Treatment Patterns of Agitation associated with Alzheimer's Dementia (AAD) Patients in Canada
Bibliographic record
Abstract
Abstract Background Agitation associated with Alzheimer's dementia (AAD) is a challenging behavioral feature of Alzheimer's dementia (AD) characterized by excessive motor activity, verbal aggression, and physical aggression. Affecting up to 80% of people with AD, AAD is linked with greater caregiver burden, morbidity, and mortality. Due to a paucity of approved AAD treatments, therapeutic strategies may vary across clinicians. To gain insights into clinical practice, we examined the treatment patterns of AAD patients in Canada. Method This was a retrospective cohort study using public drug plan claims data from Ontario and New Brunswick between 2003 and 2023 to characterize lines of therapy (LoTs). An indication algorithm was developed with a clinical expert to identify AAD through prescription claims for cognitive enhancers and medications commonly used to treat AAD symptoms (anticonvulsants [AC], antidepressants [ADT], antipsychotics [AP], benzodiazepines [benzo]). These inferred patients were indexed on the date of their first AC/ADT/AP/benzo and analyzed until their 4 th LoT. Result Overall, 23,732 inferred AAD patients were identified, mostly from Ontario, with a median (IQR) age of 80 (11) years (Table 1). Overall, 70% of the cohort were followed until the end of their 4 th LoT (4L), and the proportion of those in a long‐term care setting increased from 14% (1L) to 54% (4L) (Figure 1). Inferred AAD patients were treated with a wide range of therapies, with over 500 to 2000 unique combinations observed across 1L to 4L, respectively. However, some medications and their combinations consistently ranked among the top ten most common therapies across LoT: citalopram, escitalopram, gabapentin, lorazepam, mirtazapine, pregabalin, quetiapine, risperidone, sertraline, and trazodone (Figure 2). The top ten therapies accounted for 73% of all 1L treatment approaches, with only risperidone indicated at that time for managing aggression and psychotic symptoms in severe AD. The highest proportion of patients remained on their 3L‐4L therapy after one year; the majority (≥80%) were adherent to these medications across all LoTs. Conclusion There is considerable variability in AAD‐related treatment approaches among Canadian clinicians. Our findings highlight the need for more evidence on treatments specifically indicated for AAD, and education to facilitate evidence‐based clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".