Use of Lecanemab for Alzheimer's Disease within the Veteran's Health Foundation: Early Findings
Bibliographic record
Abstract
BACKGROUND: Lecanemab, an anti-amyloid monoclonal antibody for Alzheimer's disease, was approved for use in the Veteran's Health Administration (VHA) in 2023. Little is known about the real-world use and outcomes of lecanemab. METHODS: This retrospective cohort study included Veterans who initiated lecanemab in the VHA between October 2023 and September 2024. Data on demographics, healthcare utilization, medication exposures, and neuroimaging were extracted from the VA Corporate Data Warehouse. Chart review was used to collect information on dementia diagnosis, Montreal Cognitive Assessments (MoCA), and Apolipoprotein E (APOE) genotype. We examined medication adherence, brain MRIs, 6-month MoCAs, amyloid-related imaging abnormalities (ARIA), and healthcare utilization from initiation to study end (12/31/24). RESULTS: Overall, 32 Veterans (mean [SD] age 75 [6] years, 100% male, 97% white, 84% urban-dwelling) initiated lecanemab; 53% had mild cognitive impairment and 47% mild dementia. The baseline mean MoCA score was 21 (SD 3). Half were heterozygous for APOE ε4 and half non-carriers. The median days of follow-up was 172 (range 62-349), and the median number of lecanemab infusions received was 11 (2-24). Brain MRI follow-up occurred regularly, with all eligible patients receiving an MRI between the 4th and 5th infusion, 92% (22/24) between the 6th and 7th infusions, and 92% (11/12) between the 13th and 14th infusions. However, of the 28 patients with 6 months follow-up, only 9 (32%) had a MoCA completed and the mean change in MoCA was -0.2 (SD 4.2). During follow-up, 25% of patients had imaging abnormalities: 2 (6.2%) experienced acute stroke and 6 (18.8%) experienced ARIA (3 ARIA-edema, 3 ARIA-hemorrhage, and 1 both). Three patients (9.4%) stopped lecanemab for ≥30 days by the end of the study period. Reasons for discontinuation included ARIA, side effects, and patient preference. During follow-up, 0 patients died, 3 (9.4%) were hospitalized within VHA, and 11 (34.4%) had VHA emergency department or urgent care visits. CONCLUSIONS: In the first year lecanemab was available in VHA, the few patients initiated on treatment were mostly white, male, and urban residents. The finding that 25% of patients experienced ARIA or stroke underscores the importance of monitoring the lecanemab safety and effectiveness long-term.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".