Post Hoc Analyses of a Randomized Controlled Trial for the Effect of Pharmacist Deprescribing Intervention on the Anticholinergic Burden in Frail Community-Dwelling Older Adults
Bibliographic record
Abstract
Objectives Anticholinergic burden is detrimental to cognitive health. Multiple studies found that a high anticholinergic burden is associated with an increased risk for dementia, changes to the brain structure, function, and cognitive decline. We performed a post hoc analysis of a randomized controlled deprescribing trial. We compared the effect of the intervention on baseline anticholinergic burden across the treatment and control groups and the time of recruitment before and after a lockdown due to the COVID pandemic with subgroup analyses by baseline frailty index. Design Randomized controlled trial. Settings and Participants We analyzed data from a de-prescribing trial of older adults (>65 years) previously conducted in New Zealand that was focused on reducing the Drug Burden Index (DBI). Methods We used the anticholinergic cognitive burden (ACB) to quantify the impact of the intervention on reducing the anticholinergic burden. Participants not taking anticholinergics at the start of the trial were excluded. The primary outcome for this subgroup analysis was a change in ACB, measured with the ĝ Hedges statistic describing the difference in standard deviation units of this change between intervention and control. For this analysis, the trial participants were stratified into low, medium, and high frailty and timing into prior- and post-lockdown (public health measures for COVID-19). Results Among the 295 participants in this analysis, the median (IQR) age was 79 (74, 85), and 67% were women. For the primary outcome ĝ Hedges = −0.04 (95% CI −0.26 to 0.19) with a −0.23 mean reduction in ACB in the intervention arm and −0.19 in the control arm. Before lockdown ĝ Hedges = −0.38 (95% CI −0.84 to 0.04) and post-lockdown ĝ Hedges = 0.07 (95% CI −0.19 to 0.33). The mean change in ACB for each of the frailty strata was as follows: low frailty (−0.02; 95% CI −0.65 to 0.18); medium frailty (0.05; 95% CI −0.28 to 0.38); high frailty (0.08; 95% CI −0.40 to 0.56). Conclusions and Implications The study did not provide evidence for the effect of pharmacist deprescribing intervention on reducing the anticholinergic burden. However, this post hoc analysis examined the impact of COVID on the effectiveness of the intervention, and further research in this area may be warranted.
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 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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".