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Record W2804289427 · doi:10.1093/ndt/gfy104.fp377

FP377NOVEL RISK-BASED THRESHOLDS FOR BONE MINERAL BIOMARKERS IN ADVANCED CKD

2018· article· en· W2804289427 on OpenAlexaffabout
Mark Canney, Ognjenka Djurdjev, Mila Tang, Claudia Zierold, Frank Blocki, Fabrizio Bonelli, Myles Wolf, Adeera Levin

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsSt. Paul's HospitalBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsMedicineBone mineralInternal medicineIntensive care medicineOsteoporosis

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Current laboratory reference ranges for bone mineral biomarkers (BMB) are drawn from normal population values, and have limited utility in advanced CKD. Current guidelines offer little to facilitate the interpretation of BMB results, which may contribute to therapeutic nihilism. We aimed to describe expected values of parathyroid hormone (PTH), fibroblast growth factor 23 (FGF23), 1,25-dihydroxyvitamin D (1,25D3), and 25-hydroxyvitamin D3 (25D3) with decreasing eGFR, and to establish risk-based thresholds for each biomarker within specific eGFR intervals. METHODS: Using data from a prospective cohort study of 1812 patient with advanced CKD in Canada under the care of nephrologists between 2008-2013, we measured intact PTH, FGF23, 1,25D3, and 25D3 in a central laboratory using sensitive DiaSorin assays. Adjudicated cardiovascular (ischaemic heart disease, congestive heart failure, stroke and sudden cardiac death) and renal outcomes (40% decrease in eGFR or initiation of renal replacement therapy) were recorded over 5 years of follow-up. We describe the expected distribution of BMBs as a function of eGFR, and determine risk-based thresholds by eGFR level using a robust computational methodology (Contal and O'Quigle). RESULTS: The mean age was 68.9, 62% were male and 45% were diabetic. The mean eGFR was 28.9 ±10 ml/min per 1.73m2 with 19.4%, 40.3% and 40.3% with eGFR <20, 20-29 and >30 ml/min per 1.73m2 respectively. The median follow up of the cohort was 52 months. Within each category of eGFR, there were statistically significant differences in PTH, FGF23, 25D3 and 1,25D3 levels (see Table). For each of the BMBs, a high proportion of the cohort had values outside the laboratory reference range, and these proportions were higher at lower levels of eGFR. Risk-based thresholds differed by eGFR level and identified significantly different proportions of patients at risk. For example, advanced CKD patients with PTH above the laboratory reference range, but below the risk-based threshold, did not have significantly higher risk of cardiovascular events. However, patients with PTH above the risk-based threshold had significantly higher risk of events compared with patients above the laboratory reference range but below the risk-based threshold (eGFR 20-30 ml/min: HR=1.78, 95% CI: 1.17-2.72; eGFR < 20ml/min: HR=1.86, 95% CI: 1.19-2.89). CONCLUSIONS: The majority of patients with advanced CKD have values of BMB that are outside the laboratory reference range. Furthermore, the distributions of BMB vary at different levels of eGFR. We propose that employing risk-based thresholds of BMB may serve to inform clinicians of ‘expected values’ of BMB within eGFR ranges, and eGFR-specific values that confer increased risk of hard outcomes. Further studies are needed to validate these findings, and to determine the clinical utility of this novel approach.

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)0.006
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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 routes2
Has abstractyes

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