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Record W4415424671 · doi:10.1016/j.ekir.2026.106664

#1917 Peritoneal dialysis modality and outcomes in the peritoneal dialysis outcomes and practice patterns study

2025· article· en· W4415424671 on OpenAlexaffabout
Thyago Proença de Moraes, Charlotte Tu, Brian Bieber, R. Pisoni, Talerngsak Kanjanabuch, Tadashi Tomo, Fiona G. Brown, Simon Davies, Jeffrey Perl, Yong-Lim Kim

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsPeritoneal dialysisContinuous ambulatory peritoneal dialysisIcodextrinHemodialysisDialysisCohortPeritonitisComorbidity

Abstract

fetched live from OpenAlex

Abstract Background and Aims Automated peritoneal dialysis (APD) is the most common peritoneal dialysis (PD) modality in many high-income countries, with increasing adoption in low/middle-income countries. Whether or not differences exist in outcomes between PD modality (APD vs. continuous ambulatory PD [CAPD]) remains controversial. A better understanding of this relationship could inform further APD uptake globally. We evaluated patient survival, permanent hemodialysis transfer (HDT), and peritonitis by PD modality in a contemporary cohort of patients across facilities and countries with significant variation in APD use. Method Using the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), we identified patients prescribed either CAPD vs. APD with a dialysis vintage of 4 months or longer at study entry in Australia, Canada, Japan, New Zealand, South Korea, United Kingdom, and the United States. Thailand was excluded due to very low APD use. Cox models estimated the association between PD modality and outcomes, including patient survival, HDT, and time to first peritonitis, adjusting for case mix and facility-level factors. Subgroup analyses were prespecified. Additional analyses explored APD as the proportion APD use at the facility level with outcomes, to reduce treatment by indication bias. Results Among 17,591 included patients, 14,343 (82%) were on APD. APD use ranged from 37% in South Korea to 90% in the United States (Fig. 1A). APD patients had similar comorbidity profile but were younger, had lower urine volume, were more likely to use hypertonic glucose, and less likely to use icodextrin compared to CAPD patients. The hazard ratio (HR) for patient survival for APD vs. CAPD was 0.93 (95% CI 0.81–1.06) overall (Fig. 1B). Country-specific mortality results varied, with HR 1.08 (95% CI 0.89–1.28) in the US and HR 0.55 (95% CI 0.34–1.03) in Japan. In a subgroup analysis, APD was associated with better survival in patients with icodextrin use (HR 0.78, 95% CI 0.61–1.00). Risk of HDT was marginally higher among APD patients (HR 1.07, 95% CI 0.97–1.18), despite a lower risk of peritonitis (HR 0.87, 95% CI 0.78–0.98). In Japan and South Korea where APD use was less common, approximately one quarter of facilities reported use in fewer than 20% of patients), lower facility proportion APD use was associated with higher mortality. Conclusion Patients on APD had lower rates of peritonitis. Patient survival did not differ significantly by PD modality in the PDOPPS. However, APD patients had a marginally higher risk of HDT, possibly reflecting its use as a “rescue therapy.” Greater icodextrin use in APD patients may improve survival. Further prospective evaluations are required to explore patient reported outcome differences across PD modalities, to identify specific patients who could benefit from initial APD use or switch from CAPD to APD and to explore the impact of increasing APD use on clinical outcomes across low- and middle-income countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.015
GPT teacher head0.341
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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