Impact of vascular access intervention therapy on cardiac load in hemodialysis patients
Bibliographic record
Abstract
Vascular access intervention therapy (VAIVT) has been positioned as the first choice of treatment for stenosis lesions frequently observed in arteriovenous fistula (AVF) for hemodialysis patients in Japan. Furthermore, increased blood flow can provide a stable dialysis. In contrast, it has been reported that excess blood flow of AVF causes high-output heart failure. Although VAIVT is used to increase blood flow of AVF, the impact of VAIVT on cardiac load has been rarely reported. We examined the factors associated with cardiac load in hemodialysis patients undergoing VAIVT by measuring levels of α human atrial natriuretic polypeptide (hANP) and brain natriuretic peptide (BNP) before and after VAIVT. Data were extracted on hemodialysis patients who underwent measurements of αhANP and BNP in before and after VAIVT at our facility and related facilities between February 2014 and December 2014. Nineteeen patients (median age, 73.0 [66.5-80.5] years; male, 52.6%; 36.8% with diabetes; median duration of dialysis treatment, 50.0 [21-109] months) were enrolled in this study. Flow volume of AVF was higher after VAIVT than that before VAIVT (442.0 vs. 758.0 mL/minute, P < 0.001). Moreover, resistance index (RI) of AVF after VAIVT was lower than that before VAIVT (0.61 vs. 0.53, P < 0.01). Although αhANP did not change before and after VAIVT (55.6 vs. 54.9 pg/mL, P = 0.099), BNP after VAIVT was significantly higher than that before VAIVT (145.2 vs. 175.0 pg/mL, P < 0.05). Factors correlated with the increase in BNP were flow volume of AVF before VAIVT (r = -0.458, P = 0.049) and levels of BNP before VAIVT (r = 0.472, P = 0.041). There was no significant correlation between the increase in αhANP with flow volume of AVF before VAIVT, levels of αhANP before VAIVT. Patients with high levels of BNP and low flow volume of AVF before VAIVT were considered to have a high risk of developing heart failure after VAIVT.
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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 | 0.003 |
| 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".