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Record W3166752129 · doi:10.1093/ndt/gfab122.001

FC 067WHEN TO INITIATE DIALYSIS TO REDUCE MORTALITY AND CARDIOVASCULAR EVENTS IN ADVANCED CKD: A NATIONWIDE COHORT STUDY

2021· article· en· W3166752129 on OpenAlexaff
Edouard L. Fu, Marie Evans, Juan Jesús Carrero, Hein Putter, Catherine M. Clase, Fergus Caskey, Maciej Szymczak, Claudia Torino, Nicholas C Chesnaye, Kitty J. Jager, Christoph Wanner, Friedo W. Dekker, Merel van Diepen

Bibliographic record

VenueNephrology Dialysis Transplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineDialysisHazard ratioInternal medicineMaceRenal functionMyocardial infarctionCohortProportional hazards modelConfidence intervalIntensive care medicinePercutaneous coronary intervention

Abstract

fetched live from OpenAlex

Abstract Background and Aims There is currently no direct evidence to inform a specific glomerular filtration rate (GFR) to initiate maintenance dialysis. Previous studies are limited by the number of kidney function thresholds compared, immortal time or lead time biases, or small sample sizes. The only randomised trial (IDEAL) found no difference between early versus late start, but confidence intervals were wide. Method Nationwide observational cohort study using data from the Swedish Renal Registry between January 1, 2007 and December 31, 2016, with follow-up until June 1, 2017. Included individuals were receiving nephrologist care and had an eGFR between 10-20 ml/min/1.73m2. A randomized trial was emulated using the cloning, censoring and weighting method. Our primary analysis compared late (at an eGFR 5-7 ml/min/1.73m2 [eGFR5-7]), intermediate (eGFR7-10) and early (eGFR10-14) dialysis initiation to validate our analytical methods by comparison with IDEAL. Our secondary analysis compared fifteen dialysis initiation strategies with eGFR values ranging between 4 and 19 ml/min/1.73m2 in increments of 1 ml/min/1.73m2. Study outcomes were 5-year all-cause mortality and major adverse cardiovascular events (MACE; composite of cardiovascular death, non-fatal myocardial infarction and stroke). Adjusted hazard ratios [HR] and cumulative survival proportions were estimated using a dynamic marginal structural model. Results Among 10,290 individuals with advanced CKD (median age 73 years; 36% women; median eGFR 16.8 ml/min/1.73m2), 3725 individuals initiated dialysis, 4160 died and 2446 experienced MACE. In trial emulation, the 5-year mortality risk was 53.0% for eGFR5-7, 50.3% for eGFR7-10 and 49.7% for eGFR10-14. Compared with eGFR5-7, the 5-year absolute mortality risk difference was -2.7% (95% CI, -4.6% to -0.7%) for eGFR7-10 and -3.3% (95% CI, -5.2% to -1.3%) for eGFR10-14, with a HR of 0.97 (0.94-0.99) and 0.96 (0.94-0.99), respectively. The 5-year absolute MACE risk differences were -1.1% (95% CI, -3.8% to 2.1%) for eGFR7-10 and -3.6% (95% CI, -6.0% to -1.0%) for eGFR10-14 compared with eGFR5-7, with a HR of 1.00 (0.97-1.04) and 0.96 (0.97-1.00), respectively. When analysing fifteen eGFR thresholds, initiation at eGFR15-16 was associated with the largest reduction in mortality (absolute difference, -5.9% [95% CI, -8.0% to -3.1%]; HR, 0.88 [95% CI, 0.85-0.92]) and MACE (absolute difference, -4.5% [95% CI, -7.6% to -1.4%]; HR, 0.92 [95% CI, 0.89-0.97]), compared with eGFR4-5. This -5.9% absolute risk difference translates to a mean postponement of death of 1.8 months over 5-years of follow-up. However, dialysis would need to be initiated on average 14 months earlier. Conclusion Early dialysis initiation was associated with a modest reduction in mortality and cardiovascular events. Such a reduction may not outweigh the burden of longer dialysis treatment duration for the patient.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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
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