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]:

$$\text{VR}=\frac{{\dot{V}}_{E_{measured}}\times P_{a_{c{o}_{2measured}}}}{{\dot{V}}_{E_{predicted}}\times P_{a_{c{o}_{2predicted}}}}$$

See Figure 1 overleaf (where predicted values are VE 100 ml/kg/minute and PaCO₂ 5 kPa).

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.