Variation in the use of pulmonary vasodilators in children and adolescents with pulmonary hypertension: a study using data from the MarketScan® insurance claims database
Bibliographic record
Abstract
Despite progress in pharmacotherapy in pediatric pulmonary hypertension, real‐world patterns of directed pulmonary hypertension therapy have not been studied in the current era. A retrospective observational study of children (≤18 years) with pulmonary hypertension was performed using data from the MarketScan® Commercial and Medicaid claims databases. Associations between etiology of pulmonary hypertension and pharmaceutical regimen were evaluated, as were the associations between subject social and geographic characteristics (insurance‐type, race, and/or census region) and regimen. Annualized costs of single‐ and multi‐class regimens were calculated. In total, 873 subjects were studied, of which 94% received phosphodiesterase‐5 inhibitors, 31% endothelin receptor antagonist, 9% prostacyclin analogs, and 7% calcium channel blockers. Monotherapy was used in 72% of subjects. Phosphodiesterase‐5 inhibitors monotherapy was the most common regimen (93%). Subjects with idiopathic pulmonary hypertension, congenital heart disease, and unclassified pulmonary hypertension receive more than one agent and were more likely to receive both endothelin receptor antagonist and prostacyclin analogs than other forms of pulmonary hypertension. Compared to recipients of public insurance, subjects with commercial insurance were more likely to receive more intense therapy ( p = 0.003), which was confirmed in multivariable analysis (OR: 1.4, p = 0.03). Receipt of commercial insurance was also associated with increased annual costs across all subjects ( p < 0.001) and for the most common specific regimens. The majority of children with pulmonary hypertension receive phosphodiesterase monotherapy, followed by phosphodiesterase–endothelin receptor antagonist two drug regimens, and finally the addition of prostacyclin analogs for three‐drug therapy. However, even after adjustment for measurable confounders, commercial insurance was associated with higher intensity care and higher costs (even within specific classes of pulmonary vasodilators). The effect of these associations on clinical outcome cannot be discerned from the current data set, but patterns of treatment deserve further attention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".