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Record W4362507008 · doi:10.1007/s40120-023-00473-w

A Path to Improved Alzheimer’s Care: Simulating Long-Term Health Outcomes of Lecanemab in Early Alzheimer’s Disease from the CLARITY AD Trial

2023· article· en· W4362507008 on OpenAlexaff
Amir Abbas Tahami Monfared, Weicheng Ye, Aditya Sardesai, Henri Folse, Ameya Chavan, Elena Aruffo, Quanwu Zhang

Bibliographic record

VenueNeurology and Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University Health Centre
FundersEisai
KeywordsMedicineDementiaDiseaseNeurologyAlzheimer's diseaseCognitive declineQuality of life (healthcare)CognitionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Alzheimer's disease (AD), a progressive neurodegenerative disease, is the main cause of dementia and one of the leading causes of death for elderly people in the USA. Lecanemab is a humanized IgG1 monoclonal antibody targeting amyloid protofibrils for the treatment of early AD [i.e., mild cognitive impairment (MCI) or mild AD dementia]. In a recent 18-month phase III trial, using a double-blind, placebo-controlled design, lecanemab treatment led to reduced brain amyloid burden and significant improvements in cognitive and functional abilities in individuals with early AD. METHODS: An evidence-based patient-level disease simulation model was updated to estimate the long-term health outcomes of lecanemab plus standard of care (SoC) compared to SoC alone in patients with early AD and evidence of brain amyloid burden, using recent phase III trial data and published literature. The disease progression is described by changes in the underlying biomarkers of AD, including measures of amyloid and tau, and their connection to the clinical presentation of the disease assessed through various patient-level scales of cognition and function. RESULTS: Lecanemab treatment was estimated to slow the progression of AD to moderate and severe stages and reduce the time spent in these more advanced states. In individuals with early AD, lecanemab plus SoC was associated with a gain of 0.71 quality-adjusted life-years (QALYs), a 2.95-year delay in mean time to progression to AD dementia, a reduction of 0.11 years in institutional care, and an additional 1.07 years in community care as shown in the base-case study. Improved health outcomes were demonstrated with lecanemab treatment when initiated earlier based on age, disease severity, or tau pathology, resulting in estimated gains in QALYs ranging from 0.77 to 1.09 years, compared to 0.4 years in the mild AD dementia subset, as shown by the model. CONCLUSION: The study findings demonstrate the potential clinical value of lecanemab for individuals with early AD by slowing down disease progression and prolonging time in earlier stages of disease, which significantly benefits not only patients and caregivers but also society overall. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT03887455.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.063
GPT teacher head0.379
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2023
Admission routes1
Has abstractyes

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