Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation. (July 2020)

• Record Type: Journal Article
• Authors: Sun, Qianhui; Zhou, Cong; Chase, J. Geoffrey
• Is Part Of: Biomedical signal processing and control. Volume 60(2020)
• Publication Date: 2020-07
• Subjects: Mechanical ventilation -- Hysteresis loop analysis -- Dynostatic curve -- Pressure-volume loop -- Model fitting -- PEEP -- Lung mechanics model; 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.2020.102003
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper develops a hysteresis loop analysis method to identify lung elastance and resistance at lower PEEP levels. A range of model fitting techniques are employed to predict peak-inspiratory-pressure (PIP) at higher PEEP levels. Clinical data of 4 patients with 8 sets of data provides initial proof of concept of the method for predicting lung mechanics. The approach is readily automated to provide an initial critical information in determining optimal MV and PEEP settings. Abstract: Mechanical ventilation (MV) is the primary way to treat patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU). Positive-end-expiratory-pressure (PEEP) is often applied in MV to maximise gas exchange and prevent further lung damage. However, optimal MV with patient-specific PEEP level is subjective, based on clinician experience, thus increasing the risk variability, and cost of care. In this study, a single compartment lung mechanics model is employed to predict pressure outcomes at higher PEEP levels using data from lower PEEP levels. Particularly, a hysteresis loop analysis (HLA) algorithm is applied to both the dynostatic curve and pressure-volume (PV) loop to identify the lung elastance at lower PEEP levels. Pulmonary resistance can then be calculated with measured airway pressure and identified elastance. Finally, the elastance and resistance at higher PEEP levels are predicted based on model fitting techniques to obtain the pressure with changes of PEEP. The result of pressure fitting and prediction show 95% of the airway pressure curve errors are within 3% across all the 8 data sets, while peak-inspiratory-pressure (PIP) prediction errors are within 1% for all data sets. The overall approach is readily automated, thus providing an initial critical information in determining optimal MV and PEEP settings. Such a model could also be used to validate the prediction accuracy of other more complex models and improve the confidence of their clinical application. Most importantly, the ability to predict PIP and overall pressure trajectories provide a means to safely titrate PEEP to optimal levels without the necessary to build an accurate enough mathematical model which could cost plenty of time and effort.