[OP.LB01.01] CARDIOVASCULAR RISK IN RELATION TO ACHIEVED AUTOMATED BLOOD PRESSURE IN CLINICAL PRACTICE
Bibliographic record
Abstract
Objective: The results of the Systolic Blood Pressure Intervention Trial (SPRINT) have renewed interest in the optimum BP target for treated hypertension. Using automated office blood pressure (AOBP), SPRINT reported that treating to a target systolic BP of < 120 mmHg significantly reduced cardiovascular endpoints compared to the conventional target of < 140 mmHg. However, it is not yet known if and how these findings using AOBP can be integrated into clinical practice. Accordingly, we sought to examine the relationship between achieved AOBP on antihypertensive therapy and cardiovascular endpoints in a large cohort of community-dwelling, older Canadians. Design and method: The relationship between systolic AOBP and cardiovascular endpoints over a mean 4.6 years of follow-up was examined in 6,183 subjects aged > 65 years who were receiving anti-hypertensive medication at baseline. Adjusted hazard ratios (HR) with 95% confidence intervals (CI) were computed for 10 mmHg categories of achieved AOBP recorded at baseline using Cox proportional hazards regression models, with the AOBP category having the lowest event rate designated as the reference category. Results: There were 904 fatal and non-fatal cardiovascular events with the lowest event rate in the 110–119 mmHg systolic AOBP category, which was significantly lower than the next highest category of 120–129 mmHg (HR 1.30; 1.01, 1.66). The HR for diastolic AOBP was relatively unchanged above 60 mmHg. There was a significant increase in the HR for pulse pressure at 80 mmHg (HR 133; 1.02, 1.72). Conclusions: Conclusions: These findings in a community-dwelling, older cohort of subjects extend the results of SPRINT to routine clinical practice and support an optimum systolic AOBP on antihypertensive therapy of 110–119 mmHg.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.252 | 0.072 |
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".