Trial reportBMC immunology2019
A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection in renal transplantation.
Trial report in BMC immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00724022 (Triple Arm, Prospectively Randomized Multi Centre Study Phase IV to Evaluate Calcineurin Inhibitor Reduced, Steroid Free Immunosuppression After Renal Transplantation in Non-risk Patients), which is not on this map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Triple Arm, Prospectively Randomized Multi Centre Study Phase IV to Evaluate Calcineurin Inhibitor Reduced, Steroid Free Immunosuppression After Renal Transplantation in Non-risk Patients
Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Review
- Structural principles of peptide-centric chimeric antigen receptor recognition guide therapeutic expansion.Science immunology · 2023Article
- A Chicken Tapasin ortholog can chaperone empty HLA-B∗37:01 molecules independent of other peptide-loading components.The Journal of biological chemistry · 2023Article
- Xeno interactions between MHC-I proteins and molecular chaperones enable ligand exchange on a broad repertoire of HLA allotypes.Science advances · 2023Article
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- Distinct Polymorphisms in HLA Class I Molecules Govern Their Susceptibility to Peptide Editing by TAPBPR.Cell reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 8 institutions in 1 country.
Funding
Abstract
backgroundAcute cellular rejection (ACR) is associated with complications after kidney transplantation, such as graft dysfunction and graft loss. Early risk assessment is therefore critical for the improvement of transplantation outcomes. In this work, we retrospectively analyzed a pre-transplant HLA antigen bead assay data set that was acquired by the e:KID consortium as part of a systems medicine approach.
resultsThe data set included single antigen bead (SAB) reactivity profiles of 52 low-risk graft recipients (negative complement dependent cytotoxicity crossmatch, PRA < 30%) who showed detectable pre-transplant anti-HLA 1 antibodies. To assess whether the reactivity profiles provide a means for ACR risk assessment, we established a novel approach which differs from standard approaches in two aspects: the use of quantitative continuous data and the use of a multiparameter classification method. Remarkably, it achieved significant prediction of the 38 graft recipients who experienced ACR with a balanced accuracy of 82.7% (sensitivity = 76.5%, specificity = 88.9%).
conclusionsThe resultant classifier achieved one of the highest prediction accuracies in the literature for pre-transplant risk assessment of ACR. Importantly, it can facilitate risk assessment in non-sensitized patients who lack donor-specific antibodies. As the classifier is based on continuous data and includes weak signals, our results emphasize that not only strong but also weak binding interactions of antibodies and HLA 1 antigens contain predictive information.
trial registrationClinicalTrials.gov NCT00724022 . Retrospectively registered July 2008.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.