A newly developed VO2 peak prediction model for patients with heart and neurologic diseases demonstrated good predictive performance (adjusted R2 0.444, RMSE 5.798), outperforming existing equations.
Cross-Sectional (n=269)
No
Does a novel disease-specific regression equation improve the prediction of VO2 peak compared to established equations in patients with combined heart and neurologic diseases?
A newly developed regression equation incorporating disease-specific factors provides a more accurate estimation of VO2 peak than established models in patients with combined cardiovascular and neurologic conditions.
To develop and validate a peak oxygen consumption (VO 2 peak) prediction model for Korean patients with both cardiovascular and neurologic diseases, addressing limitations in existing models that fail to account for disease-specific physiological interactions. Retrospective observational cross-sectional study with model development and validation. Data were collected from a single tertiary care hospital, Myongji Hospital, South Korea. A total of 269 patients (mean age: 61.62 ± 13.37 years, 76% male) who underwent cardiopulmonary exercise testing (CPET) between 2019 and 2021. Predictor variables included age, sex, body mass index (BMI), presence of neurologic disease, and type of heart disease. The primary outcome was VO 2 peak, measured via CPET. The model’s predictive performance was evaluated using adjusted R 2 and root mean square error (RMSE) and compared with established equations by Wasserman, Hansen, Jones, and Dun. Leave-one-subject-out cross-validation (LOSO-CV) was performed to assess generalizability. The final model demonstrated an adjusted R 2 of 0.444 and RMSE of 5.798, outperforming existing prediction equations for patients with heart and neurologic diseases. VO 2 peak was negatively influenced by age, BMI, and neurologic disease, while coronary heart disease had a relatively positive association compared to other heart conditions. Validation using LOSO-CV showed an R 2 of 0.4335, indicating good predictive performance when accounting for disease-specific factors. This model provides an accurate, practical tool for estimating VO 2 peak in patients with both cardiovascular and neurologic conditions, supporting personalized rehabilitation and treatment planning. Future studies should aim to validate the model in diverse populations to enhance its generalizability.
Lee et al. (Fri,) conducted a cross-sectional in Cardiovascular and neurologic diseases (n=269). New VO2 peak prediction model vs. Established equations (Wasserman, Hansen, Jones, and Dun) was evaluated on VO2 peak. A newly developed VO2 peak prediction model for patients with heart and neurologic diseases demonstrated good predictive performance (adjusted R2 0.444, RMSE 5.798), outperforming existing equations.