Population-Based Analysis of Nonsteroidal Anti-inflammatory Drug Prescription in Subjects With Chronic Kidney Disease
Bibliographic record
Abstract
Background: Pain is a prevalent symptom experienced by patients with chronic kidney disease (CKD) and appropriate management of pain is an important element of comprehensive care. Nonsteroidal anti-inflammatory drugs (NSAID) are known to be nephrotoxic in persons with CKD. Objective: This study examined the pattern of NSAID prescribing practices in a population based-cohort of patients with CKD. Design: Retrospective cohort study using linked population-based health care data. Setting: Entire province of Alberta, Canada. Participants: All adults in Alberta with eGFR defined CKD G3 or greater between 2009 and 2017 were included. Measurements: CKD was defined using at least 2 outpatient serum creatinine (SCr) greater than 90 days apart; the date of second SCr measurement was used as index date. We determined the incidence of hyperkalemia using the peak serum potassium. Prescription drug information was obtained from the Pharmaceutical Information Network (PIN) database. Methods: All patients were followed from the index date until March 31, 2019, with a minimum follow-up of 2 years. Prescription drug information and the follow-up laboratory testing of serum creatinine and serum potassium were obtained. Patients with kidney failure defined as eGFR < 15 mL/min per 1.73 m 2 , receiving chronic dialysis, or prior kidney transplant at baseline were excluded. Results: A total of 170 574 adults (mean age 76.3; 44% male) with CKD were identified and followed for a median of 7 years; 27% were dispensed at least 1 NSAID prescription. While there was a trend toward fewer prescriptions in patients with more advanced CKD ( P < .001), 16% of those with CKD G4 were prescribed an NSAID. Primary care providers provided 79% of the prescriptions. Among NSAID users, 21% had a follow-up serum creatinine (SCr) within 30 days of the index prescription. Limitations: Data collected were from clinical and administrative databases not created for research purposes. The study cohort is limited to subjects who sought medical care and had a serum creatinine measurement obtained. Measurement of NSAID use is limited to those who were dispensed a prescription, over-the-counter NSAIDs use is not captured. Conclusions: Despite guidelines advocating cautious use of NSAIDs in patients with CKD, this study indicates that there is a discrepancy from best practice recommendations. Effective strategies to better support and educate prescribers, as well as patients, may help reduce inappropriate prescribing and adverse events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".