Abstract 23087: Applying the FOURIER Trial amongst Real-world Cardiovascular Disease Patients: The Cardiovascular Health in Ambulatory Care Research Team (CANHEART) PCSK9 Study
Bibliographic record
Abstract
Background: FOURIER demonstrated a 15% reduction in cardiovascular CV events for patients with CVD. With its high cost, healthcare systems are faced with the potential impact of adopting PCSK9 inhibitors in practice. The goal of this study was to evaluate eligibility, preventable events and incremental costs if FOURIER is applied to the real-world. Methods: The CANHEART cohort is a population-based database of the entire Ontario adult population created by merging multiple individual-level data sources (www.canheart.ca). CVD patients who were alive and 40- 85 years as of January 1 st 2008, and had a cholesterol measurement were included. Similar eligibility for PCSK9 inhibitors with FOURIER was applied. Our primary outcome was a composite of CV death, MI, unstable angina, stroke or coronary revascularization. Number of preventable events was estimated by applying FOURIER’s observed hazard ratio to our cohort’s survival function. The cost to prevent an event was calculated as the cumulative healthcare cost for each patient, assuming PCSK9 inhibitor costs $8,000 CAD (~$6,000 USD)/yr. Results: Of 1.5 million individuals in CANHEART, 62,729 (4.3%) had a history of CVD and 38,348 (2.6% of entire population, 61% of CVD patients) met FOURIER eligibility. Mean age was 66 years, 69% were men, 78% had a history of MI, and 31% were on high dose statins. Within 3 years, 11.9% of patients experienced a primary outcome. PCSK9 inhibitor uptake by all CVD patients was estimated to reduce primary events by 1.9% (746 events avoided) at a cost of $1.14 million CAD per event. Projected event rates, absolute risk reductions and PCSK9 associated costs are shown in the Table . Conclusions: In this large population-based cohort, 2.6% of individuals and 61% of CVD patients fulfil FOURIER criteria for PCSK9 inhibitor use. While the absolute anticipated benefit of adopting this new therapy was similar to that observed in clinical trials, it is associated with substantial costs to prevent each event.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".