Anemia Treatment Among Prevalent Hemodialysis and Peritoneal Dialysis Patients in the United States
Bibliographic record
Abstract
Background: This study aims to assess anemia treatment in chronic kidney disease (CKD) patients (pts) undergoing dialysis to understand the evolving landscape of CKD anemia management. Methods: Using USRDS data, we conducted an observational, descriptive cohort study of adults (≥18 years) receiving dialysis on Jan 1, 2018 (index) and with 6 months of Medicare fee-for-service coverage pre-index. Pts with prior kidney transplant, cancer, and hospitalization for heart failure, myocardial infarction, or stroke in the previous month were excluded. Follow-up was from index until death, loss of Medicare coverage, kidney transplantation, or Dec 31, 2019. Use of erythropoiesis-stimulating agents (ESA), IV iron, and blood transfusions were assessed during follow-up. ESA, iron use, and transfusions were identified by a combination of HCPCS codes, ICD-10-PCS procedure codes, and/or revenue center codes. ESA use was calculated as days covered/week (total days covered by ESA divided by follow-up time in weeks; 3 epoetin alfa administrations covered 7 days; 1 darbepoetin for 14 days), IV iron calculated as number of administrations per week, and transfusions as number per 100 person-years. Overall rates were calculated as weighted mean of pt-level rates using follow-up as weight. Results: Overall, 209,408 pts were on HD and 20,647 on PD; median follow-up was 24.0 months (HD, IQR: 14.6-24.0; PD, 13.0-24.0), PD pts were younger (median age 62.2 vs 64.7 yrs). More PD pts were White (53.5 vs 39.1%), higher income (34.0 vs 48.7% with Medicare/Medicaid dual enrollment), and with glomerulonephritis as the cause of end-stage kidney disease (15.2 vs 8.5%). PD pts had fewer comorbidities and shorter dialysis duration (median 2.7 vs 3.9 yrs). During follow-up (Table), PD pts had lower ESA and IV iron use (1.24 vs 2.64 days of ESA coverage/week; 0.15 vs 0.48 iron administrations/week) and a higher transfusion rate (38.3 vs 32.4 /100 person-years). Conclusions: In this descriptive study, PD pts had lower ESA and iron use and a higher transfusion rate than HD pts during follow-up. Anemia management may need improvement among PD patients. Funding: Commercial Support - Funded by GSK (Study 217316)Table.: Anemia treatment in prevalent dialysis patients, by modality
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".