Eligibility, Clinical Outcomes, and Budget Impact of PCSK9 Inhibitor Adoption: The CANHEART PCSK9 Study
Bibliographic record
Abstract
Background The FOURIER (Further Cardiovascular Outcomes Research With PCSK9i [Proprotein Convertase Subtilisin-Kexin Type 9 Inhibitors] in Subjects With Elevated Risk) trial found a reduction in cardiovascular events in patients with atherosclerotic cardiovascular disease ( ASCVD ). Our objective was to estimate the eligibility, clinical outcomes, and budget impact of adopting PCSK 9i in a large healthcare system. Methods and Results Ontario, Canada, residents alive in 2011, aged 40 to 85 years, were eligible for inclusion. PCSK 9i eligibility was determined on the basis of FOURIER trial definition. Hazard ratios observed in the FOURIER trial were applied to assess the number of events that could be avoided. Budget impact was calculated as the difference between projected costs of treatment adoption and events avoided if PCSK 9i were used. Of the 2.4 million included individuals, 5.3% had a history of ASCVD . We estimated that 2.7% of the general population and 51.9% of the patients with ASCVD would be eligible for PCSK 9i. Adoption of PCSK 9i in all eligible patients with ASCVD was projected to reduce primary events rates by 1.8% after 3 years. Despite cost reduction of $44 million in events, PCSK 9i adoption would have a net budget impact of $1.5 billion over 3 years. Potential benefits of PCSK 9i varied widely across subgroups, with the largest absolute risk reduction estimated to be 4.3% at 3 years in peripheral artery disease. In this subgroup of 5601 patients, the budget impact of treatment adoption was $116 million. Conclusions We estimated that ≈1 in 2 patients with ASCVD would be eligible for PCSK 9i. The budget impact of adopting PCSK 9i for all patients with ASCVD is substantial. Selective adoption to high-risk patients will lessen the overall budgetary impact of PCSK 9i treatment.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".