文章基本信息

标题：Estimation of Inspiratory Respiratory Elastance Using Expiratory Data
本地全文：下载
作者：Sarah L. Howe ; J. Geoff Chase ; Daniel P. Redmond 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：27
页码：204-208
DOI：10.1016/j.ifacol.2018.11.642
语种：English
出版社：Elsevier
摘要：AbstractModels of respiratory mechanics can be used to titrate patient-specific mechanical ventilation (MV) settings in critical care, but often perform poorly in the presence of patient breathing effort. Respiratory mechanics are conventionally calculated using only inspiratory data. Muscle activity is normally assumed relatively minimal or absent during passive expiration regardless of the presence of inspiratory spontaneous breathing (SB) efforts. Hence, this study assesses whether expiratory lung elastance can be used to estimate inspiratory lung elastance for spontaneously breathing, reverse triggered patients. Clinical data from recruitment manoeuvres in fully sedated patients were used to determine a relationship between inspiratory and expiratory modeled lung elastance. The validity of this relationship was assessed using data recorded pre- and post- sedation from different patients.A strong, linear relationship was found between inspiratory and expiratory elastance in fully sedated patients, with gradient 1.04 [95% CI: 1.03-1.07] and intercept 1.66 [1.06-2.08] with R2= 0.94. After adjustment according to the linear relationship, expiratory elastance produced stable estimations post sedation, with similar median and variance as inspiratory elastance. However, variation in estimates pre-sedation, although significantly improved, may be larger than clinically acceptable in some cases. The results of this study show that the typically ignored expiratory data may be able to provide insight into patient condition when conventional methods fail. Clinically, these methods could have an impact in guiding MV therapy by providing clinicians with information about lung mechanics under the effect of patient SB effort.
关键词：KeywordsMechanical ventilationMathematical modelsLungsParameter identificationRespiratory elastance