Peritoneal Dialysis–Related Infection Rates and Outcomes: Results From the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS)
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Peritoneal dialysis (PD)-related peritonitis carries high morbidity for PD patients. Understanding the characteristics and risk factors for peritonitis can guide regional development of prevention strategies. We describe peritonitis rates and the associations of selected facility practices with peritonitis risk among countries participating in the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS). STUDY DESIGN: Observational prospective cohort study. SETTING & PARTICIPANTS: 7,051 adult PD patients in 209 facilities across 7 countries (Australia, New Zealand, Canada, Japan, Thailand, United Kingdom, United States). EXPOSURES: Facility characteristics (census count, facility age, nurse to patient ratio) and selected facility practices (use of automated PD, use of icodextrin or biocompatible PD solutions, antibiotic prophylaxis strategies, duration of PD training). OUTCOMES: Peritonitis rate (by country, overall and variation across facilities), microbiology patterns. ANALYTICAL APPROACH: Poisson rate estimation, proportional rate models adjusted for selected patient case-mix variables. RESULTS: 2,272 peritonitis episodes were identified in 7,051 patients (crude rate, 0.28 episodes/patient-year). Facility peritonitis rates were variable within each country and exceeded 0.50/patient-year in 10% of facilities. Overall peritonitis rates, in episodes per patient-year, were 0.40 (95% CI, 0.36-0.46) in Thailand, 0.38 (95% CI, 0.32-0.46) in the United Kingdom, 0.35 (95% CI, 0.30-0.40) in Australia/New Zealand, 0.29 (95% CI, 0.26-0.32) in Canada, 0.27 (95% CI, 0.25-0.30) in Japan, and 0.26 (95% CI, 0.24-0.27) in the United States. The microbiology of peritonitis was similar across countries, except in Thailand, where Gram-negative infections and culture-negative peritonitis were more common. Facility size was positively associated with risk for peritonitis in Japan (rate ratio [RR] per 10 patients, 1.07; 95% CI, 1.04-1.09). Lower peritonitis risk was observed in facilities that had higher automated PD use (RR per 10 percentage points greater, 0.95; 95% CI, 0.91-1.00), facilities that used antibiotics at catheter insertion (RR, 0.83; 95% CI, 0.69-0.99), and facilities with PD training duration of 6 or more (vs <6) days (RR, 0.81; 95% CI, 0.68-0.96). Lower peritonitis risk was seen in facilities that used topical exit-site mupirocin or aminoglycoside ointment, but this association did not achieve conventional levels of statistical significance (RR, 0.79; 95% CI, 0.62-1.01). LIMITATIONS: Sampling variation, selection bias (rate estimates), and residual confounding (associations). CONCLUSIONS: Important international differences exist in the risk for peritonitis that may result from varied and potentially modifiable treatment practices. These findings may inform future guidelines in potentially setting lower maximally acceptable peritonitis rates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".