#5529 RED BLOOD CELL TRANSFUSION RATES IN THE INTERNATIONAL DOPPS PROGRAM (2012–2022): HEMODIALYSIS, PERITONEAL DIALYSIS, AND NON-DIALYSIS CKD POPULATIONS
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».