Improving Reporting of Exercise Capacity Across Age Ranges Using Novel Workload Reference Equations

Everton J Santana; Jeffrey W Christle; Nicholas Cauwenberghs; James E Peterman; Vincent Busque; Bruna Gomes; Shadi P Bagherzadeh; Kegan Moneghetti; Tatiana Kuznetsova; Matthew Wheeler; Euan Ashley; Matthew P Harber; Ross Arena; Leonard A Kaminsky; Jonathan Myers; Francois Haddad

doi:10.1016/j.amjcard.2024.01.022

Improving Reporting of Exercise Capacity Across Age Ranges Using Novel Workload Reference Equations

Am J Cardiol. 2024 Mar 15:215:32-41. doi: 10.1016/j.amjcard.2024.01.022. Epub 2024 Jan 30.

Authors

Everton J Santana¹, Jeffrey W Christle², Nicholas Cauwenberghs³, James E Peterman⁴, Vincent Busque⁵, Bruna Gomes⁶, Shadi P Bagherzadeh⁵, Kegan Moneghetti⁷, Tatiana Kuznetsova³, Matthew Wheeler⁵, Euan Ashley⁸, Matthew P Harber⁹, Ross Arena¹⁰, Leonard A Kaminsky¹¹, Jonathan Myers¹², Francois Haddad¹³

Affiliations

¹ Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cardiovascular Institute, Stanford University, Stanford, California; Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium. Electronic address: evertonsantana@stanford.edu.
² Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cardiovascular Institute, Stanford University, Stanford, California; Stanford Sports Cardiology, Department of Medicine, Stanford University, Stanford, California.
³ Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
⁴ Fisher Institute of Health and Well-Being, Ball State University, Muncie, Indiana.
⁵ Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California.
⁶ Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cardiovascular Institute, Stanford University, Stanford, California; Department of Cardiology, Pneumology and Angiology, Heidelberg University Hospital, Heidelberg, Germany.
⁷ Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia.
⁸ Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Department of Genetics, Stanford University School of Medicine, Stanford, California; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California.
⁹ Clinical Exercise Physiology Laboratory, College of Health, Ball State University, Muncie, Indiana.
¹⁰ Department of Physical Therapy, College of Applied Sciences, University of Illinois at Chicago, Chicago, Illinois; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, Illinios.
¹¹ Fisher Institute of Health and Well-Being, Ball State University, Muncie, Indiana; Clinical Exercise Physiology Laboratory, College of Health, Ball State University, Muncie, Indiana; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, Illinios.
¹² Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, Illinios; Division of Cardiology, Veterans Affairs Palo Alto Healthcare System and Stanford University, Palo Alto, California. Electronic address: drj993@aol.com.
¹³ Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cardiovascular Institute, Stanford University, Stanford, California; Stanford Diabetes Research Center, Stanford University, Stanford, California; Wu Tsai Performance Alliance, Stanford University, Stanford, California. Electronic address: fhaddad@stanford.edu.

PMID: 38301753
DOI: 10.1016/j.amjcard.2024.01.022

Abstract

Exercise capacity (EC) is an important predictor of survival in the general population and in subjects with cardiopulmonary disease. Despite its relevance, considering the percent-predicted workload (%pWL) given by current equations may overestimate EC in older adults. Therefore, to improve the reporting of EC in clinical practice, our main objective was to develop workload reference equations (pWL) that better reflect the relation between workload and age. Using the Fitness Registry and the Importance of Exercise National Database (FRIEND), we analyzed a reference group of 6,966 apparently healthy participants and 1,060 participants with heart failure who underwent graded treadmill cardiopulmonary exercise testing. For the first group, the mean age was 44 years (18 to 79); 56.5% of participants were males and 15.4% had obesity. Peak oxygen consumption was 11.6 ± 3.0 METs in males and 8.5 ± 2.4 METs in females. After partition analysis, we first developed sex-specific pWL equations to allow comparisons to a healthy weight reference. For males, pWL (METs) = 14.1-0.9×10^-3×age² and 11.5-0.87×10^-3×age² for females. We used those equations as denominators of %pWL, and based on their distribution, we determined thresholds for EC classification, with average EC defined by the range corresponding to 85% to 115%pWL. Compared with %pWL using current equations, the new equations yielded better-calibrated %pWL across different age ranges. We also derived body mass index-adjusted pWL equations that better assessed EC in subjects with heart failure. In conclusion, the novel pWL equations have the potential to impact the report of EC in practice.

Keywords: cardiorespiratory fitness; exercise testing; nonlinear statistical models; reference limits.

MeSH terms

Adult
Aged
Body Mass Index
Child, Preschool
Exercise Tolerance
Female
Heart Failure*
Humans
Male
Pulmonary Heart Disease*
Workload