Magnitude of the Potential Screening Gap for Fabry Disease in Manitoba: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
Background: Fabry disease is a rare disorder caused by the deficient activity of α-galactosidase A (GLA) that often leads to organ damage. Fabry disease can be treated with enzyme replacement or pharmacological therapy, but due to its rarity and nonspecific manifestations, it often goes undiagnosed. Mass screening for Fabry disease is impractical; however, a targeted screening program for high-risk individuals may uncover previously unknown cases. Objective: Our objective was to use population-level administrative health databases to identify patients at high risk of Fabry disease. Design: Retrospective cohort study. Setting: Population-level health administrative databases housed at the Manitoba Centre for Health Policy. Patients: All residents of Manitoba, Canada, between 1998 and 2018. Measurements: We ascertained the evidence of GLA testing in a cohort of patients at high risk of Fabry disease. Methods: Individuals without a hospitalization or prescription indicative of Fabry disease were included if they had evidence of 1 of 4 high-risk conditions for Fabry disease: (1) ischemic stroke <45 years of age, (2) idiopathic hypertrophic cardiomyopathy, (3) proteinuric chronic kidney disease or kidney failure of unknown cause, or (4) peripheral neuropathy. Patients were excluded if they had known contributing factors to these high-risk conditions. Those who remained and had no prior GLA testing were assigned a 0% to 4.2% probability of having Fabry disease depending on their high-risk condition and sex. Results: After applying exclusion criteria, 1386 individuals were identified as having at least 1 high-risk clinical condition for Fabry disease in Manitoba. There were 416 GLA tests conducted during the study period, and of those, 22 were conducted in individuals with at least 1 high-risk condition. This leaves a screening gap of 1364 individuals with a high-risk clinical condition for Fabry disease in Manitoba who have not been tested. At the end of the study period, 932 of those individuals were still alive and residing in Manitoba, and if screened today, we expect between 3 and 18 would test positive for Fabry disease. Limitations: The algorithms we used to identify our patients have not been validated elsewhere. Diagnoses of Fabry disease, idiopathic hypertrophic cardiomyopathy, and peripheral neuropathy were only available via hospitalizations and not physician claims. We were only able to capture GLA testing processed through public laboratories. Patients identified to be at high risk of Fabry disease by the algorithm did not undergo GLA testing due to a clinical rationale that we were unable to capture. Conclusions: Administrative health databases may be a useful tool to identify patients at higher risk of Fabry disease or other rare conditions. Further directions include designing a program to screen high-risk individuals for Fabry disease as identified by our administrative data algorithms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".