Estimating the true respiratory mechanics during asynchronous pressure controlled ventilation. (September 2016)

• Record Type: Journal Article
• Authors: Kannangara, D.O.; Newberry, F.; Howe, S.; Major, V.; Redmond, D.; Szlavecs, A.; Chiew, Y.S.; Pretty, C.; Benyo, B.; Shaw, G.M.; Chase, J.G.
• Is Part Of: Biomedical signal processing and control. Volume 30(2016)
• Page Start: 70
• Page End: 78
• Publication Date: 2016-09
• Subjects: Mechanical ventilation -- Intensive care -- Asynchrony -- Pulmonary -- Mechanics -- Model -- Identification; 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.2016.06.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A novel flow reconstruction method is presented and validated (on clinical data). Method enables improved estimation of underlying elastance to guide MV. Automated detection of AEs. First ever quantification of AEs. Will enable further study of impact on clinical outcomes. Abstract: Mechanical ventilation (MV) therapy partially or fully replaces the work of breathing in patients with respiratory failure. Respiratory mechanics during pressure controlled (PC) or pressure support (PS) are often not estimated due to variability induced by patient's spontaneous breathing effort (SB) or asynchronous events (AEs). Thus for non-invasive model-based MV with PC/PS, there is a need for improved estimation of respiratory mechanics. An algorithm is proposed that allows for the improvement of respiratory system mechanics estimation during pressure controlled ventilation, while providing a means of quantifying AE magnitude as one indicator of patient-ventilator interaction, which may be valuable to clinicians to monitor patient response to care. For testing, 10 retrospective airway pressure and flow data samples were obtained from 6 MV patients, with each data sample containing 450–500 breaths. All data samples with AE present experienced a decrease in 5th to 95th range (Range90) and mean absolute deviation (MAD) for the estimated respiratory system elastance after reconstruction. These results suggested improved in respiratory mechanics estimation during pressure controlled ventilation. The median [maximum (max), minimum (min)] decrease in MAD was 29.4% (51%, 18.6%), and the median (max, min) decrease in Range90 divided by median respiratory system elastance was 30.7% (48.8%, 6.4%). The algorithm is robust to many different spontaneous breathing efforts, asynchrony shapes and types. The proposed algorithm demonstrates the potential to effectively improve respiratory mechanics and quantify the magnitude of AEs.