A real-world longitudinal study of anemia management in non-dialysis-dependent chronic kidney disease patients: a multinational analysis of CKDopps
Bibliographic record
Abstract
Previously lacking in the literature, we describe longitudinal patterns of anemia prescriptions for non-dialysis-dependent chronic kidney disease (NDD-CKD) patients under nephrologist care. We analyzed data from 2818 Stage 3-5 NDD-CKD patients from Brazil, Germany, and the US, naïve to anemia medications (oral iron, intravenous [IV] iron, or erythropoiesis stimulating agent [ESA]) at enrollment in the CKDopps. We report the cumulative incidence function (CIF) of medication initiation stratified by baseline characteristics. Even in patients with hemoglobin (Hb) < 10 g/dL, the CIF at 12 months for any anemia medication was 40%, and 28% for ESAs. Patients with TSAT < 20% had a CIF of 26% and 6% for oral and IV iron, respectively. Heart failure was associated with earlier initiation of anemia medications. IV iron was prescribed to < 10% of patients with iron deficiency. Only 40% of patients with Hb < 10 g/dL received any anemia medication within a year. Discontinuation of anemia treatment was very common. Anemia treatment is initiated in a limited number of NDD-CKD patients, even in those with guideline-based indications to treat. Hemoglobin trajectory and a history of heart failure appear to guide treatment start. These results support the concept that anemia is sub-optimally managed among NDD-CKD patients 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".