Typical patterns of expiratory flow and carbon dioxide in mechanically ventilated patients with spontaneous breathing

S. E. Rees, S. Larraza, N. Dey, S. Spadaro, J. B. Brohus, R. W. Winding, C. A. Volta, D. S. Karbing

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Incomplete expiration of tidal volume can lead to dynamic hyperinflation and auto-PEEP. Methods are available for assessing these, but are not appropriate for patients with respiratory muscle activity, as occurs in pressure support. Information may exist in expiratory flow and carbon dioxide measurements, which, when taken together, may help characterize dynamic hyperinflation. This paper postulates such patterns and investigates whether these can be seen systematically in data. Two variables are proposed summarizing the number of incomplete expirations quantified as a lack of return to zero flow in expiration (IncExp), and the end tidal CO2 variability (varetCO2), over 20 breaths. Using these variables, three patterns of ventilation are postulated: (a) few incomplete expirations (IncExp < 2) and small varetCO2; (b) a variable number of incomplete expirations (2 ≤ IncExp ≤ 18) and large varetCO2; and (c) a large number of incomplete expirations (IncExp > 18) and small varetCO2. IncExp and varetCO2 were calculated from data describing respiratory flow and CO2 signals in 11 patients mechanically ventilated at 5 levels of pressure support. Data analysis showed that the three patterns presented systematically in the data, with periods of IncExp < 2 or IncExp > 18 having significantly lower variability in end-tidal CO2 than periods with 2 ≤ IncExp ≤ 18 (p < 0.05). It was also shown that sudden change in IncExp from either IncExp < 2 or IncExp > 18 to 2 ≤ IncExp ≤ 18 results in significant, rapid, change in the variability of end-tidal CO2p < 0.05. This study illustrates that systematic patterns of expiratory flow and end-tidal CO2 are present in patients in supported mechanical ventilation, and that changes between these patterns can be identified. Further studies are required to see if these patterns characterize dynamic hyperinflation. If so, then their combination may provide a useful addition to understanding the patient at the bedside.

Original languageEnglish
Pages (from-to)773-781
Number of pages9
JournalJournal of Clinical Monitoring and Computing
Volume31
Issue number4
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Critical Care and Intensive Care Medicine
  • Anesthesiology and Pain Medicine

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