An Environmental Scan of Canadian Kidney Transplant Programs for the Management of Patients With Graft Failure: A Research Letter
Notice bibliographique
Résumé
Background: Kidney transplant recipients with graft failure (KTR-GF) and those with a failing graft are an increasingly prevalent group of patients. Their clinical management is complex, and outcomes are worse than transplant naïve patients on dialysis. In 2023, the Kidney Disease: Improving Global Outcomes (KDIGO) organization reported findings from a controversies conference and identified several clinical practice priorities for KTR-GF. Objective: As an exercise in needs assessment, we aimed to collate and summarize current practices in adult Canadian kidney transplant programs around these KDIGO-identified clinical practice priorities. Design: Environmental scan followed by content analysis. Setting: Canadian adult kidney transplant programs. Measurements: We categorized the themes of our content analysis around 7 clinical practice priorities: (1) determining prognosis and kidney failure trajectory; (2) immunosuppression management; (3) management of medical complications; (4) preparing for return to dialysis; (5) evaluation and listing for re-transplantation; (6) management of psychological effects; and (7) transition to supportive care. Methods: We solicited documents that identified each program's current care practices for KTR-GF or patients with a failing graft, including policies, procedures, pathways, and protocols. A content analysis of documents and informal correspondence (email or telephone conversations) was done to extract information surrounding the 7 practice priorities. Results: Of the 18 programs contacted, 12 transplant programs participated in this study and a document from a provincial organization (where 2 non-responding programs are located) was procured and included in this analysis. Overall, practice gaps and discrepancies were noted. Many participants highlighted the lack of evidence or consensus to guide the management of KTR-GF as the key reason. Immunosuppression management was the most frequently addressed priority. Six programs and the provincial document recommended a nuanced approach to immunosuppressant management based on clinical factors and re-transplant candidacy. Two programs used the Kidney Failure Risk Equation and eGFR to determine referral trajectories and prepare patients for return to dialysis. Exact processes outlining medical management during the transition were not found except for nephrectomy indications and in 1 program that has a specific transition clinic for KTR-GF. All programs have a formal or informal policy that KTR-GF should be assessed for re-transplantation. Referrals for psychological support and transition to supportive care were made on a case-by-case basis. Limitations: Our environmental scan was at risk of non-response bias and restricted to transplant programs. Kidney clinics and dialysis units may have relevant policies and procedures that were not examined. Conclusion: The findings from our environmental scan suggest gaps in care and potential areas for quality improvement, including a lack of multidisciplinary care, structured dialysis preparation and psychological support. There is also a need to prioritize research that generates evidence to guide the management of KTR-GF and contributes to the aim of developing clinical practice guidelines.
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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 | 0,011 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,019 | 0,004 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».