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Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication.

ABSTRACT: The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the setting of increased data collection and summarizes recent advances in outcomes measurements pertaining to clinical, histopathological, and immune outcomes. Finally, we discuss the use of composite endpoints and Bayesian concepts, specifically focusing on the integrative box risk prediction score, in conjunction with machine learning to refine prognostication.

SUBMITTER: Anwar IJ

PROVIDER: S-EPMC10900533 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication.

Anwar Imran J IJ Srinivas Titte R TR Gao Qimeng Q Knechtle Stuart J SJ

Transplantation 20220323 8

The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the settin ...[more]

PMID: 35323161

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Project description:Composite endpoints that not only encompass mortality and relapse, but other critical post-transplant events such as graft-versus-host disease, are being increasingly utilized to quantify survival without significant morbidity after allogeneic blood or marrow transplantation. High-dose, post-transplantation cyclophosphamide reduces severe graft-versus-host disease with allogeneic marrow transplantation, making composite endpoints after this management particularly interesting. We retrospectively analyzed 684 adults with hematologic malignancies who received T-cell-replete bone marrow grafts and cyclophosphamide after myeloablative HLA-matched related (n=192) or unrelated (n=120), or non-myeloablative HLA-haploidentical (n=372) donor transplantation. The median follow up was 4 (range, 0.02-11.4) years. Graft-versus-host disease-free, relapse-free survival was defined as the time after transplantation without grade III-IV acute graft-versus-host disease, chronic graft-versus-host disease requiring systemic treatment, relapse, or death. Chronic graft-versus-host disease-free, relapse-free survival was defined as the time after transplantation without moderate or severe chronic graft-versus-host disease, relapse, or death. One-year graft-versus-host disease-free, relapse-free survival and chronic graft-versus-host disease-free, relapse-free survival estimates were, respectively, 47% (95% CI: 41-55%) and 53% (95% CI: 46-61%) after myeloablative HLA-matched related, 42% (95% CI: 34-52%) and 52% (95% CI: 44-62%) after myeloablative HLA-matched unrelated, and 45% (95% CI: 40-50%) and 50% (95% CI: 45-55%) after non-myeloablative HLA-haploidentical donor transplantation. In multivariable models, there were no differences in graft-versus-host disease-free, or chronic graft-versus-host disease-free, relapse-free survival after either myeloablative HLA-matched unrelated or non-myeloablative HLA-haploidentical, compared with myeloablative HLA-matched related donor transplantation. Although limited by inclusion of dissimilar cohorts, we found that post-transplantation cyclophosphamide-based platforms yield comparable composite endpoints across conditioning intensity, donor type, and HLA match.

Project description:BackgroundThe variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival.MethodsThis review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot.ResultsA total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses.ConclusionPrediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.

Dataset Information

Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication.

Publications

Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication.

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