Renal replacement therapy for rare diseases affecting the kidney: an analysis of the ERA-EDTA Registry
Bibliographic record
Abstract
BACKGROUND: In recent years, increased efforts have been undertaken to address the needs of patients with rare diseases by international initiatives and consortia devoted to rare disease research and management. However, information on the overall prevalence of rare diseases within the end-stage renal disease (ESRD) population is limited. The aims of this study were (i) to identify those rare diseases within the ERA-EDTA Registry for which renal replacement therapy (RRT) is being provided and (ii) to determine the prevalence and incidence of RRT for ESRD due to rare diseases, both overall and separately for children and adults. METHODS: The Orphanet classification of rare disease was searched for rare diseases potentially causing ESRD, and these diagnosis codes were mapped to the corresponding ERA-EDTA primary renal disease codes. Thirty-one diagnoses were defined as rare diseases causing ESRD. RESULTS: From 1 January 2007 to 31 December 2011, 7194 patients started RRT for a rare disease (10.6% children). While some diseases were exclusively found in adults (e.g. Fabry disease), primary oxalosis, cystinosis, congenital anomalies of the kidney and urinary tract (CAKUT) and medullary cystic kidney disease affected young patients in up to 46%. On 31 December 2011, 20 595 patients (12.4% of the total RRT population) were on RRT for ESRD caused by a rare disease. The point prevalence was 32.5 per million age-related population in children and 152.0 in adults. Only 5.8% of these patients were younger than 20 years; however, 57.7% of all children on RRT had a rare disease, compared with only 11.9% in adults. CAKUT and focal segmental glomerulosclerosis were the most prevalent rare disease entities among patients on RRT. CONCLUSIONS: More than half of all children and one of nine adults on RRT in the ERA-EDTA Registry suffer from kidney failure due to a rare disease, potentially with a large number of additional undiagnosed or miscoded cases. Comprehensive diagnostic assessment and the application of accurate disease classification systems are essential for improving the identification and management of patients with rare kidney diseases.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".