Nonexercise prediction equation for oxygen uptake at anaerobic threshold in Chinese patients with cardiovascular disease.

Authors

Shiyu Wang, Jiaqi Liu, Zhongyan Li, Xiang Xie, Jing Yao, Yujie Zhu, Wei Jiang, Yang Li, Xiaojun Wu, Haoning Cui, Xianghui Zheng, Xinyu Hou, Qifeng Li, Tianhui Cao, Yang Zheng, Jian Wu, Bo Yu

Published in

European journal of applied physiology. Aug 03, 2026. Epub Aug 03, 2026.

Abstract

Oxygen uptake at anaerobic threshold (VO₂AT) is an important indicator for guiding cardiovascular disease (CVD) rehabilitation training and evaluating cardiac function, which varies according to race and specific disease conditions. However, there is no well-established nonexercise prediction equation for VO₂AT.
This study aims to develop a nonexercise predictive equation for VO₂AT that is applicable to Chinese patients with CVD.
A cohort was established with 22,336 cases of cardiopulmonary exercise testing data from CVD patients across 20 hospitals in China. Approximately 70% of the samples (n = 15,635) were used for development of prediction equation, and the remaining 30% (n = 6701) were selected for equation validation. The CVD equation for VO₂AT was developed using a multivariate forward stepwise regression analysis. In validation group, the stability and accuracy of VO₂AT equation were evaluated.
The prediction equation is as follows: VO₂AT (mL/min) = 314.748 - (3.887 × age [years]) + (85.933*sex [male = 1; female = 0]) + (1.085 × height [cm]) + (7.790 × weight [kg]) + (17.808 × stable angina [yes=1, no=0]) - (85.469 × percutaneous coronary intervention [yes=1, no=0]) - (112.64 × myocardial infarction [yes=1, no=0]) - (159.730 × heart failure [yes=1, no=0]). The standard error of the estimate = 264 mL/min; R = 0.71; and adjusted R²= 0.51. The VO2AT values predicted by the equation for subgroups were consistent with their corresponding measured values.
This study presents the first nonexercise VO₂AT prediction equation for Chinese CVD patients, offering a new tool for cardiac rehabilitation and potential risk stratification.

PMID:
42545474
Bibliographic data and abstract were imported from PubMed on 03 Aug 2026.

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