Impact of a Comprehensive Intervention Bundle Including the Drug Burden Index on Deprescribing Anticholinergic and Sedative Drugs in Older Acute Inpatients: A Non-randomised Controlled Before-and-After Pilot Study
Bibliographic record
Abstract
INTRODUCTION: Implementation of the Drug Burden Index (DBI) as a risk assessment tool in clinical practice may facilitate deprescribing. OBJECTIVE: The purpose of this study is to evaluate how a comprehensive intervention bundle using the DBI impacts (i) the proportion of older inpatients with at least one DBI-contributing medication stopped or dose reduced on discharge, compared with admission; and (ii) the changes in deprescribing of different DBI-contributing medication classes during hospitalisation. METHODS: This before-and-after study was conducted in an Australian metropolitan tertiary referral hospital. Patients aged ≥ 75 years admitted to the acute aged care service for ≥ 48 h from December 2020 to October 2021 and prescribed DBI-contributing medication were included. During the control period, usual care was provided. During the intervention, access to the intervention bundle was added, including a clinician interface displaying DBI score in the electronic medical record. In a subsequent 'stewardship' period, a stewardship pharmacist used the bundle to provide clinicians with patient-specific recommendations on deprescribing of DBI-contributing medications. RESULTS: Overall, 457 hospitalisations were included. The proportion of patients with at least one DBI-contributing medication stopped/reduced on discharge increased from 29.9% (control period) to 37.5% [intervention; adjusted risk difference (aRD) 6.5%, 95% confidence intervals (CI) -3.2 to 17.5%] and 43.1% (stewardship; aRD 12.1%, 95% CI 1.0-24.0%). The proportion of opioid prescriptions stopped/reduced rose from 17.9% during control to 45.7% during stewardship (p = 0.04). CONCLUSION: Integrating a comprehensive intervention bundle and accompanying stewardship program is a promising strategy to facilitate deprescribing of sedative and anticholinergic medications in older inpatients.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".