgical procedure. Renal function and hospital stay were not affected by this approach.

Figure 1.

The decision algorithm used to adjust the high BMI patient's BSA toward LBM to estimate a new target BF rate. The adjusted BSA and target BF rate are used to select a right-sized circuit. “B” connects to the algorithm in Figure 1.

Figure 2.

FX15, FX25, W40, and W30 are trademarks of Terumo Cardiovascular Systems Inc. The volumes are the crystalloid dynamic prime requirements for the perfusion circuits prior to autologous priming. Target BF is based on BSA and CI = 2.4 L/min/m² and may be adjusted for BMI or LBM. Values in parentheses are optional setups based on patient parameters. “A” connects to the algorithm in Figure 2.