Incorporating pulse wave velocity into model-based pulse contour analysis method for estimation of cardiac stroke volume. (October 2020)

• Record Type: Journal Article
• Authors: Smith, Rachel; Balmer, Joel; Pretty, Christopher G.; Mehta-Wilson, Tashana; Desaive, Thomas; Shaw, Geoffrey M.; Chase, J. Geoffrey
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Pulse contour analysis -- Pressure contour analysis -- Pulse wave velocity -- Windkessel model -- Stroke volume -- Cardiac output -- Hemodynamic monitoring -- Intensive care; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105553
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Pulse wave velocity used for dynamic parameter identification of pulse contour model. Validation using clinically relevant pressure waveforms during recruitment manoeuvre and induced septic shock interventions. Stroke volume estimation accuracy was not improved by incorporating pulse wave velocity for parameter identification. Abstract: Background and Objectives: Stroke volume (SV) and cardiac output (CO) are important metrics for hemodynamic management of critically ill patients. Clinically available devices to continuously monitor these metrics are invasive, and less invasive methods perform poorly during hemodynamic instability. Pulse wave velocity (PWV) could potentially improve estimation of SV and CO by providing information on changing vascular tone. This study investigates whether using PWV for parameter identification of a model-based pulse contour analysis method improves SV estimation accuracy. Methods: Three implementations of a 3-element windkessel pulse contour analysis model are compared: constant- Z, water hammer, and Bramwell-Hill methods. Each implementation identifies the characteristic impedance parameter ( Z ) differently. The first method identifies Z statically and does not use PWV, and the latter two methods use PWV to dynamically update Z . Accuracy of SV estimation is tested in an animal trial, where interventions induce severe hemodynamic changes in 5 pigs. Model-predicted SV is compared to SV measured using an aortic flow probe. Results: SV percentage error had median bias and [(IQR); (2.5th, 97.5th percentiles)] of -0.5% [(-6.1%, 4.7%); (-50.3%, +24.1%)] for the constant- Z method, 0.6% [(-4.9%, 6.2%); (-43.4%, +29.3%)] for the water hammer method, and 0.8% [(-6.5, 8.6); (-37.1%, +47.6%)] for the Bramwell-Hill method. Conclusion: Incorporating PWV for dynamic Z parameter identification through either the Bramwell-Hill equation or the water hammer equation does not appreciably improve the 3-element windkessel pulse contour analysis model's prediction of SV during hemodynamic changes compared to the constant- Z method.