Medication Deprescribing in Patients Receiving Hemodialysis: A Prospective Controlled Quality Improvement Study
Bibliographic record
Abstract
Rationale & Objective Patients treated with dialysis are commonly prescribed multiple medications (polypharmacy), including some potentially inappropriate medications (PIMs). PIMs are associated with an increased risk of medication harm (eg, falls, fractures, hospitalization). Deprescribing is a solution that proposes to stop, reduce, or switch medications to a safer alternative. Although deprescribing pairs well with routine medication reviews, it can be complex and time-consuming. Whether clinical decision support improves the process and increases deprescribing for patients treated with dialysis is unknown. This study aimed to test the efficacy of the clinical decision support software MedSafer at increasing deprescribing for patients treated with dialysis. Study Design Prospective controlled quality improvement study with a contemporaneous control. Setting & Participants Patients prescribed≥5 medications in 2 outpatient dialysis units in Montréal, Canada. Exposures Patient health data from the electronic medical record were input into the MedSafer web-based portal to generate reports listing candidate PIMs for deprescribing. At the time of a planned biannual medication review (usual care), treating nephrologists in the intervention unit additionally received deprescribing reports, and patients received EMPOWER brochures containing safety information on PIMs they were prescribed. In the control unit, patients received usual care alone. Analytical Approach The proportion of patients with≥1 PIMs deprescribed was compared between the intervention and control units following a planned medication review to determine the effect of using MedSafer. The absolute risk difference with 95% CI and number needed to treat were calculated. Results In total, 195 patients were included (127, control unit; 68, intervention unit); the mean age was 64.8±15.9 (SD), and 36.9% were women. The proportion of patients with≥1 PIMs deprescribed in the control unit was 3.1% (4/127) vs 39.7% (27/68) in the intervention unit (absolute risk difference, 36.6%; 95% CI, 24.5%-48.6%; P <0.0001; number needed to treat=3). Limitations This was a single-center nonrandomized study with a type 1 error risk. Deprescribing durability was not assessed, and the study was not powered to reduce adverse drug events. Conclusions Deprescribing clinical decision support and patient EMPOWER brochures provided during medication reviews could be an effective and scalable intervention to address PIMs in the dialysis population. A confirmatory randomized controlled trial is needed. Registration NCT05585268. Plain-Language Summary Patients treated with dialysis are commonly prescribed multiple medications, some of which are potentially inappropriate medications (PIMs). PIMs can increase a patient's pill burden and are associated with an increased risk of harm (some examples include falls, fractures, and hospitalization). Deprescribing is a proposed solution that aims to highlight medications that can be stopped, reduced, or switched to a safer option, under supervision of a health care provider. We aimed to determine if a quality improvement intervention in the dialysis unit could increase deprescribing compared to usual care. The study took place in 2 outpatient hemodialysis units where usual care involves nurses and nephrologists performing medication reviews twice a year. The intervention was a deprescribing report that was generated with the help of a software tool called MedSafer, along with brochures for patients with information on PIMs they were taking. In the intervention unit, we increased the number of patients who had a medication safely deprescribed by 36.6% more than on the control unit. Although the study was small, a future larger study in dialysis patients might show that a computer software such as MedSafer can prevent harmful complications from taking too many medications.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".