Impact of selexipag use within 12 months of pulmonary arterial hypertension diagnosis on hospitalizations and medical costs: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Oral selexipag, a prostacyclin pathway agent (PPA), is effective in patients with pulmonary arterial hypertension (PAH). The objective of this study is to assess the impact of initiating oral selexipag within 12 months of diagnosis on health outcomes. METHODS: This retrospective cohort study used data from Optum's de-identified Clinformatics® Data Mart Database. PAH patients between 1 October 2015 and 30 September 2019 were included. Patients were also required to have received PAH medication within 12 months of their initial diagnosis. Study groups included patients who initiated selexipag within 12 months of PAH diagnosis (SEL ≤ 12) and those who did not initiate any PPA within 12 months of PAH diagnosis (No PPA ≤ 12). Inverse probability of treatment weighting was used to remove potential confounding between groups. Cox and Poisson regression models were used to compare hospitalization and disease progression. Generalized linear model with gamma distribution and log link was used to compare costs. RESULTS: SEL ≤ 12 had lower rate of all-cause hospitalizations (rate ratio: 0.76, 95% confidence interval [CI]: 0.60, 0.96) versus no PPA ≤ 12, but no differences in PAH-related hospitalization rate (rate ratio: 1.03, 95% CI: 0.79, 1.33) or risk of disease progression (hazard ratio: 1.01, 95% CI: 0.71, 1.44). SEL ≤ 12 incurred lower all-cause (mean difference: -$23 623; 95% CI: -35 537, -8512) and PAH-related total medical costs (mean difference: -$12 927; 95% CI: -19 559, -5679) versus no PPA ≤ 12. CONCLUSION: Selexipag initiation within 12 months of PAH diagnosis demonstrated reductions in all-cause hospitalization rate and medical costs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".