Long-Term Health Outcomes of Lecanemab in Patients with Early Alzheimer’s Disease Using Simulation Modeling
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a progressive, neurodegenerative disease and is the most common cause of dementia. Lecanemab is a humanized monoclonal antibody targeting amyloid protofibrils for the treatment of early AD. In the phase II BAN2401-G000-201 trial (NCT01767311), lecanemab reduced amyloid accumulated in the brain and slowed progression on key global and cognitive scales evaluating efficacy after 18 months of treatment. METHODS: A disease simulation model was used to predict the long-term clinical outcomes of lecanemab for patients with early AD [i.e., mild cognitive impairment (MCI) due to AD and mild AD dementia] on the basis of BAN2401-G000-201 trial data and published literature. The model captures the pathophysiology and management of AD, with a focus on simulating the effects of disease modification and early intervention on disease progression. The model compares lecanemab in addition to standard of care (SoC) versus SoC alone. RESULTS: Lecanemab treatment was estimated to slow the rate of disease progression, resulting in an extended duration of MCI due to AD and mild AD dementia and shortened duration in moderate and severe AD dementia. The mean time to mild, moderate, and severe AD dementia was longer for patients in the lecanemab + SoC group than for patients in the SoC group by 2.51, 3.13, and 2.34 years, respectively. On base-case analysis, lecanemab was associated with 0.73 incremental life years (LY) and 0.75 incremental quality-adjusted LYs (QALY), and the caregiver QALYs lost was reduced by 0.03 years. The model also predicted a lower lifetime probability of admission to institutional care in lecanemab + SoC versus SoC group (25% versus 31%). CONCLUSION: The model results demonstrate the potential clinical value of lecanemab for patients with early AD and how it can slow the rate of disease progression and reduce the lifetime probability for institutionalized care.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".