A relationship of intradialytic blood pressure variability with vascular access outcomes in patients on hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Vascular access dysfunction is a major cause of morbidity in patients with end-stage renal disease (ESRD) on hemodialysis (HD). Thus, identifying risk factors for vascular access failure is important. Patients on HD are routinely exposed to high blood pressure variability (BPV) during HD. However, the impact of intradialytic BPV on vascular access outcomes is unknown. Therefore, we investigated the association of intradialytic BPV with vascular access outcomes in patients on HD. METHODS: One hundred and thirty patients with ESRD who created vascular access for HD were evaluated. We examined 12 dialysis sessions per patient and recorded BP five times for each session. BPV was assessed using residual standard deviation derived from the linear regression model. The patients were divided into two groups according to a level below or above the median value of intradialytic BPV and compared. The primary outcome was primary unassisted vascular access patency. FINDINGS: The median time to loss of primary unassisted patency was significantly longer in low intradialytic BPV group than in high intradialytic BPV group (52 months vs. 21 months, P < 0.001) during the mean follow-up of 3.7 years. After adjustment for other variables, high intradialytic BPV was significantly associated with loss of primary unassisted vascular access patency (hazard ratio, 2.605; 95% confidence interval, 1.462-4.643; P = 0.001). DISCUSSION: Our study revealed a significant correlation between intradialytic BPV and vascular access patency. Further studies are needed to identify methods for lowering BPV.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".