Magnitude of the Potential Screening Gap for Fabry Disease in Manitoba: A Population-Based Retrospective Cohort Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».