Predonation Prescription Opioid Use: A Novel Risk Factor for Readmission After Living Kidney Donation
Notice bibliographique
Résumé
Implications of opioid use in living kidney donors for key outcomes, including readmission rates after nephrectomy, are unknown. We integrated Scientific Registry of Transplant Recipients data with records from a nationwide pharmacy claims warehouse and administrative records from an academic hospital consortium to quantify predonation prescription opioid use and postdonation readmission events. Associations of predonation opioid use (adjusted odds ratio [aOR]) in the year before donation and other baseline clinical, procedural, and center factors with readmission within 90 days postdonation were examined by using multivariate logistic regression. Among 14 959 living donors, 11.3% filled one or more opioid prescriptions in the year before donation. Donors with the highest level of predonation opioid use (>305 mg/year) were more than twice as likely as nonusers to be readmitted (6.8% vs. 2.6%; aOR 2.49, 95% confidence interval 1.74–3.58). Adjusted readmission risk was also significantly (p < 0.05) higher for women (aOR = 1.25), African Americans (aOR = 1.45), spouses (aOR = 1.42), exchange participants (aOR = 1.46), uninsured donors (aOR = 1.40), donors with predonation estimated glomerular filtration rate <60 mL/min/1.73 m2 (aOR = 2.68), donors with predonation pulmonary conditions (aOR = 1.54), and after robotic nephrectomy (aOR = 1.68). Predonation opioid use is independently associated with readmission after donor nephrectomy. Future research should examine underlying mechanisms and approaches to reducing risks of postdonation complications. Implications of opioid use in living kidney donors for key outcomes, including readmission rates after nephrectomy, are unknown. We integrated Scientific Registry of Transplant Recipients data with records from a nationwide pharmacy claims warehouse and administrative records from an academic hospital consortium to quantify predonation prescription opioid use and postdonation readmission events. Associations of predonation opioid use (adjusted odds ratio [aOR]) in the year before donation and other baseline clinical, procedural, and center factors with readmission within 90 days postdonation were examined by using multivariate logistic regression. Among 14 959 living donors, 11.3% filled one or more opioid prescriptions in the year before donation. Donors with the highest level of predonation opioid use (>305 mg/year) were more than twice as likely as nonusers to be readmitted (6.8% vs. 2.6%; aOR 2.49, 95% confidence interval 1.74–3.58). Adjusted readmission risk was also significantly (p < 0.05) higher for women (aOR = 1.25), African Americans (aOR = 1.45), spouses (aOR = 1.42), exchange participants (aOR = 1.46), uninsured donors (aOR = 1.40), donors with predonation estimated glomerular filtration rate <60 mL/min/1.73 m2 (aOR = 2.68), donors with predonation pulmonary conditions (aOR = 1.54), and after robotic nephrectomy (aOR = 1.68). Predonation opioid use is independently associated with readmission after donor nephrectomy. Future research should examine underlying mechanisms and approaches to reducing risks of postdonation complications.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».