Effect of Ezetimibe Added to High-Intensity Statin Therapy on Low-Density Lipoprotein Cholesterol Levels: A Meta-Analysis
Bibliographic record
Abstract
Background: Adding ezetimibe to high-intensity statin therapy is used for additional lowering of low-density lipoprotein cholesterol (LDL-C); however, there are little data on the efficacy of ezetimibe when combined with a high-intensity statin. A meta-analysis was performed to evaluate the efficacy of ezetimibe added to high-intensity statin therapy on LDL-C levels. Methods: A literature search from database inception to May 2020 was performed using PubMed, EMBASE and Cochrane Central Register of Controlled Trials. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in this meta-analysis, in which the random-effects model was adopted for the calculation of the mean difference (MD). The Cochrane Collaboration’s tool for assessing the risk of bias was used to evaluate the quality of the included trials. Results: A total of 14 trials with 2,007 patients were included in this study. Compared to the high-intensity statin monotherapy, the MD in LDL-C reduction with high-intensity statin therapy plus ezetimibe was -14.00% (95% confidence interval: -17.78 to -10.22; P < 0.001) with a moderate degree of heterogeneity (P < 0.001, I 2 = 66%). No significant publication bias among the included trials was identified. Conclusions: Our study found that adding ezetimibe to high-intensity statin therapy provided a significant but attenuated incremental reduction in LDL-C levels. Whether the magnitude of this additional lowering of LDL-C levels would lead to benefits in clinical cardiovascular outcomes needs further investigation. Cardiol Res. 2021;12(2):98-108 doi: https://doi.org/10.14740/cr1224
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.073 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".