Infection prevention in home dialysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Among patients with kidney failure, home dialysis modalities, including peritoneal dialysis (PD) and home hemodialysis (HHD) provide several individual and healthcare system benefits over in-center hemodialysis (HD). Infection remains a major source of morbidity and mortality in this population, and a core outcome of critical importance to patients, caregivers, and kidney health professionals. This narrative review provides evidence-based measures for infection prevention among individuals receiving home dialysis, with a particular emphasis on dialysis and access-related infections. RECENT FINDINGS: Patient and care partner education and training is an important and major theme for infection prevention in home dialysis. In PD, identifying and managing modifiable risk factors for infections such as hypokalemia, constipation, use of gastric acid suppressants, and domestic pets, along with the use of antimicrobial prophylaxis, when indicated, can substantially reduce peritonitis risk. Reducing the use of central venous catheters (CVC), and duration of CVC dependence is the most effective means of prevention of HD access-related bloodstream infections in individuals receiving HHD. For arteriovenous fistula cannulation, rope ladder technique is associated with lower risk of infection compared to buttonhole cannulation. SUMMARY: Developing and instituting a well structured and evidence-based patient training and education program within home dialysis units is the most important measure in preventing and reducing dialysis and access-related infections. Kidney care providers should be familiar with different infection risk factors among individuals receiving home dialysis.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".