Addition of Ezetimibe to statins for patients at high cardiovascular risk: Systematic review of patient‐important outcomes
Bibliographic record
Abstract
Ezetimibe is widely used in combination with statins to reduce low-density lipoprotein. We sought to examine the impact of ezetimibe when added to statins on patient-important outcomes. Medline, EMBASE, CINAHL, and CENTRAL were searched through July, 2016. Randomized controlled trials (RCTs) of ezetimibe combined with statins versus statins alone that followed patients for at least 6 months and reported on at least one of all-cause mortality, cardiovascular deaths, non-fatal myocardial infarctions (MI), and non-fatal strokes were included. Pairs of reviewers extracted study data and assessed risk of bias independently and in duplicate. Quality of evidence was assessed using the GRADE approach. We conducted a narrative review with complementary subgroup and sensitivity analyses. IMPROVE-IT study enrolled 93% of all patients enrolled in the 8 included trials. Our analysis of the IMPROVE-IT study results showed that in patients at high risk of cardiovascular events, ezetimibe added to statins was associated with i) a likely reduction in non-fatal MI (17 fewer/1000 treated over 6 years, moderate certainty in evidence); ii) a possible reduction in non-fatal stroke (6 fewer/1000 treated over 6 years, low certainty); iii) no impact on myopathy (moderate certainty); iv) potentially no impact on all-cause mortality and cardiovascular death (both moderate certainty); and v) possibly no impact on cancer (low certainty). Addition of ezetimibe to moderate-dose statins is likely to result in 17 fewer MIs and possibly 6 fewer strokes/1000 treated over 6 years but is unlikely to reduce all-cause mortality or cardiovascular death. Patients who place a high value on a small absolute reduction in MI and are not adverse to use of an additional medication over a long duration may opt for ezetimibe in addition to statin therapy. Our analysis revealed no increased specific harms associated with addition of ezetimibe to statins.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".