Identifying Peritoneal Dialysis (PD)-Associated Peritonitis Using Medicare Claims
Bibliographic record
Abstract
Background: Medicare fee-for-service (FFS) claims offer a population-based approach to PD-associated peritonitis that may offer valuable insights into predictors, trends and preferred practices. Methods: We used United States Renal Data System (USRDS) standard analysis files for claims (inpatient, outpatient and physician-supplier), eligibility, modality and demographic information. The sample consisted of PD patient-months from 2013 through 2017 characterized by Medicare FFS coverage and paid claims for dialysis or hospital services. We identified ICD-9 and ICD-10 diagnosis codes for peritonitis, including those that do not clearly distinguish peritonitis from catheter infections/inflammation (“catheter codes”). A new peritonitis episode was defined as a peritonitis claim 30+ days from any prior peritonitis claim or 50+ days from the initial peritonitis claim for a prior episode. Results: The sample included 88,396 adult patients (128,000 observed patient-years), yielding 510,000 peritonitis claims and 75,000 peritonitis episodes. Coding was heterogeneous with no single diagnosis code present on the majority of claims. Peritonitis episodes were inferred from aggregated claims (mean 6.3, median 2). Half of episodes were exclusively outpatient, 7% exclusively inpatient, and 16% exclusively comprised of catheter code claims. The overall peritonitis rate was 0.59 and 0.49 episodes per patientyear with and without inclusion of catheter codes respectively. Peritonitis rates declined by 4%/year from 2013-2017, and varied by age, race (Black > White >Asian), and ESKD vintage. Conclusions: Coding heterogeneity indicates a lack of standardization and need for clearer coding guidance. We found differences between races, ages, and patient vintages, and declining rates from 2013-2017. These rates are 2-fold higher than reported in US-PDOPPS by Perl et al (AJKD 2020) which is not restricted to Medicare. Claims are an important data source for peritonitis, but more work is needed to validate these rates. Funding: Other NIH Support - Agency for Healthcare Research and Quality
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".