An experimental model of veno-venous arterial extracorporeal membrane oxygenation

Introduction: Veno-venous arterial extracorporeal membrane oxygenation is a hybrid-modality of extracorporeal membrane oxygenation combining veno-venous and veno-arterial extracorporeal membrane oxygenation. It may be applied to patients with both respiratory and cardio-circulatory failure. Aim: To describe a computational spreadsheet regarding an ex vivo experimental model of veno-venous arterial extracorporeal membrane oxygenation to determine the return of cannula pairs in a single pump–driven circuit. Methods: We developed an ex vivo model of veno-venous arterial extracorporeal membrane oxygenation with a single pump and two outflow cannulas, and a glucose solution was used to mimic the features of blood. We maintained a fixed aortic impedance and physiological pulmonary resistance. Both flow and pressure data were collected while testing different pairs of outflow cannulas. Six simulations of different cannula pairs were performed, and data were analysed by a custom-made spreadsheet, which was able to predict the flow partition at different flow levels. Results: In all simulations, the flow in the arterial cannula gradually increased differently depending on the cannula pair. The best cannula pair was a 19-Fr/18-cm arterial with a 17-Fr/50-cm venous cannula, where we observed an equal flow split and acceptable flow into the arterial cannula at a lower flow rate of 4 L/min. Conclusion: Our computational spreadsheet identifies the suitable cannula pairing set for correctly splitting the outlet blood flow into the arterial and venous return cannulas in a veno-venous arterial extracorporeal membrane oxygenation configuration without the use of external throttles. Several limitations were reported regarding fixed aortic impedance, central venous pressure and the types of cannulas tested; therefore, further studies are mandatory to confirm our findings

Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction

Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C, peripheral resistance R, aortic impedance r, and the inertia of blood L, to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies.