Skip to main content

Advertisement

You are viewing the new article page. Let us know what you think. Return to old version

Poster presentation | Open | Published:

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

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
figure1

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.

References

  1. 1.

    Sinha , et al.: Br J Anaesth. 2009, 102: 692-697. 10.1093/bja/aep054

  2. 2.

    Hardman , et al.: Br J Anaesth. 1998, 81: 327-329.

  3. 3.

    Bersten , et al.: Am J Respir Crit Care Med. 2002, 165: 443-448.

Download references

Author information

Correspondence to P Sinha.

Rights and permissions

Reprints and Permissions

About this article

Keywords

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