Polypharmacy and Potentially Inappropriate Medication Use in Patients with CKD Managed in Canadian Primary Care
Bibliographic record
Abstract
Background: Polypharmacy and the use of potentially inappropriate medications (PIMs) are an increasingly serious public health challenge attributable to aging populations and multimorbidity. This study assessed the prevalence of polypharmacy and use of PIMs in chronic kidney disease (CKD). Methods: A cross-sectional analysis using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database (January 1, 2010 through December 31, 2018). Polypharmacy was defined as the use of ≥ 5 medications, excessive polypharmacy as ≥ 10 medications, and PIMs as medications recommended to be avoided in CKD. Results: The cohort was comprised of 70,331 patients (mean [SD] age, 73.1 [11.4] years; 40,502 [57.6%] female) with CKD stages G3a to G5. The most common chronic conditions were hypertension (60.8%), diabetes (29.4%), and osteoarthritis (25.4%). Overall, the prevalence of polypharmacy and excessive polypharmacy was 91.5% and 74.9%, respectively. The median number of medications was 14 (IQR 9-23). The most commonly prescribed medications were atorvastatin (29.8%), amlodipine (28.9%), and rosuvastatin (27.2%). About 45% of patients with CKD had at least one PIM, 11.1% had two PIMs, and 3.6 % had three or more PIMs. The most commonly prescribed PIMs were metformin (21.7%), nitrofurantoin (16.2%), and rivaroxaban (4.5%). Conclusions: Polypharmacy and use of PIMs are highly prevalent among patients with CKD managed in primary care. These findings highlight opportunities for interventions aimed at improving prescribing practices in the management of CKD. Funding: Government Support - Non-U.S.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".