Validating the Accuracy of Diagnostic Codes for Vision Changes in Giant Cell Arteritis Using Healthcare Administrative Data from a Tertiary Hospital in Ontario, Canada
Bibliographic record
Abstract
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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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 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".