Abnormal heart-rate response during cardiopulmonary exercise testing identifies cardiac dysfunction in symptomatic patients with non-obstructive coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Symptomatic non-obstructive coronary artery disease is a growing clinical dilemma for which contemporary testing is proving to be of limited clinical utility. New methods are needed to identify cardiac dysfunction. METHODS AND RESULTS: This is a prospective observational cohort study conducted from December 2013 to August 2015 in two outpatient cardiology clinics (symptomatic cohort) and 24 outpatient practices throughout the US (healthy cohort) with centralized methodology and monitoring to compare heart-rate responses during cardiopulmonary exercise testing (CPET). Participants were 208 consecutive patients (median age, 61; range, 32-86years) with exercise intolerance and without prior heart or lung disease in whom coronary anatomy was defined and 116 healthy subjects (median age, 45; range, 26-66years). Compared to stress ECG, the novel change in heart-rate as a function of work-rate parameter (ΔHR-WR Slope) demonstrated significantly higher sensitivity to detect under-treated atherosclerosis with similar specificity. In men, area under the ROC curve increased from 60% to 94% for non-obstructive CAD and from 64% to 80% for obstructive CAD. In women, AUC increased from 64% to 85% for non-obstructive CAD and from 66% to 90% for obstructive CAD. ΔHR-WR Slope correctly reclassified abnormal studies in the non-obstructive CAD group from 22% to 81%; in the obstructive CAD group from 18% to 84% and in the revascularization group from 35% to 78%. CONCLUSION: Abnormal heart-rate response during CPET is more effective than stress ECG for identifying under-treated atherosclerosis and may be of utility to identify cardiac dysfunction in symptomatic patients with normal routine cardiac testing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".