Analgesic Use in Patients With Advanced Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Pain is common in patients with chronic kidney disease (CKD). Analgesics may be appropriate for some CKD patients. OBJECTIVES: To determine the prevalence of overall analgesic use and the use of different types of analgesics including acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), adjuvants, and opioids in patients with CKD. DESIGN: Systematic review and meta-analysis. SETTING: Interventional and observational studies presenting data from 2000 or later. Exclusion criteria included acute kidney injury or studies that limited the study population to a specific cause, symptom, and/or comorbidity. PATIENTS: Adults with stage 3-5 CKD including dialysis patients and those managed conservatively without dialysis. MEASUREMENTS: Data extracted included title, first author, design, country, year of data collection, publication year, mean age, stage of CKD, prevalence of analgesic use, and the types of analgesics prescribed. METHODS: statistic was computed to measure heterogeneity. Random-effects models were used to account for variations in study design and sample populations, and a double arcsine transformation of the prevalence variables was used to accommodate potential overweighting of studies with very large or very small prevalence measurements. Sensitivity analyses were performed to determine the magnitude of publication bias and assess possible sources of heterogeneity. RESULTS: Forty studies were included in the analysis. The prevalence of overall analgesic use in the random-effects model was 50.8%. The prevalence of acetaminophen, NSAIDs, and adjuvant use was 27.5%, 17.2%, and 23.4%, respectively, while the prevalence of opioid use was 23.8%. Due to the possibility of publication bias, the actual prevalence of acetaminophen use in patients with advanced CKD may be substantially lower than this meta-analysis indicates. A trim-and-fill analysis decreased the pooled prevalence estimate of acetaminophen use to 5.4%. The prevalence rate for opioid use was highly influenced by 2 large US studies. When these were removed, the estimated prevalence decreased to 17.3%. LIMITATIONS: There was a lack of detailed information regarding the analgesic regimen (such as specific analgesics used within each class and inconsistent accounting for patients on multiple drugs and the use of over-the-counter analgesics such as acetaminophen and NSAIDs), patient characteristics, type of pain being treated, and the outcomes of treatment. Data on adjuvant use were very limited. These results, therefore, must be interpreted with caution. CONCLUSIONS: There was tremendous variability in the prescribing patterns of both nonopioid and opioid analgesics within and between countries suggesting widespread uncertainty about the optimal pharmacological approach to treating pain. Further research that incorporates robust reporting of analgesic regimens and links prescribing patterns to clinical outcomes is needed to guide optimal clinical practice.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".