Prognostic Value of Carotid and Radial Artery Reservoir‐Wave Parameters in End‐Stage Renal Disease
Bibliographic record
Abstract
Background Reservoir-wave approach is an alternative model of arterial hemodynamics based on the assumption that measured arterial pressure is composed of volume-related (reservoir pressure) and wave-related components (excess pressure). However, the clinical utility of reservoir-wave approach remains debatable. Methods and Results In a single-center cohort of 260 dialysis patients, we examined whether carotid and radial reservoir-wave parameters were associated with all-cause and cardiovascular mortality. Central pulse pressure and augmentation index at 75 beats per minute were determined by radial arterial tonometry through generalized transfer function. Carotid and radial reservoir-wave analysis were performed to determine reservoir pressure and excess pressure integral. After a median follow-up of 32 months, 171 (66%) deaths and 88 (34%) cardiovascular deaths occurred. In Cox regression analysis, carotid excess pressure integral was associated with a hazard ratio of 1.33 (95% CI , 1.14-1.54; P<0.001 per 1 SD) for all-cause and 1.45 (95% CI : 1.18-1.75; P<0.001 per 1 SD) for cardiovascular mortality. After adjustments for age, heart rate, sex, clinical characteristics and carotid-femoral pulse wave velocity, carotid excess pressure integral was consistently associated with increased risk of all-cause (hazard ratio per 1 SD, 1.30; 95% CI : 1.08-1.54; P=0.004) and cardiovascular mortality (hazard ratio per 1 SD, 1.31; 95% CI : 1.04-1.63; P=0.019). Conversely, there were no significant associations between radial reservoir-wave parameters, central pulse pressure, augmentation index at 75 beats per minute, pressure forward, pressure backward and reflection magnitude, and all-cause or cardiovascular mortality after adjustment for comorbidities. Conclusions These observations support the clinical value of reservoir-wave approach parameters of large central elastic vessels in end-stage renal disease.
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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 | 0.005 |
| 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 | 0.001 | 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".