Expected drainage pressures on extracorporeal membrane oxygenation based on in-vitro modeling of pressure-flow variables.

Authors

Yeu Sanz Wu, Caitlin J Cain-Trivette, Samantha Lai, Caleb Varner, Kenmond Fung, Weijia Fan, Jimmy Duong, Bin Cheng, Nicholas Schmoke, Christopher Nemeh, William Middlesworth, Vincent Duron

Published in

The journal of extra-corporeal technology. Volume 58. Issue 3. Pages 246-254. Epub Sep 15, 2026.

Abstract

Pressure readings used in extracorporeal membrane oxygenation (ECMO) guide a variety of clinical interventions. However, several factors affect the accuracy of these measurements. We aimed to determine a model to mathematically predict expected pressure readings for different pediatric ECMO cannulae.
We assembled an in-vitro circuit simulating those used clinically. Flow (mL/min) was adjusted, and the corresponding pressure drop (mm Hg) across the cannula was documented for various Medtronic Bio-medicus^® and Life Support^® cannulae. We utilized both water and blood product mixtures of differing hematocrit (Hct) levels - analogous to differing viscosities. Experimentally derived mathematical models were then applied to clinical data.
The relationship between flow and pressure drops across different cannulae and at different Hct levels was graphed and analyzed. At the same flow, larger cannula size corresponded to smaller drops in pressure, and increased viscosity corresponded to larger drops in pressure. Using regression models of the experimental data, we estimated five exponential equations that include Hct and flow to predict the expected pressure drop for different pediatric ECMO cannulae. There was high variability in data among patients, with significant differences between clinically observed and expected pressures.
Our models derived from in-vitro experimentation are the first steps towards defining the pressure-flow relationship and validating drainage pressure as a surrogate measure of intravascular volume for critically ill patients on ECMO. Further studies are still needed to refine these models and validate their clinical applicability.

PMID:
42743425
Bibliographic data and abstract were imported from PubMed on 16 Sep 2026.

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