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dc.contributor.authorRiley, S
dc.contributor.authorZhang, Q
dc.contributor.authorTse, W-Y
dc.contributor.authorConnor, A
dc.contributor.authorWei, Yinghui
dc.date.accessioned2022-05-12T16:27:59Z
dc.date.issued2022-06-23
dc.identifier.issn0934-0874
dc.identifier.issn1432-2277
dc.identifier.other10397
dc.identifier.urihttp://hdl.handle.net/10026.1/19220
dc.description.abstract

Statistical models that can predict graft and patient survival outcomes following kidney transplantation could be of great clinical utility. We sought to appraise existing clinical prediction models for kidney transplant survival outcomes that could guide kidney donor acceptance decision-making. We searched for clinical prediction models for survival outcomes in adult recipients with single kidney-only transplants. Models that require information anticipated to become available only after the time of transplantation were excluded as, by that time, the kidney donor acceptance decision would have already been made. The outcomes of interest were all-cause and death-censored graft failure, and death. We summarised the methodological characteristics of the prediction models, predictive performance and risk of bias. We retrieved 4,026 citations from which 23 articles describing 74 models met the inclusion criteria. Discrimination was moderate for all-cause graft failure (C-statistic: 0.570–0.652; Harrell’s C: 0.580–0.660; AUC: 0.530–0.742), death-censored graft failure (C-statistic: 0.540–0.660; Harrell’s C: 0.590–0.700; AUC: 0.450–0.810) and death (C-statistic: 0.637–0.770; Harrell’s C: 0.570–0.735). Calibration was seldom reported. Risk of bias was high in 49 of the 74 models, primarily due to methods for handling missing data. The currently available prediction models using pre-transplantation information show moderate discrimination and varied calibration. Further model development is needed to improve predictions for the purpose of clinical decision-making.

dc.language.isoen
dc.publisherFrontiers Media
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleUsing information available at the time of donor offer to predict kidney transplant survival outcomes: a systematic review of prediction models
dc.typejournal-article
plymouth.volume35
plymouth.publication-statusPublished online
plymouth.journalTransplant International
dc.identifier.doi10.3389/ti.2022.10397
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dcterms.dateAccepted2022-05-04
dc.rights.embargodate2022-6-25
dc.identifier.eissn1432-2277
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.3389/ti.2022.10397
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.typeJournal Article/Review
plymouth.funderUsing big data to develop and validate clinical prediction models for survival outcomes in kidney transplant::Engineering and Physical Sciences Research Council and University Hospitals Plymouth NHS Trust


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