#5529 RED BLOOD CELL TRANSFUSION RATES IN THE INTERNATIONAL DOPPS PROGRAM (2012–2022): HEMODIALYSIS, PERITONEAL DIALYSIS, AND NON-DIALYSIS CKD POPULATIONS
Bibliographic record
Abstract
Abstract Background and Aims Anemia is a common complication of chronic kidney disease (CKD), and affects the majority of patients on hemodialysis (HD) and peritoneal dialysis (PD). Consequences of anemia include lower quality of life, increased healthcare utilization, and greater mortality risk. Current international guidelines recommend treating patients with iron and erythropoiesis-stimulating agents (ESAs) to a target hemoglobin (Hb) range, and avoiding red blood cell transfusions (RBCT) when possible, given the associated short- and long-term adverse consequences including hyperkalemia and allosensitization. A description of contemporary RBCT rates – after the 2012 KDIGO guideline changes – across the spectrum of CKD and different geographies is lacking. In this analysis, we estimated RBCT rates in non-dialysis CKD (ND-CKD), PD, and HD patients in a multinational setting, overall and stratified by Hb levels. Method We used data from 3 ongoing prospective cohort studies from the Dialysis Outcomes and Practice Patterns Study (DOPPS) Program: 2443 Stage 3–4 ND-CKD patients from 2 countries in CKDopps phases 1–2 (2013–2022), 3557 PD patients from 6 countries in PDOPPS phase 1 (2014–2018), and 22,771 HD patients from 20 countries in DOPPS phases 5–7 (2012–2022). Within each study, patients were randomly selected from stratified national samples of clinics, representative of a snapshot in time at study entry. Prospective information on RBCT receipt (yes/no) was captured at the patient-month level; thus, multiple events per patient were considered in the rate calculation, if they occurred in different months. RBCT rates were calculated as a count of the total patient-months with an RBCT event divided by the total patient-months with RBCT data available. Rates (per patient-month) were converted to per 100 patient-years, with 95% confidence intervals estimated by Poisson regression. RBCT rates are presented in each population (ND-CKD, PD, HD) across strata of interest, including CKD stage, country/region, and baseline Hb levels. Results Mean age (years) of the cohort was 68.6 for ND-CKD, 59.1 for PD, and 63.2 for HD. Mean (std dev) baseline Hb levels (g/dL) were 12.1 (1.9) in ND-CKD compared to 10.9 (1.7) in PD and 10.9 (1.5) in HD. Median [IQR] months of follow up (with RBCT data available) was 17 [6, 27] for ND-CKD, 16 [9, 20] for PD, and 8 [4, 19] for HD. Table 1 shows crude RBCT rates (per 100 patient-years) in the ND-CKD, PD, and HD populations, overall, by CKD stage and country/region. For ND-CKD, the RBCT rate was 6.7 (95% CI: 5.9, 7.6) overall, including 7.0 in the US and 5.5 in Brazil. RBCT rates increased with ND-CKD stage, from 2.2 in Stage 3a to 4.5 in Stage 3b to 8.5 in Stage 4. For PD, the RBCT rate was 20.3 (95% CI: 19.1, 21.6), higher than in ND-CKD, with substantial variation across countries, from 7.3 in the UK to 45.6 in Thailand. For HD, the RBCT rate was 23.9 (95% CI: 23.3, 24.5), also higher than in ND-CKD, with international variation observed. In the HD cohort, RBCT rates were lower in China (6.3) compared to the US (21.9), Canada (34.4), and the Gulf Cooperation Council region (34.8). Within HD-Europe, RBCT rates were highest in Belgium (48.1) and Sweden (42.4), and lowest in Germany (15.4), France (19.0), and Italy (20.4). Table 2 shows crude RBCT rates by baseline Hb levels. In all 3 cohorts (ND-CKD, PD, HD), RBCT rates appeared higher among patients with lower versus higher baseline Hb levels. In the PD and HD populations, patients with baseline Hb <8 g/dL had RBCT rates >100. Conclusion Our multinational study used uniform and standardized data collection tools, procedures, and processes to estimate RBCT rates in the post-2012 era following the revision of international guidelines. As expected, we observed higher RBCT rates in the PD and HD (vs. ND-CKD) populations, and higher RBCT rates among patients with lower baseline Hb levels. Interestingly, we also observed substantial international variation in RBCT rates in all 3 cohorts (ND-CKD, PD, HD). Further research is needed to better understand the reasons why RBCT use is higher or lower in certain countries, and to investigate predisposing and modifiable risk factors for RBCT. Funding: GSK (study 218930)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".