Genetic Assessment of Living Kidney Transplant Donors: A Survey of Canadian Practices
Bibliographic record
Abstract
Background: Kidney failure is a prevalent condition with tendency for familial clustering in up to 27% of the affected individuals. Living kidney donor (LKD) transplantation is the optimal treatment option; however, in Canada, more than 45% of LKDs are biologically related to their recipients which subjects recipients to worse graft survival and donors to higher future risk of kidney failure. Although not fully understood, this observation could be partially explained by genetic predisposition to kidney diseases. Genetic testing of potential LKDs may improve risk assessment and inform the safety of donation. The strategies to evaluate these donors are still evolving. In Canada, little is known about the practice of assessing for genetic conditions among LKDs. Aim: The aim was to examine the Canadian practices regarding LKDs genetic assessment. Methods: Questionnaires were sent to 23 Canadian adult transplant centers to examine their protocols for LKDs genetic assessment. Design: The questionnaire comprised of 10 sections and 21 questions including case scenarios of different LKD encounters. Major domains of the survey addressed general demographics, information sharing practices, effect of mode of inheritance on candidacy decision, having a policy for LKD genetic evaluation, and case scenarios covering the following conditions: autosomal dominant polycystic kidney disease (ADPKD), Alport syndrome, Fabry disease, familial focal and segmental glomerulosclerosis (FSGS), atypical hemolytic uremic syndrome (aHUS), autosomal dominant tubulointerstitial kidney disease (ADTKD), sickle cell, and apolipoprotein L1 mutation (APOL1). Participants: The questionnaire was sent to the living-donor assessment committee representative (nephrologist) in adult and pediatric kidney transplant centers across Canada. Results: In total, 16 of 23 Canadian centers responded to the survey. Of the 8 surveyed genetic conditions, ADPKD, Alport syndrome, and aHUS were the most frequently encountered. More centers have specific policies for donor evaluation for ADPKD (25%) and aHUS (21.4%) vs none to very few for other genetic conditions. The most cited guidelines are Kidney Disease Improving Global Outcomes (KDIGO), Canadian Society of Nephrology/Canadian Society of Transplantation (CSN/CST), and the Canadian Blood Services' Kidney Paired Donation Protocol. Conclusions: Canadian transplant centers follow a case-by-case approach rather than a standard protocol for genetic assessment of LKDs given that current guideline recommendations are based on expert opinion due to a lack of a reliable body of evidence. With the expected rise in utilization of the increasingly available genetic testing, early multidisciplinary assessment including medical geneticists has the potential to improve personalized management. Studies examining long-term donor and graft outcomes are needed to construct the basis for evidence-based recommendations and inform the safety of donations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".