#1642 Characteristics and outcomes of culturally and linguistically diverse patients receiving kidney replacement therapy in Australia: insights from a nationwide registry study (CALD-KF study)
Notice bibliographique
Résumé
Abstract Background and Aims Australia is one of the world's most culturally and linguistically diverse (CALD) nations, yet there is limited research on the characteristics and outcomes of CALD individuals undergoing kidney replacement therapy (KRT). This study aims to examine demographic and clinical characteristics, access to transplantation, and health outcomes of CALD patients receiving KRT in Australia over the past two decades. Method Data were obtained from the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) between 1 January 2002 and 31 December 2023. Non-Indigenous adults who initiated KRT in Australia during the study period were included. Patients were categorised into three groups: 1) individuals born in Australia/New Zealand (Aus/NZ), 2) CALD English (born in predominantly English-speaking countries), and 3) CALD non-English (born elsewhere). Statistical analysis included chi-squared tests, multivariate regression, and Kaplan-Meier survival curves to evaluate characteristics and outcomes, including mortality and access to transplantation. Results The cohort consisted of 52,045 patients, 64% of whom were male, with a median age of 62 years, and predominance of Aus/NZ-born individuals. CALD non-English patients had a higher prevalence of diabetic kidney disease, were more likely to commence KRT on peritoneal dialysis and had lower rates of pre-emptive kidney transplant. They were also more likely to live in postcodes with higher socioeconomic disadvantage and in major cities. In contrast, CALD English patients were older at KRT initiation, had higher number of comorbidities and were more likely to be former smokers. Late referrals were relatively consistent across the groups. While native fistulas were the most common vascular access across all groups, they were slightly less frequent in CALD groups. Baseline characteristics are summarised in Table 1. Mortality was highest in CALD-English patients and lowest in CALD-Non-English patients, consistent across all initial KRT modalities (Fig. 1). Cardiovascular disease and withdrawal from dialysis were the leading causes of death, with withdrawal being more frequent in CALD English and Aus/NZ groups, while infections were more common in CALD non-English patients (Fig. 2). Medical comorbidities, age, smoking status, body mass index, living in postcodes with higher socioeconomic disadvantage, and remoteness were all significant predictors of waitlisting and death. After adjustment for the above factors, CALD non-English had higher likelihood of waitlisting (subdistribution hazard ratios (SHR) 1.33, 95% CI: 1.28–1.4) and lower likelihood of death (SHR 0.76, 95% CI 0.74–0.78) (P < 0.001). Conclusion This study highlights significant differences in the characteristics and outcomes of CALD patients receiving KRT in Australia. CALD non-English patients demonstrated a healthier profile, with higher likelihood of waitlisting for kidney transplantation and lower mortality rates compared to Aus/NZ and CALD English groups. These findings suggest a “healthy immigrant effect” for CALD non-English patients and aligns with the data from the 2021 Australian census, which reported that individuals born in Australia had the highest prevalence of at least one long-term health condition, followed by those born in England, the USA, Scotland, and New Zealand. Further research is warranted to explore barriers to pre-emptive transplant and vascular access use among CALD groups and to evaluate the impact of these disparities on long-term outcomes. A deeper understanding of cultural and systemic factors influencing KRT care may inform policies to ensure equitable access and improve outcomes for all patients. This study may pave the way for similar research in other countries to assess outcomes and patterns of CALD patients on KRT, helping to identify universal and region-specific factors that influence access to care and patient outcomes across diverse populations.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».