Prescription opioid use before and after kidney transplant: Implications for posttransplant outcomes
Notice bibliographique
Résumé
Evolving literature suggests that the epidemic of prescription opioid use affects the transplant population. We examined a novel database wherein national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007-2015) to characterize prescription opioid use before and after kidney transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 75 430 eligible patients, 43.1% filled opioids in the year before transplant. Use was more common among recipients who were women, white, unemployed, publicly insured, and with longer pretransplant dialysis. Of those with the highest level of pretransplant opioid use, 60% continued high-level use posttransplant. Pretransplant opioid use had graded associations with one-year posttransplant outcomes; the highest-level use predicted 46% increased risk of death (aHR 1.281.461.66) and 28% increased risk of all-cause graft failure (aHR 1.171.281.41). Effects of high-level opioid use in the first year after transplant were stronger, predicting twice the risk of death (aHR 1.932.242.60) and 68% higher all-cause graft failure risk (aHR 1.501.681.89) over the subsequent year; increased risk persisted over five years. While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients. Evolving literature suggests that the epidemic of prescription opioid use affects the transplant population. We examined a novel database wherein national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007-2015) to characterize prescription opioid use before and after kidney transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 75 430 eligible patients, 43.1% filled opioids in the year before transplant. Use was more common among recipients who were women, white, unemployed, publicly insured, and with longer pretransplant dialysis. Of those with the highest level of pretransplant opioid use, 60% continued high-level use posttransplant. Pretransplant opioid use had graded associations with one-year posttransplant outcomes; the highest-level use predicted 46% increased risk of death (aHR 1.281.461.66) and 28% increased risk of all-cause graft failure (aHR 1.171.281.41). Effects of high-level opioid use in the first year after transplant were stronger, predicting twice the risk of death (aHR 1.932.242.60) and 68% higher all-cause graft failure risk (aHR 1.501.681.89) over the subsequent year; increased risk persisted over five years. While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients.
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 | 0,000 |
| 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 ».