Effect of evolocumab on lipoprotein apheresis requirement and lipid levels: Results of the randomized, controlled, open-label DE LAVAL study
Bibliographic record
Abstract
BACKGROUND: Lipoprotein apheresis (LA) can effectively lower lipoproteins but is an invasive procedure. OBJECTIVE: The objective of this study was to evaluate whether evolocumab can reduce LA requirement in patients undergoing chronic LA. METHODS: Patients on regular weekly or every-2-week LA and moderate- to high-intensity statin (if tolerated) with pre-LA low-density lipoprotein cholesterol (LDL-C) levels ≥2.6 mmol/L (100 mg/dL) to ≤4.9 mmol/L (190 mg/dL) were randomized to continue the same LA frequency, or discontinue LA and receive evolocumab 140 mg every-2-weeks subcutaneously for 6 weeks. At week 6, all patients received only open-label evolocumab for 18 weeks. The primary endpoint was LA avoidance at the end of 6 weeks based on achieving pre-LA LDL-C <2.6 mmol/L at week 4. RESULTS: Thirty-nine patients (mean [SD] age 62 [10] years, 59% male, 82% with familial hypercholesterolemia) were randomized (evolocumab, n = 19; LA, n = 20). At the end of 6 weeks, more patients receiving evolocumab avoided LA than those receiving LA (84% vs 10%; treatment difference, 74% [95% CI: 45, 87]; P < .0001). Thirty patients (77%) did not require LA at 24 weeks. Evolocumab reduced pre-LA LDL-C by 50% from the baseline to week 4 compared with a 3% increase in the LA arm. Pre-LA LDL-C <1.8 mmol/L (70 mg/dL) was achieved by 10 patients (53%) receiving evolocumab and none receiving LA (week 4). Safety was comparable between arms. CONCLUSION: Evolocumab treatment significantly reduced LA requirement in patients undergoing chronic LA. In addition, >50% of patients achieved LDL-C <1.8 mmol/L on evolocumab alone, demonstrating that in patients with pre-LA LDL-C ≤4.9 mmol/L, evolocumab may replace LA.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".