Cost Effectiveness of First-Line Oral Therapies for Pulmonary Arterial Hypertension: A Modelling Study
Bibliographic record
Abstract
BACKGROUND: In recent years, a significant number of costly oral therapies have become available for the treatment of pulmonary arterial hypertension (PAH). Funding decisions for these therapies requires weighing up their effectiveness and costs. OBJECTIVE: The aim of this study was to assess the cost effectiveness of monotherapy with oral PAH-specific therapies versus supportive care as initial therapy for patients with functional class (FC) II and III PAH in Canada. METHODS: A cost-utility analysis, from the perspective of a healthcare system and based on a Markov model, was designed to estimate the costs and quality-adjusted life-years (QALYs) associated with bosentan, ambrisentan, riociguat, tadalafil, sildenafil and supportive care for PAH in treatment-naïve patients. Separate analyses were conducted for cohorts of patients commencing therapy at FC II and III PAH. Transition probabilities, based on the relative risk of improving and worsening in FC with treatment versus placebo, were derived from a recent network meta-analysis. Utility values and costs were obtained from published data and clinical expert opinion. Extensive sensitivity analyses were conducted. RESULTS: Analysis suggests that sildenafil is the most cost-effective therapy for PAH in patients with FC II or III. Sildenafil was both the least costly and most effective therapy, thereby dominating all other treatments. Tadalafil was also less costly and more effective than supportive care in FC II and III; however, sildenafil was dominant over tadalafil. Even given the uncertainty within the clinical inputs, the probabilistic sensitivity analysis showed that apart from sildenafil and tadalafil, the other PAH therapies had negligible probability of being the most cost effective. CONCLUSION: The results show that initiation of therapy with sildenafil is likely the most cost-effective strategy in PAH patients with either FC II or III disease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".