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Record W4389080368 · doi:10.34067/kid.0000000000000325

Gastric Acid Suppression Therapy and Its Association with Peritoneal Dialysis–Associated Peritonitis in the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS)

2023· article· en· W4389080368 on OpenAlexafffund
Shira Goldman, Junhui Zhao, Brian Bieber, Ronald L. Pisoni, Laura Horowitz, Sharon J. Nessim, Beth Piraino, Mark Lambie, Talerngsak Kanjanabuch, Yasuhiko Ito, Neil Boudville, Isaac Teitelbaum, Martin A. Schreiber, Jeffrey Perl

Bibliographic record

VenueKidney360 · 2023
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health CentreSt. Michael's Hospital
FundersOtsuka PharmaceuticalAzrieli FoundationNational Research Council of ThailandKyowa Hakko KirinBayer CanadaAgency for Healthcare Research and QualityU.S. Department of Health and Human ServicesKidney Research UKFresenius Medical Care North AmericaChugai PharmaceuticalAmgen
KeywordsPeritoneal dialysisPeritonitisMedicineHazard ratioInternal medicineProportional hazards modelDialysisGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

Key Points In a large multinational cohort of PD patients, any GAS use was not associated with an increased risk of all-organism peritonitis. For peritonitis, risks were particularly high among certain classes of organisms particularly for Gram-negative, enteric, and streptococcal peritonitis episodes. The association with enteric peritonitis appeared to be stronger among H2RA users. Background Peritonitis is a major peritoneal dialysis–related complication. We determined whether gastric acid suppression (GAS) (proton pump inhibitor [PPI] or histamine-2 receptor antagonists [H2RAs]) use was associated with all-cause and organism-specific peritonitis in peritoneal dialysis patients. Methods In the Peritoneal Dialysis Outcomes and Practice Patterns Study (595 facilities, eight countries, years 2014–2022), associations between GAS use and time to first episode of all-cause peritonitis were examined using Cox proportional hazards models. The primary exposure of interest was GAS and secondarily PPI or H2RA use. Secondary outcomes were organism-specific peritonitis, peritonitis cure rates, and death. Results Among patients ( N =23,797) at study baseline, 6020 (25.3%) used PPIs, and 1382 (5.8%) used H2RAs. Overall risks of GAS use and peritonitis risk (adjusted hazard ratio [AHR]=1.05, 95% confidence interval [CI], 0.98 to 1.13]) and use of PPI (AHR 1.06 [95% CI, 0.99 to 1.14]) or H2RA (AHR 1.02 [95% CI, 0.88 to 1.18]) did not reach statistical significance. In organism-specific analyses, GAS users displayed higher peritonitis risks for Gram-negative (AHR 1.29, 95% CI, 1.05 to 1.57), Gram-positive (AHR 1.15, 95% CI, 1.01 to 1.31), culture-negative (AHR 1.20, 95% CI, 1.01 to 1.42), enteric (AHR 1.23, 95% CI, 1.03 to 1.48), and particularly Streptococcal (AHR 1.47, 95% CI, 1.15 to 1.89) peritonitis episodes. GAS was also associated with higher overall mortality (AHR 1.13 [95% CI, 1.05 to 1.22]). Conclusion The association between GAS use and peritonitis risk was weaker (hazard ratio [HR] 1.05 [0.98 to 1.13]) than for streptococcal (HR 1.57 [1.15 to 1.89]) and Gram-negative (HR 1.29 [1.05 to 1.57]) peritonitis. A better understanding of mechanisms surrounding the differential effects of GAS subtype on peritonitis risks is needed. Clinicians should be cautious when prescribing GAS. The impact of GAS deprescribing on peritonitis risk requires further evaluation.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.309
Teacher spread0.283 · 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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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