Use of Low‐Density Lipoprotein–Lowering Therapies Before and After PCSK9 Inhibitor Initiation
Bibliographic record
Abstract
Background Proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) are used to reduce low-density lipoprotein (LDL) cholesterol. PCSK9i use after initiation, as well as persistence with or alterations to other LDL-lowering therapy after PCSK9i initiation, is not well understood. Methods and Results We conducted a retrospective study of alirocumab or evolocumab (PCSK9i) new users from July 2015 to December 2017 in the MarketScan Early View database of US commercial insurance beneficiaries. We determined the prevalence of PCSK9i interruption (≥30-day gap in supply) and LDL-lowering therapy use in the year after PCSK9i initiation. The average age of 6151 patients initiating PCSK9i therapy was 63 years, 44.4% were women, and 76.8% had atherosclerotic cardiovascular disease. Overall, 52.2% (95% CI, 50.8%-53.7%) of patients had an interruption in PCSK9i therapy in the first year after treatment initiation and 62.5% remained on PCSK9i therapy at 1-year postinitiation. Also, 27.7% of patients were taking a statin at the time of PCSK9i initiation, with only 22.4% on statin therapy at 1 year after PCSK9i initiation. Ezetimibe use decreased from 20.9% at the time of PCSK9i initiation to 12.0% a year later. By 1 year after PCSK9i initiation, 44.0% of patients had experienced an interruption in all LDL-lowering therapies, and 26.6% were no longer on any LDL-lowering therapies. Conclusions After PCSK9i initiation, statins were often discontinued, whereas more than half of patients experienced an interruption in PCSK9i therapy. These results suggest that many new PCSK9i users may remain at high risk for cardiovascular events because of interruptions in LDL-lowering therapy.
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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) | 0.001 | 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 | 0.000 |
| 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".