Electronic Decision Support for Deprescribing in Patients on Hemodialysis: Clinical Research Protocol for a Prospective, Controlled, Quality Improvement Study
Bibliographic record
Abstract
Background: Patients on dialysis are commonly prescribed multiple medications (polypharmacy), many of which are potentially inappropriate medications (PIMs). Potentially inappropriate medications are associated with an increased risk of falls, fractures, and hospitalization. MedSafer is an electronic tool that generates individualized, prioritized reports with deprescribing opportunities by cross-referencing patient health data and medications with guidelines for deprescribing. Objectives: Our primary aim was to increase deprescribing, as compared with usual care (medication reconciliation or MedRec), for outpatients receiving maintenance hemodialysis, through the provision of MedSafer deprescribing opportunity reports to the treating team and patient empowerment deprescribing brochures provided directly to the patients themselves. Design: This controlled, prospective, quality improvement study with a contemporary control builds on existing policy at the outpatient hemodialysis centers where biannual MedRecs are performed by the treating nephrologist and nursing team. Setting: The study takes place on 2 of the 3 outpatient hemodialysis units of the McGill University Health Centre in Montreal, Quebec, Canada. The intervention unit is the Lachine Hospital, and the control unit is the Montreal General Hospital. Patients: A closed cohort of outpatient hemodialysis patients visit one of the hemodialysis centers multiple times per week for their hemodialysis treatment. The initial cohort of the intervention unit includes 85 patients, whereas the control unit has 153 patients. Patients who are transplanted, hospitalized during their scheduled MedRec, or die before or during the MedRec will be excluded from the study. Measurements: We will compare rates of deprescribing between the control and intervention units following a single MedRec. On the intervention unit, MedRecs will be paired with MedSafer reports (the intervention), and on the control unit, MedRecs will take place without MedSafer reports (usual care). On the intervention unit, patients will also receive deprescribing patient empowerment brochures for select medication classes (gabapentinoids, proton-pump inhibitors, sedative hypnotics and opioids for chronic non-cancer pain). Physicians on the intervention unit will be interviewed post-MedRec to determine implementation barriers and facilitators. Methods: The primary outcome will be the proportion of patients with 1 or more PIMs deprescribed on the intervention unit, as compared with the control unit, following a biannual MedRec. This study will build on existing policies aimed at optimizing medication therapy in patients undergoing maintenance hemodialysis. The electronic deprescribing decision support tool, MedSafer, will be tested in a dialysis setting, where nephrologists are regularly in contact with patients. MedRecs are an interdisciplinary clinical activity performed biannually on the hemodialysis units (in the Spring and Fall), and within 1 week following discharge from any hospitalization. This study will take place in the Fall of 2022. Semi-structured interviews will be conducted among physicians on the intervention unit to determine barriers and facilitators to implementation of the MedSafer-supplemented MedRec process and analyzed according to grounded theory in qualitative research. Limitations: Deprescribing can be limited due to nephrologists' time constraints, cognitive impairment of the hemodialyzed patient stemming from their illness and complex medication regimens, and lack of sufficient patient resources to learn about the medications they are taking and their potential harms. Conclusions: Electronic decision support can facilitate deprescribing for the clinical team by providing a nudge reminder, decreasing the time it takes to review and effectuate guideline recommendations, and by lowering the barrier of when and how to taper. Guidelines for deprescribing in the dialysis population have recently been published and incorporated into the MedSafer software. To our knowledge, this will be the first study to examine the efficacy of pairing these guidelines with MedRecs by leveraging electronic decision support in the outpatient dialysis population. Trial registration: This study was registered on Clinicaltrials.gov (NCT05585268) on October 2, 2022, prior to the enrolment of the first participant on October 3, 2022. The registration number is pending at the time of protocol submission.
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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 | 0.052 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".