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Record W4403292978 · doi:10.34067/kid.0000000596

Impact of the Preservation of Residual Kidney Function on Hemodialysis Survival

2024· article· en· W4403292978 on OpenAlexaff
John Belcher, David Coyle, Elizabeth Lindley, David Keane, Fergus Caskey, Indranil Dasgupta, Andrew Davenport, Ken Farrington, Sandip Mitra, Paula Ormandy, Martin Wilkie, Jamie Macdonald, Ivonne Solis‐Trapala, Julius Sim, Simon Davies

Bibliographic record

VenueKidney360 · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsHealth Sciences Centre
FundersHealth Technology Assessment ProgrammeKeele UniversityScience Foundation IrelandSheffield Teaching Hospitals NHS Foundation TrustUniversity of NottinghamUniversity of OxfordNorth Bristol NHS TrustKing's College LondonImperial College LondonImperial College Healthcare NHS TrustNational Institute for Health and Care ResearchBarts Health NHS TrustRoyal Free London NHS Foundation TrustNHS Greater Glasgow and ClydeNottingham University Hospitals NHS TrustUniversity Hospitals of Leicester NHS TrustUniversity Hospitals Birmingham NHS Foundation TrustKing's College Hospital NHS Foundation Trust
KeywordsDialysisResidualMedicineRenal functionHemodialysisIntensive care medicineInternal medicineComputer science

Abstract

fetched live from OpenAlex

Key Points Residual kidney function during the first 2 years of hemodialysis treatment is associated with a long-term (>4 years) survival advantage. Incorporating bioimpedance measurements to inform the setting of the postdialysis target weight does not affect patient survival. Background Preservation of residual kidney function (RKF) in dialysis patients has been associated with improved survival. RKF in the BISTRO trial was relatively well preserved, and in this study, we describe its association with survival during the trial and extended follow-up. Methods RKF, measured as the average urea and creatinine clearance (GFR) or 24-hour urine volume, was assessed at baseline; 1, 2, and 3 months; and every three months for up to 2 years in incident hemodialysis patients. Time to event survival data or competing events (transplantation, modality change) was obtained for 50 months after enrollment via data linkage with the UK Renal Registry. Cox proportional hazards regression survival models, including those incorporating change in GFR from baseline as a time-varying variable and joint regression models for longitudinal and survival data (longitudinal models for GFR or urine volume), were used to explore the relationship of RKF preservation with survival. Analyses were adjusted for age, sex, comorbidity, and ethnicity. Results A total of 2919 measures of RKF were made in 387 patients from 32 UK dialysis units. Higher age and comorbidity score were associated with increased mortality in all models. Baseline GFR reduced the risk of death (hazard ratio [HR], 0.918; 95% confidence interval [CI], 0.844 to 0.999) per ml/min per 1.73 m 2 . A greater fall in GFR and urine volume from baseline was associated with a nonsignificant increased risk of death, as visualized on spline plots. In the joint survival models, higher GFR (adjusted HR, 0.88; 95% CI, 0.80 to 0.97) or urine volume (adjusted HR, 0.75, 95% CI, 0.57 to 0.95/L) at any time point was associated with better survival. Conclusions Lower RKF during the first 2 years of hemodialysis is associated with an increased death risk for up to 50 months after dialysis initiation. This adds to a growing body of evidence that interventions to preserve RKF should be developed and tested in clinical trials.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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