Heart rate in patients with reduced ejection fraction: relationship between single time point measurement and mean heart rate on prolonged implantable cardioverter defibrillator monitoring
Bibliographic record
Abstract
BACKGROUND: Heart rate (HR) is a prognostic marker that is increasingly used as a therapeutic target in patients with cardiovascular disease. The association between resting and mean HR remains unclear. We therefore set out to determine the relationship between resting HR on the electrocardiogram (ECG) obtained at a single time point, and mean HR on implantable cardioverter defibrillator (ICD) interrogation amongst patients with a reduced left ventricular ejection fraction (LVEF). METHODS: Prospective ICD data were obtained from 54 patients with LVEF < 40%. Mean HR determined using the ICD HR histograms was compared with resting HR measured on the ECG performed in the clinic. RESULTS: Average resting and ICD mean HRs were 67.9 ± 10.1 and 67.8 ± 9.6 bpm respectively. There was good correlation in the overall cohort (r = 0.79), in those with resting ECG HRs ≤ 70 bpm (r = 0.62), and amongst the 27 patients on intermediate-to-high dose beta-blockers (r = 0.91). However, Bland-Altman analysis demonstrated wide limits of agreement in the overall cohort (- 12.5, 12.7 bpm), at resting HRs ≤ 70 bpm (- 12.7, 9.8 bpm), and on intermediate-to-high dose beta-blockers (- 8.9, 7.4 bpm). Moreover, resting HR did not predict the 10-bpm interval where the most time was spent. CONCLUSIONS: While resting HR correlated with mean HR in patients with reduced LVEF, and in important subgroups, the limits of agreement were unacceptably wide raising concern over the use of single time point resting HR as a therapeutic target.
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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 | 0.001 | 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 | 0.001 |
| 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".