P4‐222: INTEGRATED CARE PATHWAY FOR AGITATION IN ALZHEIMER'S DEMENTIA
Bibliographic record
Abstract
Most patients with Alzheimer's disease (AD) experience agitation at some point in their illness. Agitation and other neuropsychiatric symptoms are associated with variation and inappropriate use of medications. To address these issues we designed an integrated care pathway (ICP) that standardizes assessments and ensures measurement-based treatment, use of non-pharmacological interventions, and a sequential pharmacological algorithm. Here we report outcomes from the implementation of the ICP at an inpatient unit and a long-term care home (LTCH). Patients diagnosed with Dementia of AD or mixed AD + vascular type and clinically significant agitation were enrolled and assessed using Cohen Mansfield Agitation Inventory–Frequency (CMAI-F) and Neuropsychiatric Inventory Questionnaire (NPI-Q) as well as for polypharmacy and length of treatment episode. ICP outcomes from its first three years of implementation were also compared to treatment-as-usual (TAU) in the three years prior to the ICP. Finally, we implemented the ICP at a LTCH and compared it also to TAU. 55 patients completed the inpatient ICP. CMAI-F and NPI-Q significantly decreased. 12.7% of patients improved after just cleaning up psychotropic agents, 80% improved with only one medication, and 7.3 % required polypharmacy. Comparing the ICP to TAU, patients in both groups achieved clinical improvement. Mean length of treatment episode in TAU was significantly longer than that in the ICP group. The proportion of patients treated with polypharmacy was more than 4-fold higher in TAU than in the ICP. In LTCH, 18 residents completed the ICP. NPI-Q severity and distress scores significantly decreased. 17% patients were treated without medications, 82% with one medication, and 11% with polypharmacy. Comparing them with 18 residents treated with TAU, mean length of treatment episode in TAU was significantly longer than that in the ICP. The proportion of patients treated with polypharmacy was 4-fold higher in TAU than in the ICP. An algorithmic approach is efficacious in treating agitation in AD with low rates of polypharmacy. Building on these results, we are now testing the ICP in a multisite randomized controlled trial in Canada which design and rationale we will present at the meeting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".