Development of a Predictive Pulmonary Elastance Model to Describe Lung Mechanics throughout Recruitment Manoeuvres. Issue 27 (2018)

• Record Type: Journal Article
• Authors: Morton, Sophie E.; Knopp, Jennifer L.; Chase, J. Geoffrey; Docherty, Paul D.; Howe, Sarah L.; Shaw, Geoffrey M.; Tawhai, Merryn
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 27(2018)
• Page Start: 215
• Page End: 220
• Publication Date: 2018
• Subjects: Respiration -- ventilation -- Biological -- medical system modelling -- System identification -- validation -- decision support systems -- model formulation -- mechanical ventilation -- lung mechanics -- virtual patient; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.11.640
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Mechanical ventilation is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause lung damage due to the heterogeneity of healthy and damaged alveoli. A commonly used lung protective strategy is titrating PEEP (positive end expiratory pressure) to minimum elastance. However, in clinical practice this initial PEEP is rarely changed. As lung disease evolves over time, the optimal PEEP required to maintain recruitment also changes. A predictive elastance model could be used to assess whether or not the current PEEP level should be changed based on whether a nearby PEEP had lower elastance. In this study, a physiologically relevant basis function model is developed and tested across the entirety of 18 recruitment manoeuvres. Accurate prediction of pressure and PIP to 10% across each of these data sets validates the functionality of this model.