An Environmental Scan of Canadian Kidney Transplant Programs for the Management of Patients With Graft Failure: A Research Letter
Bibliographic record
Abstract
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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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".