Long-term prognostic value of resting heart rate in patients with suspected or proven coronary artery disease
Bibliographic record
Abstract
AIMS: Heart rate reduction is the cornerstone of the treatment of angina. The purpose of this study was to explore the prognostic value of heart rate in patients with stable coronary artery disease (CAD). METHODS AND RESULTS: We assessed the relationship between resting heart rate at baseline and cardiovascular mortality/morbidity, while adjusting for risk factors. A total of 24 913 patients with suspected or proven CAD from the Coronary Artery Surgery Study registry were studied for a median follow-up of 14.7 years. All-cause and cardiovascular mortality and cardiovascular rehospitalizations were increased with increasing heart rate (P<0.0001). Patients with resting heart rate > or =83 bpm at baseline had a significantly higher risk for total mortality [hazard ratio (HR)=1.32, CI 1.19-1.47, P<0.0001] and cardiovascular mortality (HR=1.31, CI 1.15-1.48, P<0.0001) after adjustment for multiple clinical variables when compared with the reference group. When comparing patients with heart rates between 77-82 and > or =83 bpm with patients with a heart rate < or =62 bpm, the HR values for time to first cardiovascular rehospitalization were 1.11 and 1.14, respectively (P<0.001 for both). CONCLUSION: Resting heart rate is a simple measurement with prognostic implications. High resting heart rate is a predictor for total and cardiovascular mortality independent of other risk factors in patients with CAD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".