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  • Open Access

Ventilatory ratio: validation in an ex vivo model and analysis in ARDS/ALI patients

  • 1,
  • 2,
  • 3,
  • 2 and
  • 1
Critical Care201115 (Suppl 1) :P179

https://doi.org/10.1186/cc9599

  • Published:

Keywords

  • Emergency Medicine
  • Computational Model
  • Poor Correlation
  • Normal Patient
  • Minute Ventilation

Introduction

Several indices exist to monitor adequate oxygenation, but no such index exists for ventilatory efficiency. The ventilatory ratio (VR) is a simple tool to monitor changes in ventilatory efficiency using variables commonly measured at the bedside [1]:
VR = V ˙ E m e a s u r e d × P a c o 2 m e a s u r e d V ˙ E p r e d i c t e d × P a c o 2 p r e d i c t e d
See Figure 1 overleaf (where predicted values are VE 100 ml/kg/minute and PaCO2 5 kPa).
Figure 1
Figure 1

Chi-squared test for trends P = 0.0015.

Methods

The Nottingham Physiology Simulator (NPS), a validated computational model of cardiopulmonary physiology [2], was used to validate the ability of VR to reflect ventilatory efficiency ex vivo. Three virtual patients were configured, representing healthy lung, ARDS and COPD. VR was calculated while minute ventilation, ventilation rate and VCO2 were each varied in isolation. The clinical uses of VR were then examined in a database comprising 122 patients with ALI and ARDS [3]. Standard respiratory data and VR values were analysed in all patients.

Results

The NPS model showed significant correlation between VR and physiological deadspace fraction (Vd/Vtphys) at constant VCO2 (P < 0.001, r = 0.99). Similarly, VCO2 had a linear relationship with VR at constant Vd/Vtphys. Across the various ventilatory configurations the median values and ranges of calculated VR for the three patients were as follows: normal patient VR 0.89 (0.61 to 1.36), COPD 1.36 (0.95 to 1.89) and ARDS 1.73 (1.2 to 2.62). In the ALI/ARDS database the range of values for VR was 0.56 to 3.93 (median 1.36). Patients with ARDS had a significantly higher VR in comparison with patients with ALI (1.44, 1.25 to 1.77 vs. 1.25, 0.94 to 1.6, P = 0.02). VR was significantly higher in nonsurvivors as compared with survivors (1.7 ± 0.64 vs. 1.45 ± 0.56, P < 0.03). There was poor correlation between PaO2/FiO2 ratio and VR in the population (r = -0.32, 95% CI = -0.47 to -0.15).

Conclusions

Ex vivo modling shows that VR can be simply and reliably used to monitor ventilatory efficiency at the bedside. VR is influenced by changing CO2 production and deadspace ventilation. As a clinical tool it is a predictor of outcome and is independent to PaO2/FiO2 ratio.

Authors’ Affiliations

(1)
Chelsea and Westminster NHS Foundation Trust, London, UK
(2)
Queen's Medical Centre, Nottingham, UK
(3)
ANZICS CTG, Flinders Medical Center, Adelaide, Australia

References

  1. Sinha , et al.: Br J Anaesth. 2009, 102: 692-697. 10.1093/bja/aep054View ArticlePubMedGoogle Scholar
  2. Hardman , et al.: Br J Anaesth. 1998, 81: 327-329.View ArticlePubMedGoogle Scholar
  3. Bersten , et al.: Am J Respir Crit Care Med. 2002, 165: 443-448.View ArticlePubMedGoogle Scholar

Copyright

© Sinha et al. 2011

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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