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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".