Observational studyMedicine2026
Development and validation of a regression equation for VO2 peak prediction in patients with heart and neurologic diseases.
Observational study in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To develop and validate a peak oxygen consumption (VO2 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 VO2 peak, measured via CPET. The model's predictive performance was evaluated using adjusted R2 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 R2 of 0.444 and RMSE of 5.798, outperforming existing prediction equations for patients with heart and neurologic diseases. VO2 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 R2 of 0.4335, indicating good predictive performance when accounting for disease-specific factors. This model provides an accurate, practical tool for estimating VO2 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.
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