Abstract 19533: The Relationship Between Aortic-brachial Stiffness Mismatch and Mean Blood Pressure
Bibliographic record
Abstract
Background: Carotid-femoral pulse wave velocity (cf-PWV) is considered to be the gold standard for assessment of aortic stiffness. The impact of aortic stiffness on peripheral end-organ damage is explained by the loss or reversal of physiological impedance mismatch between aorta and medium-sized muscular conduit arteries. The main limitation of cf-PWV is its positive relationship with mean blood pressure. Recently, we have shown that assessment of aortic-brachial stiffness mismatch (PWV ratio) outperforms cf-PWV for the prediction of mortality in dialysis population. As both carotid-radial PWV (cr-PWV) and cf-PWV depend on blood pressure, we hypothesized that PWV ratio is not influenced by mean blood pressure. Method: In 310 dialysis patients (median age of 67 years [25 th -75 th percentiles: 56 - 76], 185(60%) men, 134 (43%) diabetes and 162 (52%) cardiovascular disease), cf-PWV and cr-PWV were measured using direct distance (Complior) and maximal upstroke algorithm. Mean blood pressure was obtained using arterial tonometry calibrated with brachial systolic and diastolic blood pressures in a controlled environment. In a linear regression analysis, we examined the relationship between MBP and cf-PWV, cr-PWV and PWV ratio. Results: The mean cf-PWV, cr-PWV, PWV ratio and MBP were respectively 13.52±4.06 m/s, 8.76±1.68 m/s, 1.59±0.52 and 92±16 mmHg. The linear relationships between MBP, cf-PWV and cr-PWV are shown in figures 1A-B. However, there were no relationship between MBP and PWV ratio (Figure 1C). There were no interaction between age, gender and the absence of a relationship between MBP and PWV ratio. Conclusion: As PWV ratio is mechanistically a logical parameter for explaining target organ damage and it has no significant relationship with MBP, it seems to be an ideal index for macro-circulatory disease. These findings need to be validated in independent cohorts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.006 | 0.001 |
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".