Effect of Anti‐VEGF Therapy on the Disease Progression of Neovascular Age‐Related Macular Degeneration: A Systematic Review and Model‐Based Meta‐Analysis
Bibliographic record
Abstract
Anti-vascular endothelial growth factor (VEGF) therapy is used to slow the disease progression of neovascular age-related macular degeneration. Due to the treatment burden of frequent intravitreal injections, anti-VEGFs are often used on treat and extend protocols rather than the labeled frequency. The current goal of anti-VEGF drug development is to minimize treatment burden by reducing the number of intravitreal injections. The purpose of this systemic review and model-based meta-analysis (MBMA) was to (1) perform modeling to describe the disease progression of neovascular age-related macular degeneration in the absence of treatment, as well as in the presence of abicipar, aflibercept, brolucizumab, or ranibizumab intervention; (2) and to simulate virtual head-to-head comparisons among the drugs with an extended dose schedule of once every 12 weeks (Q12). Data sources were PubMed, internal Allergan data, www.clinicaltrials.gov, and www.clinicaltrialsregister.eu. Eligibility assessment was performed by 2 independent review authors. Randomized, controlled trials that had at least 1 arm with an anti-VEGF (aflibercept, abicipar, bevacizumab, brolucizumab, pegaptanib, or ranibizumab), a control arm of placebo or anti-VEGF, a treatment duration of at least 4 months, reported best-corrected visual acuity data, and at least 20 patients were included. A total of 22 trials, consisting of 55 arms, from across 9500+ subjects and 500+ best-corrected visual acuity observations were used to develop the model. Consistent with reported data, results from the model showed that abicipar Q12 underperformed ranibizumab (every 4 weeks), aflibercept (every 4 weeks), and brolucizumab (every 8 weeks/Q12) labeled dosing schedules. However, when all drugs were virtually tested using the extended schedule, abicipar outperformed ranibizumab and aflibercept and produced a similar week 52 change from baseline as brolucizumab. Predicted week 52 changes from baseline were 5.92 ± 1.02, 3.04 ± 1.61, 6.61 ± 0.284, and 3.02 ± 2.35 best-corrected visual acuity letters for abicipar, aflibercept, brolucizumab, and ranibizumab, respectively, using the Q12 schedule. Results demonstrate the feasibility of Q12 dosing with clinically meaningful letter gains for abicipar and brolucizumab. The model developed under this MBMA has utility for exploring different regimens for existing or novel anti-VEGF agents.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".