Utility of a Renal Genetics Clinic: A Canadian Prospective Cohort
Bibliographic record
Abstract
Background: Genetic kidney disease (GKD) is more prevalent than previously considered, with 1 in 10 chronic kidney disease (CKD) patients affected. When correct inclusion criteria for GKD is used, 34-67% of patients can have a genetic diagnosis. Genomic testing using gene-panel or exome sequencing (ES) can confirm GKD through detection of mutations in genes known to cause CKD. Unfortunately, widespread integration into clinical practice has been hampered by small studies and highly selective populations predominately performed in research rather than clinical settings. This study aims to prospectively demonstrate the utility of a renal genetics clinic in a Canadian cohort. Methods: We analyzed data from a cohort of patients (n=174 families, n=209 affected patients) referred to a renal genetic clinic between September 2019 and April 2023. Testing strategy was firstly to perform gene-panel testing, and if negative or unsuitable, ES was performed. Testing was performed for the detection of mutations in genes suspected to cause the specific subtype of CKD. Mutations are classified according to the American College of Medical Genetics guidelines, with pathogenic and likely pathogenic variants being causative. Results: We identified a causative mutation in a gene known to cause CKD in 38% of patients (n=80/209). Gene panel testing, performed as the first line of investigation, detected the underlying molecular cause of CKD in 31% of patients tested (n=44/142). Primary ES was performed as the first test in patients with CKD of unknown etiology (CKDu) and had a solve rate of 18% (n=8/45). In patients whom gene-panel was negative or not possible (n=82), secondary ES analysis was performed and confirmed the suspected clinical diagnosis in 26% of patients (n=21/82). For solved patients (n=80), there were many clincial outcomes, including change of diagnosis. Of note, 48% of solved patients had a pre-priori diagnosis of CKDu, but all receied a diagnosis after testing. Other important clinical outcomes include, avail of genetic counselling, resolved diagnostic confusion, and correction of diagnosis. Conclusions: We show that in a Canadian cohort of adults referred to a renal genetic clinic, genomic testing has utility by confirming the cause of genetic kidney disease in 38% of patients. Genetic sequencing also has significant impacts on clinical management and patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".