Deprescribing Opportunities for Hospitalized Patients With End-Stage Kidney Disease on Hemodialysis: A Secondary Analysis of the MedSafer Cluster Randomized Controlled Trial
Bibliographic record
Abstract
Background: End-stage kidney disease patients on dialysis have a substantial risk of polypharmacy due their propensity for comorbidity and contact with the health care system. MedSafer is an electronic decision support tool that integrates patient comorbidity and medication lists to generate personalized deprescribing reports focused on identifying potentially inappropriate medications (PIMs). Objective: To conduct a secondary analysis of patients on regular hemodialysis included in the MedSafer randomized controlled trial to investigate the patterns of polypharmacy and evaluate the efficacy of the MedSafer deprescribing algorithms. Design: Secondary analysis of a cluster randomized clinical trial. Setting: Medical units in 11 acute care hospitals in Canada. Patients: The MedSafer trial enrolled 5698 participants with an expected prognosis of >3 months, age 65 years and older, and on 5 or more daily home medications; 140 participants were receiving chronic hemodialysis. Measurements: The primary outcome of the trial was 30-day adverse drug events (ADEs) post-hospital discharge, and a key secondary outcome was deprescribing. Methods: Control patients received usual care (medication reconciliation), whereas clinicians caring for intervention patients received a MedSafer report that highlighted individualized opportunities for deprescribing. Results: There were 70 patients in each of the control and intervention arms. The median number of home medications was 14 (compared with a median of 10 medications in the general trial population). The most frequent medications observed that were potentially inappropriate were proton pump inhibitors (potentially inappropriate in 55/76 users; 72.4%), diabetes medications in patients with a HBA1C <7.5% (36/65 users; 55.4%), docusate (27/27 users; 100%), gabapentinoids (27/36 users; 75%), and combination antiplatelet/anticoagulants (22/97 users; 22.7%). The proportion of PIMs deprescribed was higher during the intervention phase (28.8% vs 19.3%; absolute increase 9.4% [95% confidence interval 1.3%-17.6%]) compared with the control phase. There was no observed difference in ADEs at 30-day post-discharge between the control and the intervention groups. The most common ADE (n = 3) was gastrointestinal bleeding attributed to antiplatelet agents. Limitations: This was a post hoc exploratory analysis, the original trial did not stratify by hemodialysis status, and the small sample size precludes drawing any definitive conclusions. Conclusion: MedSafer facilitates deprescribing in hospitalized patients on hemodialysis. Larger-scale implementation of decision support software for deprescribing in dialysis and long-term follow-up are likely required to demonstrate an impact on ADEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".