Validating the Accuracy of Diagnostic Codes for Vision Changes in Giant Cell Arteritis Using Healthcare Administrative Data from a Tertiary Hospital in Ontario, Canada
Notice bibliographique
Résumé
Objectives Trends in the incidence and prevalence of giant cell arteritis-associated vision changes (GCAVCs) are unknown and may change over time in response to models of care and new therapeutic options. We developed and validated a case definition for GCAVCs to accurately identify these patients from healthcare administrative data. Methods We conducted a retrospective cohort validation study of individuals from a tertiary healthcare facility in Ontario, Canada using routinely collected healthcare administrative as well as clinical data between December 2017 and November 2023. The reference standard for GCAVC was the presence or absence of visual changes attributable to GCA as assessed by the treating rheumatologist, ophthalmologist, or internist. We simulated population level administrative data using international classification of disease version 10 (ICD10) diagnostic codes that are directly uploaded to national healthcare administrative databases. Individuals included in the cohort were aged 50 or older and assigned an ICD10 code for GCA during an inpatient, outpatient, or emergency department visit. We captured cases of GCA using a previously validated definition and assessed the diagnostic accuracy of case definitions for GCAVCs within these cases.[1] Multiple candidate case definitions including the use of ICD10 codes, visits to ophthalmology, and/or time window between onset of vision changes and the first visit to a rheumatologist were used. The diagnostic accuracy of each case definition was calculated, and the optimal case definition was chosen based on sensitivity and positive predictive value while maintaining specificity. Results The cohort included 392 individuals diagnosed with GCA; 77 (26.6%) of which had confirmed GCAVCs based on chart review. For those classified as having GCA using the previously validated case definition, the case definition for GCAVCs that displayed optimal performance was any ICD10 code for GCAVCs within 1 year of the first ICD10 code for GCA with 53.3% (95% CI 38.0-61.7%) sensitivity, 96.0% (91.1-98.4%) specificity, 80.0% (60.9-91.6%) positive predictive value and 87.2% (80.9-91.7%) negative predictive value. (Table 1). Case definitions were more sensitive for permanent visual loss (ischemic optic neuropathy and retinal artery occlusion) than temporary visual loss (amaurosis fugax and diplopia). Table 1: case definitions with diagnostic performance. Bold highlights the parameter that had the best performance for a given case definition; grey indicates the optimal case definition. O = ophthalmology visit, ICD10 code = international classification of diseases version 10 diagnostic code for vision changes. Conclusion We developed a case definition that can be used to capture GCAVCs within healthcare administrative data. The definition can be used to create healthcare administrative cohorts of individuals with GCAVCs to better inform treatment patterns and outcomes at the population level. [1.] Barra L. Rheumatology 2020;59(11):3250-8. Best Abstract by a Rheumatology Post-Graduate Research Trainee Award
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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 | 0,009 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».