Real-World Insights into Evolocumab Use in Patients with Hyperlipidemia Across Five Countries: Analysis from the ZERBINI Study
Bibliographic record
Abstract
INTRODUCTION: This study characterizes patients receiving evolocumab in clinical practice and assesses treatment effectiveness, safety and persistence outcomes across five countries. METHODS: This retrospective and prospective observational study enrolled patients initiated on evolocumab during August 2017 to July 2019 at 49 sites across Canada, Mexico, Colombia, Saudi Arabia and Kuwait. Medical records data were extracted within 6 months prior to (baseline) and every 3 months for 12 months post evolocumab initiation and reported as available. RESULTS: A total of 578 patients were enrolled (40.1% female, median age 60 [interquartile range (IQR) 51-68] years); 83.7% had atherosclerotic cardiovascular disease and/or familial hypercholesterolemia. Median low-density lipoprotein cholesterol (LDL-C) at baseline was 3.4 (IQR 2.7-4.2) mmol/L (131.5 [IQR 104.4-162.4] mg/dL), with 75.6% of patients receiving a statin (59.2% high intensity). Compared to baseline, the median lowest LDL-C was reduced by 70.2% and remained stable over 12 months of treatment. Guideline-recommended LDL-C thresholds < 1.8, < 1.4 and < 1.0 mmol/L (< 70, < 55 and < 40 mg/dL) were achieved by 75.3%, 63.6% and 47.4% of patients. LDL-C outcomes were consistent across high- and very high-risk patients. Background lipid-lowering therapy remained relatively stable. No serious treatment-emergent adverse events were reported, and persistence to evolocumab was 90.2% at 12 months. CONCLUSION: These findings provide real-world evidence that evolocumab use is in accordance with its international guideline-recommended place in dyslipidemia therapy, as well as confirmation of its effectiveness and safety in a heterogeneous population. Evolocumab can address a healthcare gap in the management of dyslipidemia by increasing the proportion of patients achieving LDL-C goals recommended to lower cardiovascular risk.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".