Association of Vascular Access Type with Inflammatory Marker Levels in Maintenance Hemodialysis Patients
Bibliographic record
Abstract
Aggressive NIH is a common histopathological lesion found at the sites of venous stenosis in arteriovenous fistula (AVF) and arteriovenous grafts (AVG). Inflammatory mediators have been proposed to play a pathogenic role in NIH, but there is paucity of data evaluating this hypothesis in clinical studies or in animal models. Serum levels of inflammatory mediators can potentially identify patients at high risk of AVF and AVG dysfunction. In a cross-sectional cohort study of 754 HD patients who were part of the NIED study cohort, we examined the associations between inflammatory markers including serum interleukin (IL) 1β, IL-6, C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α) and type of vascular access. Unadjusted and multivariate-adjusted linear regression models were used. In addition, time-dependent regression model was used to assess the association between inflammatory markers and mortality. We observed that in the multivariate-adjusted model, inflammatory mediators interleukin-6 (IL-6), interleukin-1L-ß (IL-1ß), and C-reactive protein (CRP), the predicted value in hemodialysis patients, are lowest in patients with AVF and highest in central venous catheter (CVC) and AVG even in case-mix and malnutrition-inflammation complex syndrome (MICS)-adjusted models. IL-6 and CRP levels fall consistently in the same patients when AVG or CVC is changed to AVF and increase if the same patient changes access from AVF to AVG or CVC. Obesity is a risk factor for fistula failure and fistulas are associated with the lowest mortality compared with CVC and AVG. We did not find any statistically significant association between tumor necrosis factor-α (TNF- α) and vascular access outcomes. Higher levels of inflammatory mediators seen in CVC and AVG compared with AVF could potentially explain the higher mortality seen in patients with CVC and AVG compared with AVF.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".