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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".