Estimating Equations for Cardiopulmonary Exercise Testing Variables
    in Fontan Patients: Derivation and Validation Using a Multicenter
    Cross-Sectional Database

Butts, Ryan; Spencer, Carolyn; Jackson,
Lanier; Heal, Martha; Forbus, Geoffrey; Hulsey, Thomas; Atz, Andrew

Pediatric Cardiology, February 2015, Vol. 36 Issue: Number 2 p393-401,
9p;

Abstract: Cardiopulmonary exercise testing (CPET) is a common
method of evaluating patients with a Fontan circulation. Equations to
calculate predicted CPET values are based on children with normal
circulation. This study aims to create predictive equations for CPET
variables solely based on patients with Fontan circulation. Patients
who performed CPET in the multicenter Pediatric Heart Network Fontan
Cross-Sectional Study were screened. Peak variable equations were
calculated using patients who performed a maximal test (RER > 1.1) and
anaerobic threshold (AT) variable equations on patients where AT was
adequately calculated. Eighty percent of each cohort was randomly
selected to derive the predictive equation and the remaining served as
a validation cohort. Linear regression analysis was performed for each
CPET variable within the derivation cohort. The resulting equations
were applied to calculate predicted values in the validation cohort.
Observed versus predicted variables were compared in the validation
cohort using linear regression. 411 patients underwent CPET, 166
performed maximal exercise tests and 317 had adequately calculated AT.
Predictive equations for peak CPET variables had good performance; peak
VO2, R2= 0.61; maximum work, R2= 0.61; maximum O2pulse, R2= 0.59. The
equations for CPET variables at AT explained less of the variability;
VO2at AT, R2= 0.15; work at AT, R2= 0.39; O2pulse at AT, R2= 0.34;
VE/VCO2at AT, R2= 0.18; VE/VO2at AT, R2= 0.14. Only the models for
VE/VCO2and VE/VO2at AT had significantly worse performance in
validation cohort. Of the 8 equations for commonly measured CPET
variables, six were able to be validated. The equations for peak
variables were more robust in explaining variation in values than AT
equations.