Association Between Sex and Opiate and Benzodiazepine Prescription Among Patients With CKD: Research Letter
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».