Escitalopram for Agitation in Alzheimer’s Dementia
Bibliographic record
Abstract
Abstract Background Agitation is a common and disabling symptom of Alzheimer’s dementia (AD). Pharmacological treatments are recommended if agitation is not responsive to psychosocial intervention. Citalopram was effective in treating agitation in AD but was associated with cognitive and cardiac risks linked to its R‐ but not S‐enantiomer. Primary objective of the Escitalopram for Agitation in Alzheimer’s Disease (S‐CitAD) study was to examine the efficacy and safety of escitalopram, in combination with PSI, for the treatment of agitation in patients with AD who fail to respond to psychosocial intervention. A secondary objective was to define groups with agitation most likely to benefit from escitalopram. Method S‐CitAD is an NIH‐funded, multicenter, community‐based, phase 3, double‐masked randomized placebo‐controlled trial. Assessments were conducted at enrollment, following three weeks of psychosocial intervention (baseline), and then weeks 1‐4, 6, 7, 9, 10 and 12. Participants were adults with AD, a telephone Mini‐Mental State Examination Telephone score from 3 to 20, and clinically significant agitation/aggression as assessed by the Neuropsychiatric Inventory with either frequency being “Very frequently”, or frequency being “Frequently” and severity being “Moderate” or “Marked”. Following three weeks of psychosocial intervention alone, participants not showing response were randomized (1:1) to receive escitalopram at a target dose of 15mg/day or placebo for 12 weeks while continuing PSI. Participants who responded to three weeks of psychosocial intervention alone continued to receive the intervention and were monitored on usual care through week 12. Primary outcome was the proportion of participants with clinically significant improvement in agitation from baseline on the modified Alzheimer’s Disease Cooperative Study ‐ Clinical Global Impression of Change (mADCS‐CGIC). Result Two hundred and five (N = 205) participants were enrolled, and 173 were randomized to escitalopram or placebo for 12 weeks while continuing psychosocial intervention. The last participant will complete the study in late winter and mask will be broken in March 2024. Outcomes will be first presented at the AAIC meeting. Conclusion As escitalopram is a most commonly used treatment for disruptive behavior in dementia, the outcomes whether positive or negative will have major clinical impact.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".