Mechanical ventilation is a primary therapy for patients with respiratory failure. However, incorrect ventilator settings can cause lung damage. Optimising ventilation while minimising risk is complex in practice. A common lung protective strategy is to titrate positive end-expiratory pressure (PEEP) to the point of minimum elastance. This process can result in additional available lung volume due to alveolar recruitment but comes with the risk of subjecting the lungs to excessive pressure and lung damage. Predictive elastance models can mitigate this risk by estimating airway pressure at a higher PEEP level. Due to the increased risk of barotrauma during inspiration, many models exclude expiration data. However, this section of the breath can include useful information about lung mechanics. This research investigates the impact that including expiration data into the fitting of a validated predictive elastance model will have on its ability to predict peak inspiratory pressure. Results showed that expiration data did not improve the efficacy of the model in this case with an increase in error (median (%)) of predicting peak inspiratory pressure through an increase in PEEP of 8 cmH2O from 6% to 16%.
© 2018 the author(s), published by Walter de Gruyter Berlin/Boston
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