P40 Arteriovenous Fistula, Blood Pressure and Arterial Reservoir-wave Analysis: Lessons From End-stage Renal Disease
Bibliographic record
Abstract
Abstract Purpose/Background/Objectives According to reservoir-wave theory, excess pressure (XSP) is analogous to flow and related to excess cardiac workload. In end-stage renal disease (ESRD) patients, we have shown that XSPI is associated with increased mortality and is higher in patients with arteriovenous fistula (AVF). Recently AVF has been proposed treatment for treatment of resistant hypertension. However, this benefit may be mitigated through an increase in cardiac output. Therefore, we examine whether XSPI increases after creation of an AVF in ESRD patients. Methods Hemodynamic assessments were performed within 1 month before and 6 months after creation of AVF in ESRD patients. Carotid pressure waves were recorded using arterial tonometry, calibrated using brachial diastolic and mean arterial pressure. Using pressure only approach, reservoir-wave analysis was used to derive reservoir pressure (RP), XSP and their integrals (RPI, XSPI). Results 38 patients (63% male, mean age 59 ± 15 years) were assessed 3.9 ± 1.2 months Post-AVF. Carotid RP decreased slightly (115 ± 18 vs 109 ± 24, p = 0.060), due to the reduction of diastolic BP (79 ± 10 vs 73 ± 12 mm Hg, p = 0.003). While, carotid systolic BP (123 ± 20 vs 119 ± 27 mm Hg, p = 0.380) remained unchanged, XSP and XSPI increased (XSP: 14 [12–19] to 17 [12–22] mmHg, p = 0.031; XSPI: 275 [212–335] to 334 [241–349] kPa.s, p = 0.015). Conclusion While AVF creation reduced diastolic BP, it resulted in higher XSPI, which has been associated with increased mortality. Therefore, the long-term efficacy of AVF in reducing clinical outcomes should be specifically addressed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".