Abstract EP57: A Non-exercise Prediction Of Cardiorespiratory Fitness For Patients With Cardiovascular Disease: Data From The Fitness Registry And The Importance Of Exercise International Database (FRIEND)
Bibliographic record
Abstract
Introduction: The importance of cardiorespiratory fitness (CRF) for stratifying mortality risk and guiding clinical care in patients with cardiovascular disease (CVD) is well-established. An American Heart Association Scientific Statement suggests routine clinical assessment of CRF using non-exercise prediction equations when direct assessment from a cardiopulmonary exercise test is not feasible. However, current prediction equations have been created from cohorts of apparently healthy individuals. Hypothesis: A CVD-specific non-exercise equation would have higher accuracy for predicting CRF compared to an equation developed from a cohort without known CVD. Methods: Participants 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 performed a cardiopulmonary exercise test were studied (83% [10,417 of 12,578] male; age 62.7 ± 10.3 years). The cohort (12,578 tests; 49% [6,190] treadmill tests) was split into development (10,062) and validation (2,516) groups. The prediction equation was developed using multiple regression analysis and comparisons were made with a CRF prediction equation developed on an apparently healthy cohort using FRIEND. Results: Age, sex, height, body mass, exercise mode, and CVD diagnosis were all significant predictors of CRF. The regression equation was: CRF (mL/kg/min) = 17.03 – (0.21 * age [years]) + (3.60 * sex [male = 1; female = 0]) + (0.12 * height [cm]) – (0.11 * body mass [kg]) + (3.75 * mode [treadmill = 1; cycle = 0]) – (2.40 * CABG [yes = 1, no = 0]) – (0.29 * MI [yes = 1, no = 0]) + (0.75 * PCI [yes = 1, no = 0]) – (3.90 * HF [yes = 1, no = 0]) (adjusted R 2 = 0.42, SEE = 4.74 mL/kg/min). When compared to measured CRF in the validation group (19.6 ± 6.2 mL/kg/min), predicted CRF was similar for the CVD equation (19.8 ± 4.1 mL/kg/min [101%]) and higher for the healthy cohort equation (28.2 ± 7.0 mL/kg/min [144%]; P <0.05). Significant Pearson correlations were found when using either prediction equation although the correlation when using the CVD equation was higher (r = 0.65) than that for the healthy cohort equation (r = 0.48, P <0.05). Differences between equations were also observed for root mean square error (4.7 and 10.9 mL/kg/min for the CVD and healthy cohort equations, respectively). Conclusions: As hypothesized, the CVD-specific non-exercise equation was a better predictor of CRF in a cohort of individuals with CVD. The new equation for individuals with CVD provided a lower mean error between measured and predicted CRF than an equation developed from an apparently healthy cohort. Thus, population specific equations are needed for predicting CRF; however, the error associated with non-exercise prediction equations suggests CRF 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".