Red Blood Cell Transfusion Rates in the Early Dialysis Period: Comparing Hemodialysis (DOPPS) and Peritoneal Dialysis (PDOPPS) in an International Setting
Bibliographic record
Abstract
Background: The transition to dialysis is a period of clinical instability during which red blood cell transfusion (RBCT) use may be indicated. Given key differences in pre-dialysis nephrologist care and anemia management between patients who initiate hemodialysis (HD) and peritoneal dialysis (PD), patterns in RBCT rates in the early dialysis period should be investigated. Methods: We used data from 22,643 in-center HD patients from 20 countries in the Dialysis Outcomes and Practice Patterns Study (DOPPS) phases 5-7 (2012-2022) and 3557 PD patients from 6 countries in the Peritoneal DOPPS (PDOPPS) phase 1 (2014-2018). In these prospective cohort studies, RBCT receipt (yes/no) was prospectively captured each month, with rates presented by time since dialysis initiation (vintage). Results: Hemoglobin levels measured within 30 days after dialysis initiation were lower in HD (mean 9.5; 37% <9 g/dL) vs. PD (mean 10.8; 12% <9 g/dL) patients. The RBCT rate (per 100 patient-years) was 74.0 during the first 90 days of HD vs. 12.7 during the first 90 days of PD. After 90 days, RBCT rates were very similar in the HD (21.0) vs. PD (20.8) populations. These patterns - relatively lower RBCT rates during the early PD period but higher RBCT rates during the early HD period - were consistently observed across countries, with rates generally stabilizing after the first 9-12 months of dialysis (Figure 1).Figure 1.: RBCT rates (per 100 patient-years) by dialysis modality and vintageConclusions: Using uniform and standardized RBCT capture across countries and modalities, this study showed high RBCT rates in the first few months of HD, but not PD, therapy, likely reflecting differences in patient profiles. When considering the short-term and long-term risks of RBCT, awareness of this relationship may help inform anemia management before, during, and after this important transition period to reduce the need for RBCT intervention. Funding: Commercial Support - This manuscript was directly supported by GSK. Global support for the ongoing DOPPS Programs is provided without restriction on publications by a variety of funders. For details see https://www.dopps.org/AboutUs/Support.aspx. As of May 5, 2023, the DOPPS program is supported by Amgen Inc. (since 1996, founding sponsor); Akebia Therapeutics, Inc.; Astellas Pharma Inc.; Bard Peripheral Vascular, Inc.; Baxter Healthcare Corp; Bayer AG & Bayer Yakuhin, Ltd; Cara Therapeutics, Inc.; Chugai Pharmaceutical Co., LTD; GlaxoSmithKline LLC; Japanese Society for Peritoneal Dialysis; JMS Co., Ltd; Kidney Foundation Japan; Kissei Pharmaceutical Co., Ltd; Kyowa Kirin Co., Ltd. (since 1999 for Japan DOPPS); Merck Sharp & Dohme Corp; Nikkiso Co., Ltd.; ONO Pharmaceutical Co., Ltd; Terumo Corporation; Torii Pharmaceutical Co., Ltd; CSL-Vifor, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".