Opioid prescribing practices in chronic kidney disease: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is common, and its management is complex in patients with chronic kidney disease (CKD), but limited data are available on opioid prescribing. We examined opioid prescribing for non-cancer and non-end-of-life care in patients with CKD. METHODS: This was a population-based retrospective cohort study using administrative databases in Ontario, Canada which included adults with CKD defined by an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 from 1 November 2012 to 31 December 2018 and estimated the proportion of opioid prescriptions (type, duration, dose, potentially inappropriate prescribing, etc.) within 1 year of cohort entry. Prescriptions had to precede dialysis, kidney transplant or death. RESULTS: We included 680 445 adults with CKD, and 198 063 (29.1%) were prescribed opioids. Codeine (14.9%) and hydromorphone (7.2%) were the most common opioids. Among opioid users, 24.3% had repeated or long-term use, 26.1% were prescribed high doses and 56.8% were new users. Opioid users were more likely to be female, had cardiac disease or a mental health diagnosis, and had more healthcare visits. The proportions for potentially inappropriate prescribing indicators varied (e.g. 50.1% with eGFR <30 were prescribed codeine, and 20.6% of opioid users were concurrently prescribed benzodiazepines, while 7.2% with eGFR <30 mL/min/1.73 m2 were prescribed morphine, and 7.0% were received more than one opioid concurrently). Opioid prescriptions declined with time (2013 cohort: 31.1% versus 2018 cohort: 24.5%; p <0.0001), as did indicators of potentially inappropriate prescribing. CONCLUSIONS: Opioid use was common in patients with CKD. While opioid prescriptions and potentially inappropriate prescribing have declined in recent years, interventions to improve pain management without the use of opioids and education on safer prescribing practices are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".