Severe, eosinophilic asthma in primary care in Canada: a longitudinal study of the clinical burden and economic impact based on linked electronic medical record data
Bibliographic record
Abstract
Stratification of patients with severe asthma by blood eosinophil counts predicts responders to anti-interleukin (IL)-5 (mepolizumab and reslizumab) and anti-IL-5 receptor α (benralizumab) therapies. This study characterized patients with severe asthma who could qualify for these biologics in a primary care setting. We retrospectively selected patients from July 1, 2010, to June 30, 2014, using a linked electronic medical records (EMR) database (IMS Evidence 360 EMR Canada) for > 950,000 patients in primary care in Ontario, Canada. Patients aged ≥ 12 years with ≥ 2 documented asthma diagnoses were identified as having severe asthma based on prescriptions for high-dosage inhaled corticosteroids (ICS) plus either a leukotriene receptor antagonist, long-acting β 2 -agonist (LABA), or theophylline filled on the same day. Patients’ asthma was considered severe also if they received a prescription for ICS with oral corticosteroids (OCS) or an additional prescription for omalizumab. Patient characteristics, asthma-related medications, and blood eosinophil counts were captured using observed care patterns for the year prior to ICS/LABA and/or OCS prescription. Health care resource use (HCRU) and costs were captured throughout the 1-year follow-up period. We identified 212 patients who met the criteria for severe asthma. These patients required an average of 6.5 physician visits during the 1-year follow-up period (95% confidence interval 5.7–7.3), and 20 (9%) were referred to respiratory specialists. Overall, 56 patients (26%) with severe asthma had complete blood counts, of whom 23 (41%) had blood eosinophil counts ≥ 300 cells/μL and might be considered for anti-eosinophil therapies. Patients with severe asthma and blood eosinophil counts ≥ 300 cells/μL had more respiratory specialist referrals (17% vs. 12%) than patients with blood eosinophils < 300 cells/μL. Our data suggest that during 2010–2014, Ontario primary care patients with severe asthma and high blood eosinophil counts had greater HRCU than those with lower counts. Approximately 41% of patients with severe asthma could qualify for anti-eosinophil drugs based on blood eosinophil counts. However, the eosinophilic status of most patients was unknown. It is appropriate to increase awareness of the use of blood eosinophil counts to identify patients who could be considered for anti-eosinophil therapies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".