Projected Annual Lecanemab Treatment Eligibility in an Irish Regional Specialist Memory Clinic
Bibliographic record
Abstract
OBJECTIVES: The advent of Disease Modifying Therapies (DMTs) for the treatment of Alzheimer's Disease (AD) has the potential to transform the lives of those with early AD. Timely identification of eligible patients is needed to ensure treatments are delivered during a narrow window of therapeutic opportunity. Appropriate clinical service design will hinge on improved understanding of future demands, thus there is a pressing need to investigate patient eligibility in real world clinical cohorts. The primary aim of this study is to assess the eligibility by appropriate use criteria (AUC) for lecanemab therapy in a real-world, undifferentiated clinical patient cohort attending a Regional Specialist Memory Clinic (RSMC), with the secondary aims of determining the proportion of patients with biomarker positive Alzheimer's Disease (AD) who would be eligible for lecanemab therapy by AUC. Clinical trial eligibility criteria were also applied to both groups and discrepancies that exist between eligibility rates explored. METHODS: A retrospective cohort study of all new patients attending a RSMC from 1st January 2022 to 31st December 2022 was conducted. Data collected included demographic details, outcomes of diagnostic assessments and comorbidities. MRI images, where indicated, were reviewed. Amyloid positivity was defined as either Amyloid and Tau positive (A+T+) or Amyloid positive with a positive P-Tau/Ab42 ratio on cerebrospinal fluid (CSF) testing. Appropriate use criteria (AUC) and clinical trial criteria for lecanemab were applied. Proportion of eligible patients was calculated. RESULTS: Eleven (5.9%) of 188 new patient attenders were eligible (average age 66.7 years [SD 8.9], 63.6% female) by AUC, with 26.2% of patients with biomarker positive Alzheimer's Disease eligible for lecanemab therapy. The most common reason for exclusion was a lack of biomarker confirmation of AD pathology followed by cognitive ineligibility (based on defined cognitive testing cut-offs) at the time of referral and/or initial assessment. Only 40.4% of patients had CSF testing for AD biomarkers while almost 20% of the patients with biomarker positive AD were excluded due to lack of a screening MRI in the previous 12 months. CONCLUSION: In this study, the potential eligibility rate by AUC of the entire patient cohort (5.9%) was limited by the small proportion of patients who had CSF testing for AD biomarkers. So while disease-modification with Lecanemab is a welcome therapeutic advance, although only a small proportion of people currently attending specialist services will be eligible. Successful delivery of DMTs will require significant resource allocation and optimisation of referral pathways to facilitate early identification of potentially eligible patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".