Characterizing the Journey of Early Alzheimer’s Disease in Patients Initiating Lecanemab Treatment in the United States: A Real-World Evidence Study
Bibliographic record
Abstract
INTRODUCTION: With the advent of disease-modifying therapies for early Alzheimer's disease (AD), a comprehensive characterization of patients initiating lecanemab in the USA is needed to understand its use in real-world settings. METHODS: This retrospective observational study used administrative claims from the Komodo Research Database (1/1/2023-6/30/2024). Eligible patients had ≥ 1 lecanemab administration (first claim defined the index date) and ≥ 12 months of clinical activity/insurance eligibility before the index date. Patient characteristics, diagnostic process, and AD-related medications were evaluated within 12 months before the index date (baseline), whereas lecanemab treatment patterns and concomitant medications were evaluated on or after the index date (follow-up). Outcomes were reported using descriptive statistics and persistence to lecanemab was evaluated using Kaplan-Meier analysis. RESULTS: Of 3155 patients included in the study, mean age was 75.0 years, 55.8% were female, 44.2% were male, and most (93.3%) received their index lecanemab administration in an urban setting. Diagnoses of AD (83.8%) and mild cognitive impairment (60.8%) were common at baseline, and 67.6% of patients used AD symptomatic medications. Average time from earliest diagnosis to first lecanemab administration was 4.9 months among patients with a diagnosis in January 2023 (accelerated approval date) or onwards. Over a mean follow-up of 138.8 days, the monthly mean number of administrations of lecanemab was 1.9, with an average of 16.5 days between consecutive administrations and 47.4 days to the first follow-up head magnetic resonance imaging. Persistence to lecanemab was 87.6% at 4 months after treatment initiation. CONCLUSION: Lecanemab was utilized in appropriate patient populations according to the prescribing information approved by the US Food and Drug Administration. Findings from our study provide first insights into the real-world use of lecanemab in the USA and shed light on the need for increased and timely lecanemab initiation for the long-term management of early AD.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".