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

Expiration Intensive care Mechanical ventilation Model-based methods Time constant

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Apr 2020
Historique:
received: 04 07 2019
revised: 29 10 2019
accepted: 02 11 2019
pubmed: 13 11 2019
medline: 20 1 2021
entrez: 13 11 2019
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

BACKGROUND AND OBJECTIVE 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 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 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 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.

Identifiants

pubmed: 31715280
pii: S0169-2607(19)31040-5
doi: 10.1016/j.cmpb.2019.105184
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105184

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

S L Howe (SL)

University of Canterbury, 8041, Christchurch, New Zealand. Electronic address: sarah.howe@pg.canterbury.ac.nz.

J G Chase (JG)

University of Canterbury, 8041, Christchurch, New Zealand.

D P Redmond (DP)

University of Canterbury, 8041, Christchurch, New Zealand.

S E Morton (SE)

University of Canterbury, 8041, Christchurch, New Zealand.

K T Kim (KT)

University of Canterbury, 8041, Christchurch, New Zealand.

C Pretty (C)

University of Canterbury, 8041, Christchurch, New Zealand.

G M Shaw (GM)

Christchurch Hospital, 8011, Christchurch, New Zealand.

M H Tawhai (MH)

University of Auckland, Auckland, New Zealand.

T Desaive (T)

University of Lige, Lige, Belgium.

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Classifications MeSH