Effect of alirocumab on cataracts in patients with acute coronary syndromes
Bibliographic record
Abstract
BACKGROUND: Some data suggest that low levels of low-density lipoprotein cholesterol (LDL-C) are associated with risk of cataracts. Proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors reduce LDL-C below levels achieved with statins alone. We determined whether the incidence of cataracts was influenced by treatment with the PCSK9 inhibitor alirocumab versus placebo, and whether that incidence was affected by achieved LDL-C levels. METHODS: The ODYSSEY OUTCOMES trial (NCT01663402) compared alirocumab with placebo in 18,924 patients with recent acute coronary syndrome receiving high-intensity or maximum-tolerated statin. Incident cataracts were pre-specified events of interest. In multivariable analysis using propensity score-matching on characteristics including cataract risk factors, incident cataracts were compared in the alirocumab and placebo groups according to LDL-C levels achieved with alirocumab. RESULTS: Over median follow-up of 2.8 years (interquartile range 2.3 - 3.4), the incidence of cataracts was similar with alirocumab (127/9462 [1.3%]) versus placebo (134/9462 [1.4%]); hazard ratio [HR] 0.94, 95% confidence interval [CI] 0.74 - 1.20). In patients treated with alirocumab with ≥ 2 LDL-C values < 25 mg/dL (0.65 mmol/L), the incidence of cataracts was 71/4305 (1.6%), versus 60/4305 (1.4%) in propensity score-matched patients from the placebo group (HR 1.10, CI 95% 0.78 - 1.55). In patients treated with alirocumab with ≥ 2 LDL-C values < 15 mg/dL (0.39 mmol/L), the incidence of cataracts was 13/782 (1.7%), versus 36/2346 (1.5%) in matched patients from the placebo group (HR 1.03, CI 95% 0.54 - 1.94). CONCLUSION: Treatment with alirocumab versus placebo, added to statin, did not influence the incidence of cataracts, even when achieved LDL-C levels on alirocumab were very low. Longer follow-up studies might be necessary to exclude the long-term effects on the incidence or progression of cataracts. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01663402 .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".