The Cost of Vascular Access Infections: Three Years Experience from a Single Outpatient Dialysis Center.
Bibliographic record
Abstract
Vascular access care accounts for a third of ESRD cost and is a leading cause of morbidity in hemodialysis (HD) patients. We designed this study to identify the risk factors for vascular access infection and to determine the cost of related hospitalizations. Methods: All HD patients at DCI Oakland from 1/1/99 to 6/30/02 were prospectively studied for access type, infections (Infx) including bacteremia, exit site, tunnel or cellulitis, and related hospitalizations. Cost data was obtained from the inpatient electronic medical record system. Risk factors for infection were evaluated using Poisson regression analysis, and cost comparison was done using ANOVA. Results: 153 patients were included in the analysis and were grouped by type of access: temporary catheter (TC) n = 84, permanent/tunneled catheter (PC) n = 158, AV graft (AVG) n = 87 and AV fistula (AVF) n = 57. Univariate analysis revealed significant risk factors to be female gender (p = 0.004, RR = 1.85) and type of access. In multivariate analysis, compared to AVF, risk of infection was highest with TC (p < 0.001, RR = 44), followed by PC (p < 0.001, RR = 17) and AVG (p = 0.03, RR = 3). Age, gender, and diabetes were not predictors of infection. The cost per hospitalization in the PC vs. AVG groups was not significantly different. Access type TC PC AVG AVF # Infx/1000 access days 5.0 3.8 0.6 0.1 % Infx requiring hospitalization 12.5 12.2 58.0 60.0 Cost/hosp. admission (mean) $16,896 25,683 9,016 5,650 Conclusions: Catheters are associated with much higher vascular access infection rates when compared to fistulas and grafts. When AVFs or AVGs do become infected, they are more likely to require hospitalization. Infections severe enough to require hospitalization result in similar inpatient cost per admission regardless of access type; however, this may be due to small sample size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".