Potentially inappropriate prescribing in older adults with advanced chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Older adults with chronic kidney disease (CKD) are at heightened risk for polypharmacy. We examined potentially inappropriate prescribing in this population and whether introducing pharmacists into the ambulatory kidney care model was associated with improved prescribing practices. METHODS: Retrospective cohort study using linked administrative databases. We included patients with an eGFR ≤30 mL/min/1.73 m2 ≥66 years of age followed in multidisciplinary kidney clinics in Ontario, Canada (n = 25,016 from 28 centres). The primary outcome was the absence of a statin prescription or the receipt of a potentially inappropriate prescription defined by the American Geriatric Society Beers Criteria® and a modified Delphi panel that identified key drugs of concern in CKD. We calculated the crude cumulative incidence and incidence rate for the primary outcome and used change-point regression to determine if a change occurred following pharmacist introduction. RESULTS: There were 6,007 (24%) and 16,497 patients (66%) not prescribed a statin and with ≥1 potentially inappropriate prescription, respectively. The rate of potentially inappropriate prescribing was 125.6 per 100 person-years and was higher in more recent years. The change-point regression analysis included 2,275 patients from two centres. No immediate change was detected at pharmacist introduction, but potentially inappropriate prescribing was increasing pre-pharmacist introduction, and this rising trend was reversed post-pharmacist introduction. The incidence of potentially inappropriate prescribing still remained high post-pharmacist introduction. CONCLUSIONS: Potentially inappropriate prescribing practices were common. Incorporating pharmacists into the kidney care model may improve prescribing practices. The role of pharmacists in the ambulatory kidney care team warrants further investigation in a randomized controlled trial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".