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Record W3033606062 · doi:10.1093/ndt/gfaa139.so053

SO053A REAL WORLD LONGITUDINAL ANALYSIS OF ANAEMIA TREATMENT PRESCRIPTIONS IN NON-DIALYSIS-DEPENDENT CHRONIC KIDNEY DISEASE PATIENTS, A CKDOPPS STUDY

2020· article· en· W3033606062 on OpenAlexaff
Marcelo Barreto Lopes, Charlotte Tu, Jarcy Zee, Bryce Foote, Murilo Guedes, Katarina Hedman, Glen James, Antônio Alberto Lopes, Ziad A. Massy, Helmut Reichel, Ronald L. Pisoni, James A. Sloand, Sandra Waechter, Michelle Wong, Bruce Robinson, Roberto Pecoits‐Filho

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney diseaseInternal medicineDiscontinuationDialysisMedical prescriptionNephrologyPopulationAnemiaIncidence (geometry)PediatricsPharmacology

Abstract

fetched live from OpenAlex

Abstract Background and Aims Previously lacking in the literature, this analysis aims to comprehensively describe longitudinal patterns of anaemia management, including prescriptions of ESA and iron replacement, for non-dialysis dependent chronic kidney disease (NDD-CKD) stage 3 to 5 patients under nephrologist care. Method We analysed data from a prospective cohort of 2455 NDD-CKD patients from Brazil, Germany and the US, who were not using anaemia medications (oral iron, intravenous [IV] iron, or erythropoiesis stimulating agent [ESA]) at enrolment in the Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDOPPS). We excluded 26% (N=862) of patients who were using any anaemia treatment from the source population at CKDOPPS study entry; we further excluded patients with (a) missing data for demographics and/or clinical history, or (b) no laboratory and medication data during follow-up. We reported the cumulative incidence (CI) of anaemia treatment initiation, stratified by biochemical parameters and patient characteristics. For patients that started therapy, we report the frequency of medication type at the moment of initiation, as well as switches and discontinuation over 12 months. Results The CI of any anaemia treatment initiation at 12 months was 18% for the whole sample, and 54% for patients with haemoglobin (Hb) <10 g/dL. For oral iron therapy, the CI at 12 months was 26% (19%, 32%) for TSAT<20%, and 22% (17%, 28%) for ferritin <100. For IV iron use, CI at 12 months was 6% (3%, 11%) for patients with TSAT<20% and 4% (2%, 7%) for patients with ferritin <100ng/mL. For ESA use, the CI at 12 months was 38% (29%, 47%) for patients with Hb <10 g/dL, and 11% (8%, 14%) for Hb 10 to <12 g/dL. Oral iron alone was the overwhelming first treatment option in the US (67%) and Brazil (56%); in Germany, a higher prevalence of ESAs (38%) and IV iron use (15%) was noted. Anaemia medication switches and discontinuation patterns, over 12 months, are outlined in the figure. The majority patients starting anaemia treatment were no longer on therapy one year later in Brazil (54%) and the US (51%); discontinuation of treatment was much lower in Germany (22%). Conclusion Anaemia treatment is initiated in a limited number of NDD-CKD patients with clinical signs that would indicate to do so, and many patients discontinue treatment, for reasons yet to be clarified. Although haemoglobin was the main factor associated with prescriptions, only about half of patients with Hb<10g/dL received any anaemia medication during a year. Oral iron was the treatment option most often prescribed, however given to only a quarter of iron deficient patients. We noticed country differences in the patterns of anaemia prescription and treatment discontinuation, over time, that could be due to regional policy and physician-led CKD anaemia management inequalities. These results provide longitudinal data supporting the concept that anaemia is sub-optimally managed among patients with NDD CKD in the real-world setting.

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 score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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
Published2020
Admission routes1
Has abstractyes

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