Literature DB >> 31715280

Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles.

S L Howe1, J G Chase2, D P Redmond2, S E Morton2, K T Kim2, C Pretty2, G M Shaw3, M H Tawhai4, T Desaive5.   

Abstract

BACKGROUND AND
OBJECTIVE: Model-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring lung mechanics are not appropriate for both fully and partially sedated patients, and are unable provide lung mechanics metrics in real-time. This study proposes a novel method of using lung mechanics identified during passive expiration to estimate inspiratory lung mechanics for spontaneously breathing patients.
METHODS: Relationships between inspiratory and expiratory modeled lung mechanics were identified from clinical data from 4 fully sedated patients. The validity of these relationships were assessed using data from a further 4 spontaneously breathing patients.
RESULTS: For the fully sedated patients, a linear relationship was identified between inspiratory and expiratory elastance, with slope 1.04 and intercept 1.66. The r value of this correlation was 0.94. No cohort-wide relationship was determined for airway resistance. Expiratory elastance measurements in spontaneously breathing patients were able to produce reasonable estimates of inspiratory elastance after adjusting for the identified difference between them.
CONCLUSIONS: This study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Expiration; Intensive care; Mechanical ventilation; Model-based methods; Time constant

Mesh:

Year:  2019        PMID: 31715280     DOI: 10.1016/j.cmpb.2019.105184

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  3 in total

1.  Quantifying neonatal patient effort using non-invasive model-based methods.

Authors:  Kyeong Tae Kim; Jennifer Knopp; Bronwyn Dixon; J Geoffrey Chase
Journal:  Med Biol Eng Comput       Date:  2022-01-19       Impact factor: 2.602

2.  Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.

Authors:  Cong Zhou; J Geoffrey Chase; Qianhui Sun; Jennifer Knopp; Merryn H Tawhai; Thomas Desaive; Knut Möller; Geoffrey M Shaw; Yeong Shiong Chiew; Balazs Benyo
Journal:  Biomed Eng Online       Date:  2022-03-07       Impact factor: 2.819

3.  Measuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion.

Authors:  Sarah L Howe; Melanie März; Sabine Krüger-Ziolek; Bernhard Laufer; Chris Pretty; Geoffery M Shaw; Thomas Desaive; Knut Möller; J Geoffrey Chase
Journal:  Biomed Eng Online       Date:  2020-05-14       Impact factor: 2.819

  3 in total

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