4.7 PARAMETERS OF THE RESERVOIR-WAVE APPROACH AND MORTALITY IN DIALYSIS POPULATION
Bibliographic record
Abstract
Background: A new model has been proposed to explain hemodynamic consequences of arterial stiffness, which integrates both wave propagation and aortic reservoir function.The aim of this study was to assess the association between parameters of reservoir-wave analysis and all-cause mortality in a population with accelerated vascular ageing.Methods: Among 311 patients with chronic kidney disease on dialysis, central arterial pressures were derived from applanation tonometry (Sphygmocor) of radial artery.Reservoir wave analysis was applied on radial pressure waveforms (without generalized transfer function) to obtain reservoir pressure (Peak RP), its integral (RP integral), excess pressure parameters (Peak XS, XS integral), and systolic (SC) and diastolic time constant (DC).Results: During a median follow-up of 33 months, 204 (66%) deaths occurred.In KaplaneMeier survival curves, only increasing tertiles of DC was associated with a significant decrease in survival time (p < 0.001).Amongst all parameters, only DC and XS integral were predictors of allcause mortality in univariate Cox analysis as shown by hazard ratios for changes in 1-standardized deviation (HR 1-SD, Table 1).However, DC and XS integral were no longer significant when age was introduced in the model (p-value > 0.179).Conclusions: Amongst all parameters of the reservoir-wave analysis, DC was the most important parameter associated with survival time and mortality.Despite its hypothetically more integrated approach to arterial tree function, none of the derived parameters showed a robust and independent association with mortality in this population.The study shows that despite its simplicity, arterial stiffness gradient remains the best predictor of mortality in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".