Variation in the Approach to Antibiotic Administration for the Treatment of Peritoneal Dialysis-Associated Peritonitis: Results From a Survey of US Medical Directors Participating in the OPPUS Study
Bibliographic record
Abstract
Background: Peritoneal dialysis (PD)-associated peritonitis carries significant morbidity and is the leading cause of technique failure and transition to hemodialysis. This study aimed to explore the variation in antibiotic dosing and administration for the treatment of PD-associated peritonitis among a diverse group of PD facilities participating in the Optimizing Prevention of PD-associated Peritonitis in US (OPPUS) study. Methods: As part of the OPPUS study, an online peritonitis-focused survey was administered in quarter 1 of 2022 to medical directors at 40 PD study sites, representing independent, small, medium, and large sized dialysis organizations. Surveys to date were completed by 38 of these study sites. Results: Most centers (78%) provide patients with antibiotics for self-administration at home whenever peritonitis is suspected but to be taken during clinic off-hours. Clinics differ considerably regarding the types and numbers of intraperitoneal vs oral antibiotics prescribed for such self-administration. Antibiotics are routinely administered in one exchange/day in 95% of facilities; only 47% of facilities adjust dose for residual kidney function. Moreover, most centers (82%) indicated having no access to effluent cell count before initiation of antibiotics, with typically >12-hour turnaround time before effluent cell count results are available at 74% of PD units. Large inter-facility variability was seen as to when repeat PD effluent cell count(s) and culture(s) should be taken. In addition, only 62% of facilities routinely check vancomycin trough levels when intra-peritoneal vancomycin is prescribed. Conclusions: Prompt administration of antibiotics has been consistently shown to be associated with better outcomes of peritonitis treatment. Significant variations exist in antibiotic dosing and administration for PD-associated peritonitis across PD facilities in the US. It is notable that less than 20% of PD units routinely have access to PD effluent cell count results before treatment is initiated. Identifying optimal antibiotic dosing and administration practices that maximize the likelihood of cure is an important step to improve peritonitis outcomes and decrease related adverse events. Funding: Other NIH Support - AHRQ
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".