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Record W3021750363 · doi:10.1093/ndt/gfy104.fo068

FO068KIDNEY FAILURE AFTER AKI AMONG PEOPLE UNDER NEPHROLOGY CLINIC CARE: A PROVINCEWIDE COHORT STUDY

2018· article· en· W3021750363 on OpenAlexaff
Simon Sawhney, Monica Beaulieu, Corri Black, Ognjenka Djurdjev, Gabriella Espino, Angharad Marks, David J. McLernon, Zainab Sheriff, Adeera Levin

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineNephrologyCohortInternal medicineIntensive care medicineCohort studyEmergency medicineFamily medicine

Abstract

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INTRODUCTION AND AIMS: Poor outcomes after acute kidney injury (AKI) have been well described for hospital cohorts, but not for those already receiving nephrology clinic care. Nevertheless, this is where discussions about future kidney failure risk commonly occur. We assessed whether AKI was independently associated with kidney failure and death (competing risk) in this setting. We then evaluated the widely used five year Kidney Failure Risk Equation (KFRE5yr), and developed refitted models with and without AKI as an additional predictor. METHODS: The study involved people receiving nephrology clinic care in British Columbia 2003-2009 (population 4.5 million), followed to 2016. Predictors were KDIGO AKI during the previous two years, age, sex, proteinuria, eGFR and renal diagnosis. Cox models estimated cause-specific hazard ratios (HR) (AKI vs no AKI) for kidney failure and death, stratified by eGFR (</≥30 ml/min/1.73m2). The published KFRE5yr was compared with refitted prediction models without and with AKI. A KFRE5yr risk threshold ≥3% has been suggested to triage entry into nephrology clinics. We used decision curve analysis to evaluate this threshold among people in clinics with eGFR ≥30. RESULTS: Of 7491 people, 995 had AKI and 6496 had no AKI in the previous two years. Those with AKI had increased subsequent kidney failure (33.1% vs 26.3%) but also increased death (23.8% vs 16.8%) (p-values <0.001), irrespective of age (figure 1), eGFR and renal diagnosis. After adjusting for confounders, AKI was still associated with kidney failure for those with eGFR ≥30 (HR 1.43 [1.13-1.81]), but no longer for those with eGFR <30 (HR 1.05 [0.91-1.21]) (table). For those with eGFR ≥30, KFRE5yr gave inferior predictions of 5 year kidney failure risk than refitted models without or with AKI (respective C statistics 0.701, 0.715 and 0.716). Using decision curve analysis, KFRE5yr at any risk threshold <10% was inferior to “treating all” patients in the clinic (figure 2). Refitted models were superior but AKI made no difference. CONCLUSIONS: AKI is associated with increased kidney failure among people under nephrology clinic care, but does not incrementally improve risk predictions because of a substantial competing risk of death. While KFRE5yr may help triage referrals to nephrology clinics, for people already in clinic with eGFR ≥30, it may not help guide safe discharge from follow-up. FO068 Table Independent association of AKI with subsequent kidney failure and death (cause-specific Cox models)

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 score0.702
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.291
Teacher spread0.281 · 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
Published2018
Admission routes1
Has abstractyes

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