Disease Overlap, Healthcare Resource Utilization, and Costs in Patients with Eosinophilic Granulomatosis with Polyangiitis: A REVEAL Sub-study
Bibliographic record
Abstract
INTRODUCTION: Eosinophilic granulomatosis with polyangiitis (EGPA) is an eosinophil-associated disease (EAD) characterized by inflammation in small- to medium-sized blood vessels. In the REal-world inVestigation of Eosinophilic-Associated disease overLap (REVEAL) study, overlap among 11 EADs was assessed. In the present sub-study, we evaluated EGPA overlap with other EADs, all-cause EAD- and EGPA-related healthcare resource utilization (HCRU) and costs, and their relationship with blood eosinophil count and treatments received. METHODS: Data Mart Database. In this sub-study, eligibility criteria included an age of ≥ 12 years, ≥ 1 EAD, continuous health-plan eligibility, and compliance with the EGPA/GPA case definition per International Classification of Diseases Ninth/Tenth Revision diagnostic codes between 1 January 2015 and 30 June 2018. Patients were grouped based on whether they had received immunomodulators/cyclophosphamide/mepolizumab (ICM) or not (non-ICM). RESULTS: Of 701 patients with EGPA, 29.5% were in the ICM group. Overall, 72.2% had ≥ 1 overlapping EAD. The number of overlaps was similar for the ICM and non-ICM groups. In patients with blood eosinophil counts ≥ 300 cells/µL, 22.8% had ≥ 1 overlapping EAD. The mean annual all-cause cost was $98,644, 54.1% of which was from outpatients and 33.6% from inpatients. The mean annual EAD- and EGPA-related costs were $23,820 and $9,306, respectively. Patients in the non-ICM group versus the ICM group had higher all-cause ($101,560 vs $91,684) but lower EAD-related ($22,733 vs $26,412) and EGPA-related ($6,171 vs $16,786) costs. All-cause HCRU and costs increased with increasing overlap. CONCLUSIONS: EGPA was associated with substantial HCRU and costs, driven by outpatient and inpatient settings. More overlapping EADs were associated with higher HCRU and costs, highlighting the need for treatment to reduce healthcare expenditure in these patients. Infographic available for this article.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".