A Nonexercise Prediction of Peak Oxygen Uptake for Patients With Cardiovascular Disease
Bibliographic record
Abstract
PURPOSE: Nonexercise predictions of peak oxygen uptake (V˙ o2peak ) 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˙ o2peak . 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˙ o2peak . The regression equation was:V˙ o2peak (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˙ o2peak in the validation group, percent predicted V˙ o2peak 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˙ o2peak than the healthy cohort equation, suggesting population-specific equations are needed for predicting V˙ o2peak ; however, errors associated with nonexercise prediction equations suggest V˙ o2peak should be directly measured whenever feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".