Association Between Sex and Opiate and Benzodiazepine Prescription Among Patients With CKD: Research Letter
Bibliographic record
Abstract
Background: Opiate and benzodiazepine use is associated with increased mortality and poorer transplant outcomes in patients with chronic kidney disease (CKD). Objective: To determine the predictors of opiate and benzodiazepine prescription in people with kidney disease. Design: Cross-sectional, observational study. Setting: Outpatient clinics at Kingston Health Sciences Centre or at affiliated sites as of June 2017. Patients: Individuals with CKD being treated at clinics or with various dialysis modalities at Kingston Health Sciences Centre and affiliated sites. Measurements: The total number of regular opioid and benzodiazepine prescriptions was recorded for each patient. Patients were stratified based on clinical (eg, dialysis modality) and demographic (sex, age, diabetes mellitus [DM], ethnicity) characteristics, as elicited below. Methods: We evaluated opiate and benzodiazepine use by chart review in the following patient groups: conventional hemodialysis (HD) (n = 359), home hemodialysis (HHD) (n = 21), peritoneal dialysis (PD) (n = 95), patients attending the multidisciplinary chronic kidney disease clinic (MCKDC) (n = 322), and kidney transplant (KT) recipients (n = 176). Opiates and benzodiazepines were classified according to the American Hospital Formulary Service system. Patients were also stratified as white (n = 855), indigenous (n = 66), or all others (n = 48). Results: The mean age was 66.2 ± 14.9 years, 602 (61.9%) were men, and 439 (45.1%) had DM. Opiates were prescribed to 223 patients (22.9%), most frequently to HD (32.3%), followed by MCKDC (20.8%), HHD (19.0%), PD (14.7%), and KT (12.5%) ( P < .001). The independent predictors of opiate prescription included DM (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.4-2.6; P < 0.001), conventional HD (vs all other treatment modalities) (OR, 1.8; 95% CI, 1.3-2.5; P < .001), and female sex (OR, 1.4; 95% CI, 1.0-1.9; P = .041) after adjustment for age and ethnicity ( R 2 = 0.037, P < .001). Benzodiazepines were prescribed to 106 patients (10.9%), most frequently to HD (15.9%), followed by HHD (9.5%), KT (9.1%), MCKDC (7.5%), and PD (7.4%) ( P = .005). The independent predictors of benzodiazepine use included female sex (OR, 2.3; 95% CI, 1.5-3.4; P < .001) and dialysis modality (excluding MCKDC and KT) (OR, 1.8; 95% CI, 1.2-2.8; P = .006) after adjustment for ethnicity, DM, and age ( R 2 = 0.027, P < .001). Limitations: We were not able to ascertain the indication for prescription of these drugs or patient adherence. Conclusions: Women with kidney disease are significantly more likely to be prescribed opiates and benzodiazepines than men with kidney disease. Further research is required to determine whether these medications contribute to increased morbidity and mortality in women with kidney disease. Trial Registration: This manuscript does not meet the criteria for requiring registration or a statement of written consent from study participants. The previous submission of this manuscript already made mention of Research Ethics Board approval.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".