A292 IMPACT OF SIROLIMUS PROTEINURIA FOLLOWING LIVER TRANSPLANTATION
Notice bibliographique
Résumé
Abstract Background Sirolimus (Sr) is a potent immunosuppressant used in liver transplant recipients to prevent rejection in settings of calcineurin inhibitor toxicity and in transplanted hepatocellular carcinoma (HCC) patients. Sr can cause proteinuria, which can lead to poor renal function and survival. The significance of proteinuria is poorly understood, with a lack of studies assessing its risk factors and their impact on clinical outcomes. Aims We evaluated the incidence of proteinuria and its impact on clinical outcomes among liver transplant (LT) recipients who were Sr users compared with non-sirolimus (nonSr) users. Methods We analyzed patients with their first LT between 2001 and 2020. Data were gathered from Organ Transplant Tracking records and chart reviews. Sr users received Sr for at least 6 consecutive months in the first year post-LT. We studied demographics, pre-LT comorbidities, and immunosuppression use. We evaluated the development of proteinuria, renal dysfunction, and new comorbidities including features of metabolic syndrome and cardiovascular disease. Data on post-LT infections, graft rejection, and patient survival were collected. Results We analyzed 359 Sr and 762 non-Sr users (73.5% vs. 65% male). The average ages of Sr and non-Sr users were 54.9±9 and 51.7±11.6 years, respectively (pampersand:003C0.001). Of Sr users, 40.4% had HCC (pampersand:003C0.001). Among non-Sr users, 95% were on tacrolimus and 67.8% were on mycophenolate. Higher frequency of pre-LT hypertension (HTN) was observed in Sr users (27.2% vs. 18.2%; pampersand:003C0.001). There were no differences in pre-LT chronic kidney disease (CKD), cardiovascular disease (CVD), dyslipidemia (DLD), diabetes mellitus (DM) prevalence, creatinine levels, or proteinuria. Sr users had a higher incidence of proteinuria (13.1% vs. 7.9%, p=0.006). Protein-creatinine ratios and albumin-creatinine ratios 12 months post-LT were not significantly different in Sr and non-Sr users ([40.1±105.6 vs. 57.5±169.9 mg/g, p=0.39], and [56.2±101.4 vs. 54.6±166.8 mg/g, p=0.268], respectively). Pre-LT creatinine (OR1.5; pampersand:003C 0.001) and DM (OR2.7; pampersand:003C 0.001) were associated with an increased risk of proteinuria. Sr users had higher (pampersand:003C0.001) incidence of post-LT CVD (24% vs. 14.8%), DM (20.4% vs. 12.6%), HTN (41.5% vs. 30.3%), and DLD (41.3% vs. 20.3%). There was no difference in post-LT CKD between Sr vs non-Sr (41% vs. 47%; p =0.119). Sr users had a lower frequency of post-LT infection (43.1% vs. 53%, p=0.007). Higher incidence of graft rejection was seen in Sr users (43.5% vs 27.8%, pampersand:003C0.001). There was no difference in median graft (15.2 ± 0.40 vs. 14.4±0.30 years; p = 0.681) or patient survival (12.5±0.44 vs. 12.5±0.33 years; p = 0.536). Conclusions Although Sr users were less likely to develop CKD post-LT, they had significantly higher rates of proteinuria, CVD, HTN, and DLD. However, no significant difference was observed in graft or patient survival rates. Funding Agencies None
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 | 0,001 | 0,003 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 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 ».