Prevalence and Management of Drug-Related Problems in Chronic Kidney Disease Patients by Severity Level: A Subanalysis of a Cluster Randomized Controlled Trial in Community Pharmacies
Bibliographic record
Abstract
BACKGROUND: Drug-related problems (DRPs) are prevalent among chronic kidney disease (CKD) patients. However, little is known about their severity and management by community pharmacists. OBJECTIVES: To (a) describe the prevalence of DRPs by severity level in CKD patients and (b) assess the effect of a training-and-communication network program in nephrology (ProFiL) on these DRPs. METHODS: This is a secondary analysis of a cluster randomized controlled trial evaluating the effect of the ProFiL-program. In 6 CKD clinics, patients at CKD stage 3 or 4 and their community pharmacists were recruited and assigned to the ProFiL group or a usual care (UC) group. Using validated criteria, 2 pharmacists identified DRPs and assessed their severity at baseline and after 12 months. The mean annual change in the number of DRPs per patient by severity level was assessed using a 2-level multivariable linear mixed-effects model. RESULTS: A total of 494 pharmacists and 442 patients participated. At baseline, the prevalence (mean number of DRPs per patient [SD]) of mild DRPs (e.g., requiring dosage adjustment) and moderate DRPs (e.g., drug adherence requiring a monitoring plan) were 0.55 (0.98) and 1.04 (1.51), respectively. After 12 months, an unadjusted incremental annual reduction of 0.34 moderate DRPs (95% CI = -0.66 to -0.01) was observed in the ProFiL group compared with the UC group. After adjustment, no between-group differences were observed. CONCLUSIONS: Among patients followed in CKD clinics, most DRPs have a moderate severity requiring specific monitoring by pharmacists. The benefit of continuing education programs, such as ProFiL, to reduce moderate DRPs remains to be determined. DISCLOSURES: This study was supported by the Canadian Institutes of Health Research (grant number: MOP-230207). Part of the study was also funded by Pfizer Canada, Leo Pharma, and Amgen. The authors declare that they have no relevant financial interests. Study concept and design were contributed by Quintana-Bárcena, Lord, and Lalonde. Quintana-Bárcena, Lord, and Lizotte were responsible for the data analysis, and Quintana-Bárcena and Berbiche performed the statistical analysis. The manuscript was written by Quintana-Bárcena and Lalonde and revised by Quintana-Bárcena and Lalonde, along with the other authors.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| 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) | 0.003 | 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".