Clinically applicable model-based method, for physiologically accurate flow waveform and stroke volume estimation. (March 2020)

• Record Type: Journal Article
• Authors: Balmer, Joel; Pretty, Christopher G.; Davidson, Shaun; Mehta-Wilson, Tashana; Desaive, Thomas; Smith, Rachel; Shaw, Geoffrey M.; Chase, J. Geoffrey
• Is Part Of: Computer methods and programs in biomedicine. Volume 185(2020)
• Publication Date: 2020-03
• Subjects: Pulse contour analysis -- Pressure contour analysis -- Windkessel model -- Stroke volume -- Cardiac output -- End systole detection -- 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.2019.105125
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Accurate end systole detection the capstone of pulse contour based flow estimation. Superior end systole detection enables dynamic windkessel parameter identification. Improved parameter identification enables monitoring during transient hemodynamics. Accurate reservoir pressure enables physiological flow waveform estimation. Validation using clinically relevant pressure waveforms during induced septic shock. Abstract: Background and Objectives: Cardiovascular dysfunction can be more effectively monitored and treated, with accurate, continuous, stroke volume (SV) and/or cardiac output (CO) measurements. Since direct measurements of SV/CO are highly invasive, clinical measures are often discrete, or if continuous, can require recalibration with a discrete SV measurement after hemodynamic instability. This study presents a clinically applicable, non-additionally invasive, physiological model-based, SV and CO measurement method, which does not require recalibration during or after hemodynamic instability. Methods and Results: The model's ability to predict flow profiles and SV is assessed in an animal trial, using endotoxin to induce sepsis in 5 pigs. Mean percentage error between beat-to-beat SV measured from an aortic flow probe and estimated by the model was −2%, while 90% of estimations fell within −24.2% and +27.9% error. Error between estimated and measured changes in mean SV following interventions was less than 30% for 4 out of the 5 pigs. Correlations between model estimated and probe measured flow, for each pig and hemodynamic interventions, was r 2 = 0.58 − 0.96, with 21 of the 25 pig intervention stages having r 2 > 0.80. Conclusion: The results demonstrate the model accurately estimates and tracks changes in flow profiles and resulting SV, without requiring model recalibration.