Association of cardiovascular events with central systolic blood pressure: A systemic review and meta‐analysis
Bibliographic record
Abstract
Central blood pressure confers cardiovascular risk prediction ability, but whether the association between central systolic blood pressure (cSBP) and cardiovascular endpoints is independent of peripheral systolic blood pressure (pSBP) remains controversial. This systematic review and meta-analysis aim to investigate the associations between cSBP and cardiovascular endpoints in models including and excluding pSBP, respectively. Observational studies assessing the risk of composite cardiovascular endpoints with baseline cSBP were searched in PubMed, Embase, Scopus, Web of Science, and Cochrane Library to May 31, 2022. Risk of bias was assessed by the Newcastle-Ottawa Quality Assessment Scale, and random-effects models were used to pool estimates. Finally, 48 200 participants from 19 studies with a mean age of 59.0 ± 6.9 years were included. Per 10 mmHg increase of cSBP was associated with higher risk of composite cardiovascular outcomes (risk ratio [RR]: 1.14 [95%CI 1.08-1.19]) and cardiovascular death (RR: 1.18 [95%CI 1.08-1.30]), and the associations still existed after adjusting for pSBP (RR: 1.13 [95%CI 1.05-1.21] for composite cardiovascular endpoints; RR: 1.25 [95%CI 1.09-1.43] for cardiovascular death). In pSBP-unadjusted studies, increased cSBP was also associated with higher risk of all-cause mortality and stroke, but not in the pSBP-adjusted studies. Both cSBP and pSBP were similarly significantly associated with composite cardiovascular endpoints in models containing them separately and simultaneously. cSBP was significantly associated with cardiovascular events, independently of pSBP. Central or peripheral SBP could supplement cardiovascular risk assessment besides each other.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".