Nationwide Standardized Peritonitis Reporting: Preliminary Results from the Optimizing the Prevention of Peritoneal Dialysis-Associated Peritonitis in the United States (OPPUS) Study
Bibliographic record
Abstract
Background: Peritoneal Dialysis (PD) associated peritonitis is the leading cause of transfer to hemodialysis (HD) in the US. No formal mechanism or surveillance system exists for nationwide peritonitis reporting. Our primary aim was to develop a uniform widescale peritonitis reporting mechanism and evaluate its implementation via a one-year pilot study. Methods: Following literature review, stakeholder consultation, and ISPD guidelines review, a web-based peritonitis tracker tool (OPPUS-Link) was developed. Pilot sites for one-year data collection were selected based on geography and reported peritonitis rates, including 3 medium-large dialysis organizations. We provided formal training, central data review, and adjudication of all peritonitis episodes and outcomes. Results: Initial data for 31/64 participating facilities includes 86 peritonitis episodes (rate of 0.26 episodes/year [326 patient years of follow-up]). PD catheter removal and hospitalization occurred in 14% and 41% of episodes respectively (see table). Ongoing challenges include high rates of culture-negative peritonitis (24% overall) and data retrieval for peritonitis episodes occurring during hospitalization. Conclusions: Standardized, uniform peritonitis reporting is feasible, a first step in national PD-peritonitis surveillance, allowing for benchmarking, outbreak identification, and quality improvement initiative implementation. Further data validation is necessary and integrating routine peritonitis reporting in electronic health records with an overall goal of peritonitis reduction and improved outcomes for PD patients. Funding: Other NIH Support - AHRQ - Preliminary results for 31/64 clinics Measure/variable Total Total patient follow-up (patient-years) 325.8 Total peritonitis episodesreported*, no. 86 Peritonitis eplsode count (rate/patient-year) by organism type: Gram-positive 46 (0.14) Gram-negative 9 (0.03) Culture-negative 21 (0.06) Polymicrobial 6 (0.02) Polymicrobial 6 (0.02) Polymicrobial 6 (0.02) Yeast 3 (0.01) Unknown to clinic 1 (0.00) Peritonitisepisodes after catheter insertion, but prlor to or during PO training, no. 0 Patients with 1 periton it is episode, no. 72 Patients with 2 periton it is episodes, no. 7 Peritonitis rate, overall, event per patient-year 0.26 Peritonitis episodes associated with a hospitalization, no. (%) 35 (40.7%) Hospitalizations with pre-existing pentonitis, (%) 85.1% Peritonitis acquired in hospital [>24 hrs post-admission], (%) 14.9% Peritonitis episodes associated with death (with in 60 days), no. (%) 2 (2.3%) Peritonitis episodes in which PD catheter was removed, no. (%) 12 (14.0%) Peritonitis episodes associated with HD transfers, no. (%) 10 (11.6%) Permanent HD transfer (%) 9.3% Temporary HD transfer (%) 2.3% *Total peritonitis episodesreported, excluding relapse episodes.
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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 | 0.047 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".