The clinical utility and safety of biomarker-guided immunosuppression withdrawal in liver transplantation: the LIFT prospective RCT
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".