Abstract 14267: Evinacumab Lowers LDL-C in Patients With Homozygous Familial Hypercholesterolemia Irrespective of Background Lipid-lowering Medication
Bibliographic record
Abstract
Background: Homozygous familial hypercholesterolemia (HoFH) is characterized by very premature atherosclerotic cardiovascular disease due to profoundly elevated levels of LDL-C. Attempts to lower LDL-C in patients with HoFH often require multiple lipid-lowering medications (LLMs). Evinacumab, an angiopoietin-like protein 3 inhibitor, has been shown to reduce LDL-C in patients with HoFH by approximately 50% when added to maximally tolerated background LLMs. Objective: In this post-hoc analysis we assessed efficacy of evinacumab in patients with HoFH according to background LLM type. Methods: This was a double-blind, placebo-controlled, 24-week phase 3 trial (NCT03399786) that randomized patients 2:1 to receive intravenous (IV) evinacumab 15 mg/kg (n=43) or IV placebo (n=22) every 4 weeks. The effect of background LLMs on the efficacy of evinacumab to lower LDL-C was assessed for the following subgroups: high intensity-statin, low-intensity statins, lomitapide, triple therapy (ezetimibe + PCSK9 inhibitor + statin), and quadruple therapy (triple therapy + lomitapide). Primary endpoint was % LDL-C reduction from baseline to week 24. Results: At baseline, 55.4% (evinacumab, 58.1%; placebo, 50.0%) of HoFH patients were on triple therapy. Overall, 93.8% were on statins (76.9% on high intensity statins). Across LLM subgroups, mean baseline LDL-C levels ranged from 166.8 mg/dL to 281.8 mg/dL (Table). From baseline to week 24, marked reductions in LDL-C occurred with evinacumab treatment, which were observed in all groups: quadruple therapy (66.8%), triple therapy (56.0%), lomitapide (49.6%), high-intensity statins (48.6%) and low-intensity statins (41.0%). Evinacumab was generally well-tolerated. Conclusions: Evinacumab substantially lowers LDL-C levels in patients with HoFH irrespective of background LLM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".