Lifetime clinical benefits of lecanemab in early Alzheimer’s disease using simulation modeling
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a progressive, neurodegenerative disease and imposes a substantial societal burden. Treatment strategies that slow disease progression may improve health outcomes and reduce burden. Lecanemab is a humanized IgG1 monoclonal antibody that preferentially targets soluble aggregated Aβ species. In the phase 2b BAN2401‐G000‐201 trial, lecanemab reduced clinical decline as measured by key global and cognitive scales at 18 months. Methods A validated disease simulation model was used to assess lifetime health outcomes of lecanemab for patient with early AD (MCI due to AD and mild AD dementia) based on BAN2401‐G000‐201 trial data and published literature. The model captures the pathophysiology and management of AD, with the aim of evaluating the effects of disease modification on disease progression and translating them into meaningful health outcomes. Results Lecanemab treatment was projected to delay disease progression, resulting in an increase in patient’s expected time in the early AD stages while reducing the time in more severe states. A 26% slowing of clinical decline on CDR‐SB in patients treated with lecanemab corresponded to a 7%, 13%, and 10% reduction in the proportion of patients progressing to mild, moderate, and severe AD dementia, respectively, over a lifetime horizon compared to the standard of care (SoC). Similarly, the estimated mean time to advance to mild, moderate, and severe AD dementia extended for lecanemab treatment by 2.51, 3.13, and 2.34 years, respectively. Scenario analyses indicated the potential lifetime impact of lecanemab was greater at earlier, younger onset AD. The incremental mean times for transition to mild and moderate AD dementia were 3.29 and 3.38 years, respectively, in younger adults (baseline mean age 65 vs. 71.5 years) with MCI due to AD only. Compared with SoC, lecanemab treatment improved patient’s quality of life over lifetime with additional 0.75 quality‐adjusted life‐years per patient (4.97 vs. 4.22). Conclusions The findings suggest that lecanemab treatment in early AD may have large health impacts by slowing disease progression to more severe stages of AD and reducing patient, caregiver, and public health burden. Predicted long‐term health outcomes provide a foundation for healthcare decision‐makers to understand the potential clinical and socioeconomic value of lecanemab.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".