The Impact of Repeated SBP Measurements in a Single Visit on Cardiovascular Prediction
Bibliographic record
Abstract
Background: Blood pressure (BP) is known for its intra-individual variability, even during a single clinic visit. Several guidelines recommend averaging BP measurements during a single office visit to monitor hypertension as it correlates more closely with ambulatory BP. However, whether these averages improve cardiovascular prediction has never been evaluated yet. Methods: We studied individuals aged between 40 and 69 from the CARTaGENE cohort (Canada). Three SBP measurements (SBP1,SBP2,SBP3) at two-minute intervals were taken with an Omron 907L device. These values were averaged to generate SBP1-2 (mean of SBP1 and SBP2), SBP2-3 (SBP2 and SBP3), and SBP1-2-3 (SBP1, SBP2 and SBP3). Major adverse atherosclerotic events (MACE: cardiovascular death, stroke, myocardial infarction) during a 10-year follow-up were obtained using medico-administrative databases. Associations of each SBP parameter with MACE were obtained using fully adjusted Cox models. Attributable risks were derived for each SBP parameter. Predictive performance was assessed with 10-year atherosclerotic cardiovascular disease scores (ASCVD; using pooled cohort equations) for each SBP parameter and associated C-statistics. Results: From 17,966 included individuals, 2,378 had a MACE during the follow-up. SBP values at baseline were 126.5 mmHg (SBP1), 123.2 (SBP2) and 122.5 (SBP3). After adjustment, SBP3 had the strongest association with MACE incidence. This association was significantly greater than that observed for SBP1, SBP1-2, or SBP1-2-3. In comparison to SBP1, SBP3 and SBP2-3 increased the absolute risk attributable to SBP by up to two times. When included in ASCVD scores, SBP3 yielded the highest C-statistic, which was significantly higher than all other SBP parameters except SBP2-3. Conclusions: Averaging repeated SBP measurements during a single office visit improves cardiovascular risk prediction compared to a single measurement. The first SBP value should nevertheless be discarded in order to maximize predictive performance. Funding: Government Support - Non-U.S.Table
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".