Evinacumab in patients aged 5–17 years with homozygous familial hypercholesterolemia
Bibliographic record
Abstract
BACKGROUND AND AIMS: Children and adolescents with homozygous familial hypercholesterolemia (HoFH) routinely require advanced lipid-lowering therapies (LLTs). We assess the long-term efficacy and safety of evinacumab, a novel LLT, in children and adolescents with HoFH. METHODS: Study 17100 (NCT04233918) was a phase 3, single-arm, open-label study enrolling 20 children aged 5-11 years with HoFH. ELIPSE-OLE (NCT03409744) was a phase 3, single-arm study enrolling 14 adolescents aged 12-17 years with HoFH. Participants received intravenous evinacumab 15 mg/kg every 4 weeks; all individuals received stable LLT and most received lipoprotein apheresis (60 % of children aged 5-11 years; 64 % of adolescents aged 12-17 years). Outcomes included change from baseline to weeks 48 and 72 in low-density lipoprotein cholesterol (LDL-C) and other lipid parameters. Safety was assessed as treatment-emergent adverse events (TEAEs). RESULTS: In children aged 5-11 years, mean (standard deviation [SD]) baseline LDL-C (301.9 [149.1] mg/dL) was lowered by 45 % (131.1 mg/dL) at week 48 and 41 % (115.8 mg/dL) at week 72. In adolescents aged 12-17 years, mean (SD) baseline LDL-C (300.4 [100.5] mg/dL) was lowered by 48 % (156.4 mg/dL) at week 48 and 51 % (165.6 mg/dL) at week 72. TEAEs occurred in 100 % and 86 % of participants aged 5-11 and 12-17 years, respectively. TEAEs were considered treatment related in four individuals aged 5-11 years (20 %); no one aged 12-17 years had treatment-related TEAEs (0 %). CONCLUSIONS: Evinacumab markedly reduced LDL-C in children and adolescents with HoFH, beyond optimized standard LLT and lipoprotein apheresis. LDL-C remains above goal in most pediatric patients with HoFH, and evinacumab should be routinely considered whenever further LDL-C lowering is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".