Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation. (February 2022)

• Record Type: Journal Article
• Authors: Sun, Qianhui; Chase, J. Geoffrey; Zhou, Cong; Tawhai, Merryn H.; Knopp, Jennifer L.; Möller, Knut; Heines, Serge J; Bergmans, Dennis C.; Shaw, Geoffrey M.
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part B
• Publication Date: 2022-02
• Subjects: Volume control ventilation -- Pressure control ventilation -- Positive end expiratory pressure -- Respiratory mechanics -- Elastance -- Basis function; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103367
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A physiologically relevant, predictive lung mechanics virtual patient model. Nonlinear elastance evolution is captured and predicted with the newly proposed function. Pressure–volume loops and peak pressures/volumes are accurately predicted. The model is validated for both pressure controlled and volume controlled mechanical ventilation modes. The basis function approach used is computationally fast and can be implemented for real-time. Abstract: Mechanical ventilation (MV) is a core treatment for patients suffering from respiratory disease and failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in outcome. There is thus a need to personalize MV settings. This research significantly extends a single compartment lung mechanics model with physiologically relevant basis functions, and uses it to identify patient-specific lung mechanics and predict response to changes in MV settings. Nonlinear evolution of pulmonary elastance over positive end expiratory pressure (PEEP) is modelled by a newly proposed, physiologically relevant and simplified compensatory function to enable prediction of pulmonary response for both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV), and identified as patient-specific using each patient's data at a baseline PEEP. Predictions at higher PEEP levels test the validity of the proposed models based on errors in predicted peak inspiratory pressure (PIP) in two VCV trials and volume (PIV) in one PCV trial. A total of 210 prediction cases over 36 patients (22 VCV; 14 PCV) yielded absolute predicted PIP errors within 1.0 cmH2 O (2.3%) and 3.3 cmH2 O (7.3%) for 90% cases in VCV, while predicted PIV errors are within 0.073 L (16.8%) for 85% cases in PCV. In conclusion, a novel deterministic virtual patient model is presented, able to offer accurate prediction of pulmonary response across a wide range of PEEP changes for the two main MV modes used clinically, enabling predictive decision support in real-time to safely personalize and optimize care.