Pain and Analgesic Use in Patients With Chronic Kidney Disease Not on Dialysis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic pain is common in patients with chronic kidney disease (CKD), yet pain management in non-dialysis-dependent CKD (NDD-CKD) is underexplored. Inappropriate analgesic use poses significant risks in this population. OBJECTIVE: To evaluate patterns of analgesic use-specifically opioids and NSAIDs-and associated clinical characteristics in patients with NDD-CKD. METHODS: A systematic review was conducted following PRISMA 2020 guidelines. Databases including PubMed and ClinicalTrials.gov were searched in December 2024 using MeSH terms related to CKD, analgesics, opioids, and NSAIDs. Inclusion criteria targeted NDD-CKD patients with reported analgesic use. Data extraction and risk of bias assessments were performed independently by two reviewers. RESULTS: Nine studies encompassing 3,674,959 patients were included. Opioid use was reported in 324,111 patients (22.8%), while NSAIDs were used in 1,095,052 (77.1%). Opioid use increased with CKD severity and pain intensity, but was associated with higher mortality, especially in frail or comorbid patients. NSAID use was prevalent in early-stage CKD and associated with nephrotoxic risk and may occur without clinician oversight. Regional variation and inconsistent prescribing practices were noted. No study directly compared opioid vs. NSAID outcomes. CONCLUSION: Analgesic use in NDD-CKD is widespread and varies by region, CKD stage, and pain severity. Inadequate pain control is common. Standardized guidelines tailored to CKD patients are urgently needed to optimize pain management while minimizing harm.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".