4CPS-179 The wide review of polypharmacy in the frail older person
Bibliographic record
Abstract
<h3>Background and importance</h3> The WIDE (Wholistic Integrated Deprescribing Evaluation) review is an innovative model of patient-led, pharmacist facilitated medication review. It involves establishing patients‘ priorities and experiences of their medicines, collaborating with primary care providers and evaluating if medicines should be deprescribed because their potential harms outweigh their potential benefits. Frailty is synonymous with vulnerability, including to medication harms. To assess the potential for harm, the WIDE review model incorporates the STOPP/START criteria and the medication appropriateness index (MAI) tools, the use of which have demonstrated improvements in patient outcomes. However, the impact of a patient-led deprescribing model has not yet been studied in this setting. <h3>Aim and objectives</h3> To examine the impact and cost effectiveness of WIDE reviews. <h3>Material and methods</h3> This quantitative prospective cohort study was conducted over 8 weeks. <h3>Inclusion criteria</h3> inpatients aged >65 years and prescribed >5 regular medications who screened positive for frailty (PRISMA 7 score >3). Critically ill patients were excluded. Eligible patients were randomly allocated to the intervention or control group. Regular medications were enumerated and screened using the STOPP/START criteria on admission and discharge. The intervention group received a WIDE review and their MAI score was calculated on admission and discharge. In conjunction with the patients and their consultants, deprescribing plans were devised and communicated to their GPs and community pharmacists. <h3>Results</h3> A total of 20 intervention and 20 control group patients were enrolled. Patient characteristics (age, sex and length of stay) were similar for both groups. A total of 65% of STOPP and 62% of START criteria were addressed in the intervention group versus 12% and 5%, respectively, in the control group. In the intervention group, 83 medications were stopped, 23 doses were reduced and the total MAI score was reduced by 64%. Cost savings to the annual drug budget alone represented a 9:1 return on investment of hospital pharmacist time. Most discontinuations and dose reductions were sustained (98%) and 92% of future recommendations were enacted on 6 months of follow-up. <h3>Conclusion and relevance</h3> Pharmacists performing patient-led WIDE reviews significantly improved medication appropriateness and realised compelling cost savings. A large scale, multi-site study is warranted to demonstrate the reproducibility of these results. <h3>References and/or acknowledgements</h3> No conflict of interest.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".