#2201 Access to waitlisting & kidney transplantation for patients with incident kidney failure in Australia and the United Kingdom—a binational comparative analysis
Notice bibliographique
Résumé
Abstract Background and Aims System processes for evaluating and managing patient access to kidney transplantation (KTx) presents complex policy challenges. These processes are influenced by system frameworks operating at national, regional and centre levels, where both explicit policy directives and implicit clinical decisions shape patient evaluation and access pathways. Research has highlighted regional and centre-level differences in practice resulting in variations in access to kidney transplantation. Limited comparative studies have been conducted at an international level to assess the impact of national policy decisions on evaluation and access to kidney transplantation. The purpose of this study was to compare access to and predictors of waitlisting and kidney transplantation among patients with incident kidney failure in the United Kingdom (UK) and Australia. Method Incident adult patients commencing kidney replacement therapy (KRT) between 2010–2020 recorded in the United Kingdom Renal Registry (UKRR) and Australian & New Zealand Dialysis & Transplant (ANZDATA) Registry were included for analysis. The primary outcome was time-to-waitlisting with death and living donor kidney transplantation (LDKT) prior to waitlisting treated as competing risks. Secondary analysis included time-to-deceased donor transplantation. The cumulative incidence of the first observed outcome was recorded for each country. Multivariable competing risk time-to-event models were used to compare predictors of waitlisting between countries. Results The study cohort comprised 29,901 & 70,583 patients from Australia & the UK, respectively. Similar clinical and demographic characteristics were seen across the two groups. In Australia, 7,044 (23.6%) patients were waitlisted and 1,743 (5.8%) received a LDKT. In the UK, 22,745 (32.2%) patients waitlisted and 4,336 (6.1%) receiving a LDKT. (Table 1 and Fig. 1a). In examining predictors of waitlisting/LDKT, the difference between women and men in the chance of waitlisting/LDKT was bigger in Australia, with women less likely than men (Australia sub-distribution hazard ratio (SHR) 0.78 (95% CI 0.74–0.81), UK SHR 0.91 (95% CI 0.89–0.93)). There was a disparity in the likelihood of waitlisting/LDKT for patients with diabetic kidney disease (Australia SHR 0.35 (95% CI 0.33–0.37), UK SHR 0.55 (95% CI 0.53–0.57). Age, socio-economic status and smoking status were similar between the two countries. A secondary analysis examined deceased donor transplantation as the primary event with competing events of LDKT and death. In Australia, 5,254 (17.6%) and 2,053 (6.9%) patients had deceased and LDKT compared to 14,872 (21.1%) and 6,511 (9.2%) in the UK (Fig. 1b). Mortality was higher in the UK compared to Australia in both analyses (Fig. 1). Conclusion Incident KRT patients in Australia had lower rates of waitlisting and living donor transplantation compared to UK patients. Higher mortality rates were observed in the UK. Policy initiatives in the UK that prioritise pre-emptive waitlisting and access to pre-emptive deceased donor transplantation may support earlier waitlisting/LDKT for Australian KRT patients.
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 | 0,003 | 0,007 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».