# Comparative analysis of V˙O2 prediction equations using a novel web-based application: an illustrative example in formerly deployed military veterans

## TL;DR

A new web app helps compare different methods for predicting oxygen consumption in veterans, showing how results vary based on body mass index.

## Contribution

A novel web-based application was developed to compare VO2 prediction equations and demonstrate their variability in veterans.

## Key findings

- Significant variability in VO2peak-pp values was found between prediction equations (Friedman’s χ2 = 936.0, p < 0.01).
- 53% of veterans were reclassified at least once using different equations, with κ values indicating moderate to poor agreement.
- Body mass index was identified as the most significant factor influencing differences in VO2peak-pp.

## Abstract

Cardiopulmonary exercise tests (CPET) use clinical-algorithms for interpretation by classifying exercise capacity based on a fixed threshold (e.g., oxygen consumption percent-predicted ≥80% [
V˙O2peak−pp
]). Impact of prediction equation selection on
V˙O2peak−pp
values and subsequent classifications have not been thoroughly examined in Veterans undergoing specialty evaluation for post-deployment concerns. We developed an application (https://tom26alex-cpx-comparison.share.connect.posit.cloud/) offering a direct comparison of multiple prediction equations for
V˙O2
with data visualizations to better contextualize the individuals achieved
V˙O2peak
.

We retrospectively reviewed CPET records from U.S. Veterans undergoing evaluation for post-deployment concerns and calculated
V˙O2peak−pp
using six separate commonly used prediction equations. Exercise capacity was classified as normal using a fixed threshold (
V˙O2peak−pp
≥80%). Friedman’s test was employed for overall comparison of peak predicted across equations, followed by Cohen’s kappa (κ) to evaluate agreement in exercise capacity classification. The influence of demographic and anthropometric factors on inter-equation differences was examined using regression analysis.

Significant variability was noted in
V˙O2peak−pp
between prediction equations (Friedman’s χ2 = 936.0,
p < 0.01
, Kendall effect size = 0.6). In pairwise analysis, 53% of Veterans in the study were re-classified at least once resulting in significant discordance between all pairs of equations (κ = 0.24–0.78). Regression analysis identified body mass index (BMI) as the most significant contributor to differences in
V˙O2peak−pp
. Given these results the app created focuses on the effects of BMI on equations by providing a visual aid to interpret the effect of BMI changes on the predicted
V˙O2peak
.

Classification of exercise capacity varies considerably as a function of prediction equations, and this variation appears most influenced by anthropometric factors. Clinicians should be aware of this variability and consider alternatives to relying on a single prediction equation approach, such as, utilizing the developed app to visualize and calculate a range of
V˙O2peak

-pp values derived from multiple equations.