Determinants of Increased Central Excess Pressure in Dialysis: Role of Dialysis Modality and Arteriovenous Fistula
Bibliographic record
Abstract
BACKGROUND: Arterial reservoir-wave analysis (RWA)-a new model of arterial hemodynamics-separates arterial wave into reservoir pressure (RP) and excess pressure (XSP). The XSP integral (XSPI) has been associated with increased risk of clinical outcomes. The objectives of the present study were to examine the determinants of XSPI in a mixed cohort of hemodialysis (HD) and peritoneal dialysis (PD) patients, to examine whether dialysis modality and the presence of an arteriovenous fistula (AVF) are associated with increased XSPI. METHOD: In a cross-sectional study, 290 subjects (232 HD and 130 with AVF) underwent carotid artery tonometry (calibrated with brachial diastolic and mean blood pressure). The XSPI was calculated through RWA using pressure-only algorithms. Logistic regression was used for determinants of XSPI above median. Through forward conditional linear regression, we examined whether treatment by HD or the presence of AVF is associated with higher XSPI. RESULTS: Patients with XSPI above median were older, had a higher prevalence of diabetes and cardiovascular disease, had a higher body mass index, and were more likely to be on HD. After adjustment for confounders, HD was associated with a higher risk of higher XSPI (odds ratio = 2.39, 95% confidence interval: 1.16-4.98). In a forward conditional linear regression analysis, HD was associated with higher XSPI (standardized coefficient: 0.126, P = 0.012), but on incorporation of AVF into the model, AVF was associated with higher XSPI (standardized coefficient: 0.130, P = 0.008) and HD was excluded as a predictor. CONCLUSION: This study suggests that higher XSPI in HD patients is related to the presence of AVF.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".