Cardiovascular and safety events of PCSK9 inhibitors in statin-treated patients with cardiovascular risk: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) is associated with cardiovascular and safety events in statin-treated patients with cardiovascular risk. METHODS: Electronic databases (Pubmed, Cochrane, MEDLINE, EMBASE, ClinicalTrials.gov) were searched through March 31, 2020. Included randomized clinical trials (RCTs) compared PCSK9i use with no PCSK9i in statin treated patients. Two investigators abstracted data and appraised risks of bias. A meta-analysis was performed to calculate risk ratios (RRs) and 95% CIs using fix-effects models. Adjudicated cardiovascular events (CVE) and adverse drug events (ADE) were defined as the primary outcome. Secondary outcomes were cardiovascular (CV) death, all-cause death, nonfatal myocardial infarction, ischemic stroke, serious ADE and injection-site reaction. RESULTS: A total of 10 RCTs 50 053 participants were included. PCSK9i use was associated with signigicant reductions in the CVE (RR, 0.87 [95%CI, 0.83-0.91]; NNT, 54; P<0.00001; I2=0%, heterogeneity P=0.86), nonfatal myocardial infarction (RR, 0.86 [95% CI, 0.78-0.96]; NNT, 95; P=0.005; I2=0%, heterogeneity P=0.88), and ischemic stroke (RR, 0.75 [95%CI 0.64-0.87]; NNT, 244; P=0.00; I2=0%, heterogeneity P=0.82) compared with no PCSK9i in statin-treated patients with CV risk. No significant associations were found between PCSK9i use and no PCSK9i in ADE and serious ADE. PCSK9i use was associated with signigicant increasing in injection-site reaction (RR, 1.55 [95%CI 1.38-1.75]; NNT, 101; P<0.00001; I2=0%, heterogeneity P=0.44). CONCLUSIONS: Among statin-treated patients with CV risk, the use of PCSK9i was associated with improving CV outcomes. The use of PCSK9i was well tolerated, but had significantly injection-site reactions.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".