Meta‐Analysis of Intensive Lipid‐Lowering Therapy in Patients With Polyvascular Disease
Bibliographic record
Abstract
Background Polyvascular atherosclerotic disease is associated with an increased risk of future cardiovascular events. Intensive lipid‐lowering therapy (ILT) may mitigate this risk. The aims of this study‐level meta‐analysis were to examine the effects of ILT in patients with polyvascular disease and whether baseline low‐density lipoprotein cholesterol (LDL‐C) may determine the level of benefit. Methods and Results Electronic databases were searched through January 2020 to identify randomized controlled trials of treatments targeting upregulation of LDL‐C receptors (ie, statins, ezetimibe, and PCSK9 [proprotein convertase subtilisin–kexin type 9] inhibitors). The primary end point was major adverse vascular events as defined by the included studies. A total of 94 362 patients (14 821 [18.6%] with polyvascular disease) from 7 studies were included. In patients with monovascular disease, ILT was associated with a 13% reduction in the primary end point (rate ratio [RR] 0.87; 95% CI, 0.81–0.93 [ P =0.0002]) (absolute RR, 1.8%) compared with less ILT, while patients with polyvascular disease had 15% relative RR (0.85; 95% CI, 0.80–0.90 [ P <0.00001]) (absolute RR, 6.5%) ( P =0.66 for interaction). When factoring LDL‐C, unlike patients with monovascular disease, the relative benefits of ILT, compared with less ILT, in patients with polyvascular disease were comparable with LDL‐C >100 mg/dL (RR, 0.85; 95% CI, 0.80–0.90 [ P <0.00001]) and LDL‐C <100 mg/dL (RR, 0.88; 95% CI, 0.81–0.96 [ P =0.003]) ( P =0.23 for interaction). Conclusions Patients with polyvascular disease experienced comparable benefits to those with monovascular disease in response to ILT. The benefits of ILT in patients with polyvascular disease were not dependent on baseline LDL‐C, challenging the approach of using LDL‐C as a prerequisite to commence ILT for this high‐risk subgroup.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.004 | 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".