Lower rate of ischemic events with PCSK9 inhibition in patients with atherosclerotic cardiovascular disease but without prior ischemic events
Bibliographic record
Abstract
Abstract Background Individuals with atherosclerotic cardiovascular disease (ASCVD) without prior ischemic events, such as myocardial infarction (MI), ischemic stroke (IS), or unstable angina (UA) hospitalization, are at very high risk for having an event. Purpose To estimate the impact of PCSK9 inhibitor (PCSK9i) therapy on ischemic events in this population. Methods Patients with ASCVD were initially selected from the Optum Research Database during Jan 2016 to Dec 2022. For selected patients the index (time zero for follow-up) was required to be in a period of ASCVD without prior ischemic events, however patients may have ischemic events after index. Determination of ASCVD and ischemic events was based on diagnosis and procedure codes. For the PCSK9i group, index was initiation of PCSK9i and information before index was examined to ascertain ASCVD and rule out prior ischemic events. For each patient in the PCSK9i group, up to 5 matching patients in the no-PCSK9i group were chosen based on propensity score (PS) distance, with maximum allowable PS distance of 0.1. Among all possible choices for index for the no-PCSK9i patient, the selected one minimized the PS-distance. The primary endpoint was defined as the composite of nonfatal MI, nonfatal IS, and all-cause death. In order to further reduce bias and ensure causally interpretable estimation, the G-computation methodology was employed by marginalizing the conditional treatment effect (as estimated by a Cox model) over the baseline characteristics. Two effects were estimated: intention-to-treat (ITT) where only the treatment assignment at index was considered as the intervention, and per-protocol (PP) where the effect of sustained treatment with PCSK9i over time was estimated. Results A total of 15,067 and 66,470 patients met the selection criteria for the PCSK9i and no-PCSK9i groups, respectively. Baseline characteristics were well-balanced at index. G-computation analysis resulted in estimated survival curves over time in the two groups for the ITT and PP scenarios (Figures 1 and 2). For the ITT scenario, the estimated 5-year event rate was 18.7% (95% confidence interval [CI]: 17.1%–20.1%) in the PCSK9i group and 28.4% (95% CI: 27.7%–29.1%) in the no-PCSK9i group. The implied relative risk reduction (RRR) and absolute risk reduction (ARR) were 34.3% (p<0.001) and 9.7% (p<0.001), respectively, in favor of the PCSK9i group. For the PP scenario, the estimated 5-year event rate was 10.7% (95% CI: 9.7%–11.7%) in the PCSK9i group and 28.8% (95% CI: 28.3%–29.5%) in the no-PCSK9i group. The implied RRR and ARR were 62.7% (p<0.001) and 18.1% (p<0.001), respectively, in favor of the PCSK9i group. Conclusions Initiation of PCSK9i in patients with ASCVD but without prior ischemic events is associated with significantly and substantially lower risk for ischemic events.Event Rates Intention-to-Treat ScenarioEvent Rates Per-Protocol Scenario
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".