Risk factors of peritoneal dialysis–related peritonitis in the Japan Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS)
Bibliographic record
Abstract
Background: Peritoneal dialysis (PD)-related peritonitis is a major complication of PD. Wide variations in peritonitis prevention, treatment strategies and consequences are seen between countries. These between-country differences may result from modifiable risk factors and clinical practices. Methods: A total of 1225 Japanese PD patients were included and prospectively followed in the Peritoneal Dialysis Outcomes and Practice Patterns Study phase 1 (2014-2018) and phase 2 (2018-2022). Associations between PD-related peritonitis and various risk factors were assessed by Cox proportional hazards survival models. Results: During follow-up (median 1.52 years), 539 peritonitis episodes were experienced by 364 patients. The country crude peritonitis rate was 0.27 episodes/patient-year. In the fully adjusted model, noticeable patient-level factors associated with experiencing any peritonitis included age {hazard ratio [HR] 1.07 per 5-year increase [95% confidence interval (CI) 1.01-1.14]}, serum albumin level [HR 0.63 per 1 g/dl higher (95% CI 0.48-0.82)] and continuous ambulatory peritoneal dialysis (PD) [HR 1.31 versus automated PD (95% CI 1.05-1.63)]. The adoption of antibiotic prophylaxis practice at the time of PD catheter insertion [HR 0.63 (95% CI 0.51-0.78)] or when having complicated dental procedures [HR 0.74 (95% CI 0.57-0.95)] or lower endoscopy [HR 0.69 (95% CI 0.54-0.89)] were associated with lower hazards of any peritonitis, while a routine facility practice of having more frequent regular medical visits was associated with a higher hazard. Conclusion: Identification of risk factors in Japan may be useful for developing future versions of guidelines and improving clinical practices in Japan. Investigation of country-level risk factors for PD-related peritonitis is useful for developing and implementing local peritonitis prevention and treatment strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".