Deprescribing Anticholinergic and Sedative Drugs to Reduce Polypharmacy in Frail Older Adults Living in the Community: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Polypharmacy is associated with poor outcomes in older adults. Targeted deprescribing of anticholinergic and sedative medications may improve health outcomes for frail older adults. Our pharmacist-led deprescribing intervention was a pragmatic 2-arm randomized controlled trial stratified by frailty. We compared usual care (control) with the intervention of pharmacists providing deprescribing recommendations to general practitioners. METHODS: Community-based older adults (≥65 years) from 2 New Zealand district health boards were recruited following a standardized interRAI needs assessment. The Drug Burden Index (DBI) was used to quantify the use of sedative and anticholinergic medications for each participant. The trial was stratified into low, medium, and high-frailty. We hypothesized that the intervention would increase the proportion of participants with a reduction in DBI ≥ 0.5 within 6 months. RESULTS: Of 363 participants, 21 (12.7%) in the control group and 21 (12.2%) in the intervention group had a reduction in DBI ≥ 0.5. The difference in the proportion of -0.4% (95% confidence interval [CI]: -7.9% to 7.0%) provided no evidence of efficacy for the intervention. Similarly, there was no evidence to suggest the effectiveness of this intervention for participants of any frailty level. CONCLUSION: Our pharmacist-led medication review of frail older participants did not reduce the anticholinergic/sedative load within 6 months. Coronavirus disease 2019 (COVID-19) lockdown measures required modification of the intervention. Subgroup analyses pre- and post-lockdown showed no impact on outcomes. Reviewing this and other deprescribing trials through the lens of implementation science may aid an understanding of the contextual determinants preventing or enabling successful deprescribing implementation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".