Prevalence of Polypharmacy and Associated Adverse Health Outcomes in Patients with CKD: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Patients with chronic kidney disease (CKD) are at increased risk of adverse health outcomes associated with excessive medication use (polypharmacy) due to impaired kidney function and multimorbidity. However, data on the associations of polypharmacy and adverse health outcomes in this population are limited. We conducted a systematic review and meta-analysis to determine the prevalence of polypharmacy and its associated health consequences in CKD. Methods: The study was conducted using a pre-specified study protocol and adheres to PRISMA reporting guidelines. Six electronic databases were searched from inception to September 2020 for studies that included patients with CKD, use of polypharmacy, and associated adverse health outcomes. Random effects models were used to pool the prevalence of polypharmacy and associations with health outcomes. Results: 53 eligible articles (n = 477,909 patients) met criteria for inclusion. The pooled prevalence of polypharmacy and excessive polypharmacy was 76.2% (95% CI 73.2%-79.1%; range 14.9% to 100%) and 37.4% (95% CI 30.0%-45.2%; range 11.4% to 63.0%), respectively (Figure 1). The prevalence of polypharmacy was 72.7% and 87.1% in non-dialysis CKD and dialysis populations, respectively. 17 studies reported significant associations between polypharmacy and adverse health outcomes. These studies found an increased risk for potentially inappropriate medication use, drug-drug interactions, drug-related problems, medication-related problems, adverse drug reactions, decreased quality of life, decreased kidney function, hospitalization, and mortality. Conclusions: Polypharmacy is common in CKD and linked to adverse health outcomes. Our findings highlight the need for improved prescribing practices in CKD and the development of strategies to reduce polypharmacy. 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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".