Comparative analysis of VO2 prediction equations using a novel web-based application: an illustrative example in formerly deployed military veterans

Background 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 ˙ O 2 peak − pp ). Impact of prediction equation selection on V ˙ O 2 peak − 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 ˙ O 2 with data visualizations to better contextualize the individuals achieved V ̇ O 2 p e a k . Methods We retrospectively reviewed CPET records from U.S. Veterans undergoing evaluation for post-deployment concerns and calculated V ˙ O 2 peak − pp using six separate commonly used prediction equations. Exercise capacity was classified as normal using a fixed threshold ( V ˙ O 2 peak − 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. Results Significant variability was noted in V ˙ O 2 peak − 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 ˙ O