The clinical utility and safety of biomarker-guided immunosuppression withdrawal in liver transplantation: the LIFT prospective RCT
Notice bibliographique
Résumé
Background Long-term surviving liver transplant recipients can spontaneously develop operational tolerance, which allows them to completely discontinue their immunosuppression, but we lack validated tools to predict the likelihood of rejection following immunosuppression withdrawal. A previous clinical trial showed that a logistic regression algorithm including the transcript levels of a set of five genes in a liver biopsy could predict the success of immunosuppression withdrawal with high sensitivity and specificity. Objective To determine if the use of a liver tissue transcriptional test of tolerance to stratify liver recipients prior to immunosuppression withdrawal accurately identifies operationally tolerant recipients and reduces the incidence of rejection, as compared with a control group in whom immunosuppression withdrawal is performed without stratification. Design and methods Prospective, multicentric, phase IV, biomarker-strategy design trial with a randomised control group in which adult liver transplant recipients were randomised 1 : 1 to either: (1) non-biomarker-based immunosuppression weaning (Arm A); or (2) biomarker-based immunosuppression weaning (Arm B). Setting and participants Adult liver transplant recipients ≥ 3 years post transplant (≥ 6 years if age ≤ 50 years old) with no history of autoimmunity or recent episodes of rejection, normal allograft function, and no significant histological abnormalities in a baseline screening liver biopsy, recruited from 12 transplant units in United Kingdom, Germany, Belgium and Spain. Intervention Enrolled patients underwent a screening liver biopsy to exclude the presence of subclinical allograft damage. Eligible participants randomised to Arm A underwent gradual discontinuation of immunosuppression. Among participants allocated to Arm B, only those found to be biomarker-positive were offered immunosuppression withdrawal, while biomarker-negative participants remained on their baseline immunosuppression. Patients who completely discontinued immunosuppression and maintained stable allograft function underwent protocol liver biopsies at 12 and 24 months after immunosuppression withdrawal. Main outcome measure Development of operational tolerance, defined as the successful discontinuation of immunosuppression with maintenance of normal allograft status 12 and 24 months after immunosuppression withdrawal. Results One hundred and twenty-two patients were eligible to participate in the trial, 116 were randomised (58 to Arm A and 58 to Arm B), 80 initiated immunosuppression withdrawal and 34 were maintaining on their baseline immunosuppression. Among the 80 patients who initiated withdrawal, 54 (67.5%) developed clinically apparent rejection, 22 (27.5%) successfully discontinued immunosuppression, 21 underwent a liver biopsy and 13 (16.3%) met the histological criteria of operational tolerance at 12 months after immunosuppression discontinuation. The transcriptional tolerance biomarker was not accurate at identifying patients meeting the operational tolerance criteria [odds ratio 1.466, 95% confidence interval (CI) 0.326 to 9.215; p = 0.744; Sensitivity (Sn) 54%, Specificity (Sp) 42%, positive predictive value 16%, and negative predictive value 81%, with an accuracy of 44%]. Due to the poor diagnostic performance of the test, the trial was terminated prematurely following an interim analysis of the results. No patients lost their grafts as a result of rejection during the study duration. Conclusions In selected liver transplant recipients, immunosuppression withdrawal proved to be feasible, but was successful in a much lower proportion of patients than originally estimated. A previously validated liver tissue transcriptional biomarker test was not considered accurate in predicting the success of immunosuppression withdrawal. Study registration Current Controlled Trials ISRCTN47808000 and EudraCT 2014-004557-14. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/94/55) and is published in full in Efficacy and Mechanism Evaluation; Vol. 12, No. 3. See the NIHR Funding and Awards website for further award information.
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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 | 0,022 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».