Implementation of targeted deprescribing of potentially inappropriate medications in patients on hemodialysis
Bibliographic record
Abstract
PURPOSE: Patients on hemodialysis have a high risk of medication-related problems. Studies using deprescribing algorithms to reduce the number of inappropriate medications in this population have been published, but none have used a patient-partnership approach. Our study evaluated the impact of a similar intervention with a patient-partnership approach. METHODS: The objective was to describe the implementation of a pharmacist-led intervention with a patient-partnership approach using deprescribing algorithms and its impact on the reduction of inappropriate medications in patients on hemodialysis. Eight algorithms were developed by pharmacists and nephrologists to assess the appropriateness of medications. Pharmacists identified patients taking targeted medications. Following patient enrollment, pharmacists assessed medications with patients and applied the algorithms. With patient consent, deprescription was suggested to nephrologists if applicable. Specific data on each targeted medication were collected at 4 and 16 weeks. Descriptive statistics were used to examine the effects of the deprescribing intervention. RESULTS: Of 270 patients, 256 were taking at least one targeted medication. Of the 122 patients taking at least one targeted medication who were approached to participate, 66 were included in the study. At enrollment, these patients were taking 252 targeted medications, of which 59 (23.4%) were determined to be inappropriate. Deprescription was initiated for 35 of these 59 medications (59.3%). At 4 weeks, 33 of the 59 medications (55.9%) were still deprescribed, while, at 16 weeks, 27 of the 59 medications (45.8%) were still deprescribed. Proton pump inhibitors and benzodiazepines or Z-drugs were the most common inappropriate medications, and allopurinol was the most deprescribed medication. CONCLUSION: A pharmacist-led intervention with a patient-partnership approach and using deprescribing algorithms reduced the number of inappropriate medications in patients on hemodialysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".