Increased Excess Pressure After Creation of an Arteriovenous Fistula in End-Stage Renal Disease
Bibliographic record
Abstract
BACKGROUND: Reservoir-wave analysis (RWA) separates the arterial waveform into reservoir and excess pressure (XSP) components, where XSP is analogous to flow and related to left ventricular workload. RWA provides more detailed information about the arterial tree than traditional blood pressure (BP) parameters. In end-stage renal disease (ESRD), we have previously shown that XSP is associated with increased mortality and is higher in patients with arteriovenous fistula (AVF). In this study, we examined whether XSP increases after creation of an AVF in ESRD. METHODS: Before and after a mean of 3.9 ± 1.2 months following creation of AVF, carotid pressure waves were recorded using arterial tonometry. XSP and its integral (XSPI) were derived using RWA through pressure wave analysis alone. Aortic stiffness was assessed by carotid-femoral pulse wave velocity (CF-PWV). RESURLTS: In 38 patients (63% male, age 59 ± 15 years), after AVF creation, brachial diastolic BP decreased (79 ± 10 vs. 72 ± 12 mm Hg, P = 0.002), but the reduction in systolic BP, was not statistically significant (133 ± 20 vs. 127 ± 26 mm Hg, P = 0.137). However, carotid XSP (14 [12-19] to 17 [12-22] mm Hg, P = 0.031) and XSPI increased significantly (275 [212-335] to 334 [241-439] kPa∙s, P = 0.015), despite a reduction in CF-PWV (13 ± 3.6 vs. 12 ± 3.5 m/s, P = 0.025). CONCLUSIONS: Creation of an AVF resulted in increased XSP in this population, despite improvement in diastolic BP and aortic stiffness. These findings underline the complex hemodynamic impact of AVF on the cardiovascular system.
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 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 | 0.002 |
| 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) | 0.001 | 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".