Prediction of Cardiovascular Events by Type I Central Systolic Blood Pressure
Bibliographic record
Abstract
Compared with brachial blood pressure (BP), central systolic BP (SBP) can provide a better indication of the hemodynamic strain inflicted on target organs, but it is unclear whether this translates into improved cardiovascular risk stratification. We aimed to assess which of central or brachial BP best predicts cardiovascular risk and to identify the central SBP threshold associated with increased risk of future cardiovascular events. This study included 13 461 participants of CARTaGENE with available central BP and follow-up data from administrative databases but without cardiovascular disease or antihypertensive medication. Central BP was estimated by radial artery tonometry, calibrated for brachial SBP and diastolic BP (type I), and a generalized transfer function (SphygmoCor). The outcome was major adverse cardiovascular events. Cox proportional-hazards models, differences in areas under the curves, net reclassification indices, and integrated discrimination indices were calculated. Youden index was used to identify SBP thresholds. Over a median follow-up of 8.75 years, 1327 major adverse cardiovascular events occurred. The differences in areas under the curves, net reclassification indices, and integrated discrimination indices were of 0.2% ([95% CI, 0.1–0.3] P <0.01), 0.11 ([95% CI, 0.03–0.20] P =0.01), and 0.0004 ([95% CI, −0.0001 to 0.0014] P =0.3), all likely not clinically significant. Central and brachial SBPs of 112 mm Hg (95% CI, 111.2–114.1) and 121 mm Hg (95% CI, 120.2–121.9) were identified as optimal BP thresholds. In conclusion, central BP measured with a type I device is statistically but likely not clinically superior to brachial BP in a general population without prior cardiovascular disease. Based on the risk of major adverse cardiovascular events, the optimal type I central SBP appears to be 112 mm Hg.
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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 | 0.001 | 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".