Comparative Risk of Vascular-Access Related Infections in Home vs. In-Center Hemodialysis
Bibliographic record
Abstract
Background: Home hemodialysis (HHD) is increasingly utilized in the U.S. due to its flexibility, cost-effectiveness, and clinical benefits. However, variability in training and ongoing reinforcement and adherence to aseptic technique, vascular access (VA) type, and cannulation method may influence infection risk. Methods: Adults (≥18 years) with fee-for-service claims in the U.S. Renal Data System (USRDS) from 2016 to 2020 were included. Infection types and events were identified through outpatient and inpatient claims using a 45-day gap rule to group related episodes. Poisson regression was used to calculate incidence rate ratios (IRRs) for infections in HHD vs. ICHD, stratified by VA type and adjusted for age, sex, and cause of kidney failure [TABLE (footnote)]. Results: Compared to ICHD patients, those on HHD were younger (mean age 56.6 vs. 63.5 years), more often male (60% vs. 56%), and less likely to have diabetes as a primary cause of kidney failure (34% vs. 47%). Adjusted infection rates were 1.2–2.3 times higher among HHD patients across all VA types and infection categories. [TABLE] Conclusion: Infection rates were higher in HHD than in ICHD, according to national USRDS data. These differences may be due to variation in cannulation practices, frequency of access use/cannulation, and adherence to aseptic technique, as well as reduced opportunities for early infection detection and intervention in the home setting. The findings highlight the need for standardized infection prevention protocols, enhanced training and continuous support tailored to the HHD environment. Funding: Commercial Support - Outset Medical
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".