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Record W3168075403 · doi:10.1093/ndt/gfab101.002

MO680INTERNATIONAL COMPARISONS OF ICODEXTRIN PRESCRIPTION PRACTICE AND ITS ASSOCIATION WITH FLUID REMOVAL, BLOOD PRESSURE, PATIENT AND TECHNIQUE SURVIVAL*

2021· article· en· W3168075403 on OpenAlexaffabout
Simon Davies, Junhui Zhao, Keith McCullough, Yong-Lim Kim, Ronald L. Pisoni, Angela Yee‐Moon Wang, Rajnish Mehrotra, Talerngsak Kanjanabuch, Hideki Kawanishi, Bruce Robinson, Jeffrey Perl

Bibliographic record

VenueNephrology Dialysis Transplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineIcodextrinPeritoneal dialysisMedical prescriptionDialysisInternal medicineIntensive care medicineRenal functionSurgeryEmergency medicineUrologyPharmacology

Abstract

fetched live from OpenAlex

Abstract Background and Aims Icodextrin is designed to maintain ultrafiltration during the long dwell, especially when there is a risk of increased fluid reabsorption (fast peritoneal solute transfer rate, PSTR), without the need for excessive use of high glucose. Randomized trials have demonstrated these benefits but are insufficiently powered to investigate a clear impact on survival. We aimed to establish international prescription practices and their relationship to clinical outcomes. Method The Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) is an international prospective cohort study in collaboration with the International Society for Peritoneal Dialysis. The current analysis was drawn from A/NZ, Canada, Japan, UK, and US in PDOPPS phases 1-2 (2014-2019). Patient demographics, comorbidities, lab measurements, clinic blood pressure, membrane function (both solute transport rate and ultrafiltration capacity), dialysis prescription details, urine and 24-hour ultrafiltration volumes were captured at study enrollment. Mortality and permanent transfer to HD (HDT) events were collected during study follow-up [median (IQR) = 1.1 yrs (0.6, 1.7)]. Linear and logistic models were used to analyze the association between icodextrin and blood pressure. Cox regression, stratified by country, was used to analyze the association of icodextrin with time from study enrollment to (a) death and (b) HDT, and adjusted for demographics, 13 comorbidities, transplant waitlisting, serum albumin, urine volume, facility size and % APD use, study phase, while accounting for facility clustering. Results Icodextrin was prescribed in 1,929 (35%) of 5,432 patients studied, but this proportion differed by country, being >44% in all except the US, where it was 17%, and by facility within countries. Patients on icodextrin were more likely to have coronary artery disease and diabetes, have lower residual 24-hour urine volume and function, use less high glucose, have faster PSTR and reduced ultrafiltration capacity, and have been on PD longer (PD vintage: median 1.19, IQR 0.50-2.76). Despite this, patients using icodextrin achieved equivalent ultrafiltration to those using glucose at every level of residual urine volume (see figure). The low use of icodextrin in the US was more than compensated for by much greater use of high glucose and overall higher ultrafiltration volumes at each level of urine volume. Icodextrin use was not associated with blood pressure (effects: 0.90 mmHg, 95% CI: -0.68, 2.47), mortality (Hazard ratio [HR] 1.01, 95% CI: 0.83, 1.23) and HDT (HR 1.05, 95% CI: 0.90, 1.23). Conclusion There are important national and facility differences in the prescription of icodextrin, with the US a clear outlier, with less icodextrin and more high glucose use, resulting in higher ultrafiltration volumes. These practices and the targeting of patients with less efficient membranes for fluid removal may mask any potential survival advantage associated with icodextrin.

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)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.250
Teacher spread0.239 · 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 routes2
Has abstractyes

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