Resting heart rate is associated with renal disease outcomes in patients with vascular disease: results of the <scp>ONTARGET</scp> and <scp>TRANSCEND</scp> studies
Bibliographic record
Abstract
BACKGROUND: Resting heart rate (RHR) is associated with cardiovascular disease outcomes in high-risk patients. It is not known whether RHR is predictive of renal outcomes such as albuminuria, end-stage renal disease (ESRD) or doubling of creatinine. We evaluated whether RHR could predict renal endpoints in patients at a high risk of cardiovascular disease. We also tested the effects of RHR at different levels of systolic blood pressure (SBP). METHODS: We analysed data from 28 757 patients in the ONTARGET and TRANSCEND trials. RHR and SBP were available for a mean of 4.9 ± 0.4 visits (range 3-5) within the first 2 years of the studies. Albuminuria was determined at baseline, at 2 years and at study end. RESULTS: Mean RHR was predictive of incident micro-albuminuria [hazard ratio (HR) for RHR ≥80 vs. <60 beats min(-1) 1.49, 95% confidence interval (CI) 1.29-1.71, P < 0.0001], incident macro-albuminuria (HR 1.84, 95% CI 1.39-2.42, P < 0.0001), doubling of creatinine (HR 1.47, 95% CI 1.00-2.17, P = 0.050) and ESRD (HR 1.78, 95% CI 1.00-3.16, P = 0.050), and the combined renal end-point (HR 1.51, 95% CI 1.32-1.74, P < 0.0001). Associations were robust at SBPs from <120 to ≥150 mmHg, with the lowest risk at a SBP of 130-140 mmHg. CONCLUSION: Resting heart rate is a potent predictor of these renal outcomes, as well as their combination, in patients with cardiovascular disease. RHR at all SBP levels should be considered as a possible renal disease risk predictor and should be investigated as a treatment target with RHR-reducing agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".