A Nonexercise Prediction of Peak Oxygen Uptake for Patients With Cardiovascular Disease: DATA FROM THE FITNESS REGISTRY AND THE IMPORTANCE OF EXERCISE INTERNATIONAL DATABASE (FRIEND). Issue 2 (22nd March 2023)

• Record Type: Journal Article
• Authors: Peterman, James E.; Arena, Ross; Myers, Jonathan; Ades, Philip A.; Bonikowske, Amanda R.; Harber, Matthew P.; Marzolini, Susan; Savage, Patrick D.; Squires, Ray W.; Lavie, Carl J.; Kaminsky, Leonard A.
• Is Part Of: Journal of cardiopulmonary rehabilitation and prevention. Volume 43:Issue 2(2023)
• Page Start: 115
• Page End: 121
• Publication Date: 2023-03-22
• Subjects: cardiopulmonary exercise testing -- cardiorespiratory fitness -- health -- maximum oxygen uptake -- risk factors; Cardiopulmonary system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Cardiopulmonary system -- Diseases -- Prevention -- Periodicals; 616.103
• DOI: 10.1097/HCR.0000000000000722
• Languages: English
• ISSNs: 1932-7501

Abstract

Abstract : This study developed a nonexercise prediction equation for peak oxygen uptake (Vo 2peak ) in individuals with cardiovascular disease. The new equation provided a lower mean error between measured and predicted Vo 2peak than an equation developed from a healthy cohort highlighting the importance of using a population-specific equation when predicting Vo 2peak . Abstract : Purpose: Nonexercise predictions of peak oxygen uptake (V˙o 2peak ) are used clinically, yet current equations were developed from cohorts of apparently healthy individuals and may not be applicable to individuals with cardiovascular disease (CVD). Our purpose was to develop a CVD-specific nonexercise prediction equation for V˙o 2peak . Methods: Participants were from the Fitness Registry and Importance of Exercise International Database (FRIEND) with a diagnosis of coronary artery bypass surgery (CABG), myocardial infarction (MI), percutaneous coronary intervention (PCI), or heart failure (HF) who met maximal effort criteria during a cardiopulmonary exercise test (n = 15 997; 83% male; age 63.1 ± 10.4 yr). The cohort was split into development (n = 12 798) and validation groups (n = 3199). The prediction equation was developed using regression analysis and compared with a previous equation developed on a healthy cohort. Results: Age, sex, height, weight, exercise mode, and CVD diagnosis were all significant predictors of V˙o 2peak . The regression equation was: V˙o 2peak (mL · kg –1 · min –1 ) = 16.18 – (0.22 × age [yr]) + (3.63 × sex [male = 1; female = 0]) + (0.14 × height [cm]) – (0.12 × weight [kg]) + (3.62 × mode [treadmill = 1; cycle = 0]) – (2.70 × CABG [yes = 1, no = 0]) – (0.31 × MI [yes = 1, no = 0]) + (0.37 × PCI [yes = 1, no = 0]) – (4.47 × HF [yes = 1, no = 0]). Adjusted R 2 = 0.43; SEE = 4.75 mL · kg –1 · min –1 . Compared with measured V˙o 2peak in the validation group, percent predicted V˙o 2peak was 141% for the healthy cohort equation and 100% for the CVD-specific equation. Conclusions: The new equation for individuals with CVD had lower error between measured and predicted V˙o 2peak than the healthy cohort equation, suggesting population-specific equations are needed for predicting V˙o 2peak ; however, errors associated with nonexercise prediction equations suggest V˙o 2peak should be directly measured whenever feasible.