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Record W3170497997 · doi:10.1093/ndt/gfab141.003

FC 100ASSOCIATION OF SINGLE AND SERIAL MEASURES OF SERUM PHOSPHORUS WITH ADVERSE OUTCOMES IN PATIENTS ON PERITONEAL DIALYSIS: RESULTS FROM THE INTERNATIONAL PDOPPS

2021· article· en· W3170497997 on OpenAlexaboutno aff
Marcelo Barreto Lopes, Angelo Karaboyas, David W. Johnson, Talerngsak Kanjanabuch, Martin Wilkie, Kosaku Nitta, Hideki Kawanishi, Bryce Foote, Ronald L. Pisoni

Bibliographic record

VenueNephrology Dialysis Transplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMaceHemodialysisHyperphosphatemiaProportional hazards modelConfoundingPeritoneal dialysisInternal medicineDialysisMyocardial infarctionPhosphorusAnginaAdverse effectSurgeryGastroenterologyKidney diseasePercutaneous coronary intervention

Abstract

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Abstract Background and Aims While it has been established that high serum phosphorus is associated with mortality in hemodialysis (HD) patients, there is limited evidence in the peritoneal dialysis (PD) setting. We evaluated the association of serum phosphorus with mortality and major adverse cardiovascular events (MACE) in patients on PD, and investigated various parameterizations using single and serial measurements of serum phosphorus. Method We utilized data from 7 countries in phase 1 (2014-2017) of the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS): Australia, Canada, Japan, New Zealand, Thailand, the UK, and the US. We investigated the association of serum phosphorus and 3 outcomes: all-cause mortality, cardiovascular (CV) mortality, and MACE (CV mortality + non-fatal angina, myocardial infarction, stroke, and heart failure). We parameterized serum phosphorus using 4 different methods: (1) single measurement of baseline serum phosphorus [most recent measurement during 6-month run-in period]; (2) mean serum phosphorus over a 6-month run-in period; (3) number of months (over the past 6 months) with serum phosphorus above the target range (>4.5 mg/dL); (4) mean area-under-the-curve (AUC), calculated as the average amount of time spent with serum phosphorus >4.5 mg/dL multiplied by the extent to which this threshold was exceeded over 6 months. Cox regression was used to estimate the association between each of these 4 exposures with the time-to-event outcomes, in models thoroughly adjusted for possible confounders. Follow-up began after the 6-month run-in period and continued until the outcome occurred, 7 days after leaving the facility due to transfer or change in kidney replacement therapy modality, loss to follow-up, or end of study phase (whichever event occurred first). Results Our sample consisted of 5904 patients who were on PD. Those with higher serum phosphorus levels were younger and had lower hemoglobin levels. Compared to patients with serum phosphorus ≥3.5 to <4.5 mg/dL, we found an all-cause mortality hazard ratio (HR) of 1.62 (95% CI: 1.19, 2.20) for patients with serum phosphorus ≥ 7 mg/dL. Strong associations were also observed using serial phosphorus measures [Table]. For example, compared to the reference group of AUC=0, the HR (95% CI) of death was 1.49 (1.10, 2.00) for AUC >1 to 2; and 1.67 (1.15, 2.41) for AUC >2. Akaike Information Criteria (AIC) results showed that, among the 4 exposures, AUC was the strongest predictor of all-cause mortality, and the single phosphorus measure was the weakest predictor. Associations between serum phosphorus and adverse outcomes were generally stronger for CV death and MACE than for all-cause mortality [Table]. Conclusion As seen in HD patients, this analysis demonstrates that serum phosphorus is a strong predictor of adverse outcomes in patients on PD. When considering serial measurements of serum phosphorus, rates of adverse events began to rise at phosphorus levels >4.5 mg/dL. As recommended by KDIGO guidelines, serial measurements that consider a history of serum phosphorus excursions >4.5 mg/dL should be considered when assessing risks of adverse outcomes.

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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)0.004
metaresearch head score (Gemma)0.008
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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
Published2021
Admission routes1
Has abstractyes

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