Variation in Peritoneal Dialysis Time on Therapy by Country
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Quantifying contemporary peritoneal dialysis time on therapy is important for patients and providers. We describe time on peritoneal dialysis in the context of outcomes of hemodialysis transfer, death, and kidney transplantation on the basis of the multinational, observational Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) from 2014 to 2017. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Among 218 randomly selected peritoneal dialysis facilities (7121 patients) in the PDOPPS from Australia/New Zealand, Canada, Japan, Thailand, the United Kingdom, and the United States, we calculated the cumulative incidence from peritoneal dialysis start to hemodialysis transfer, death, or kidney transplantation over 5 years and adjusted hazard ratios for patient and facility factors associated with death and hemodialysis transfer. RESULTS: Median time on peritoneal dialysis ranged from 1.7 (interquartile range, 0.8-2.9; the United Kingdom) to 3.2 (interquartile range, 1.5-6.0; Japan) years and was longer with lower kidney transplantation rates (range: 32% [the United Kingdom] to 2% [Japan and Thailand] over 3 years). Adjusted hemodialysis transfer risk was lowest in Thailand, but death risk was higher in Thailand and the United States compared with most countries. Infection was the leading cause of hemodialysis transfer, with higher hemodialysis transfer risks seen in patients having psychiatric disorder history or elevated body mass index. The proportion of patients with total weekly Kt/V ≥1.7 at a facility was not associated with death or hemodialysis transfer. CONCLUSIONS: Countries in the PDOPPS with higher rates of kidney transplantation tended to have shorter median times on peritoneal dialysis. Identification of infection as a leading cause of hemodialysis transfer and patient and facility factors associated with the risk of hemodialysis transfer can facilitate interventions to reduce these events. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_05_31_CJN16341221.mp3.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".