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      Development and Validation of a New Score to Assess the Risk of Posttransplantation Diabetes Mellitus in Kidney Transplant Recipients

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          Abstract

          Background.

          Posttransplantation diabetes mellitus (PTDM) is a serious complication of solid organ transplantation. It is associated with major adverse cardiovascular events, which are a leading cause of morbidity and mortality in transplant patients. This study aimed to develop and validate a score to predict the risk of PTDM in kidney transplant recipients.

          Methods.

          A single-center retrospective cohort study was conducted in a tertiary care hospital in Medellín, Colombia, between 2005 and 2019. Data from 727 kidney transplant recipients were used to develop a risk prediction model. Significant predictors with competing risks were identified using time-dependent Cox proportional hazard regression models. To build the prediction model, the score for each variable was weighted using calculated regression coefficients. External validation was performed using independent data, including 198 kidney transplant recipients from Tübingen, Germany.

          Results.

          Among the 727 kidney transplant recipients, 122 developed PTDM. The predictive model was based on 5 predictors (age, gender, body mass index, tacrolimus therapy, and transient posttransplantation hyperglycemia) and exhibited good predictive performance (C-index: 0.7 [95% confidence interval, 0.65-0.76]). The risk score, which included 33 patients with PTDM, was used as a validation data set. The results showed good discrimination (C-index: 0.72 [95% confidence interval, 0.62-0.84]). The Brier score and calibration plot demonstrated an acceptable fit capability in external validation.

          Conclusions.

          We proposed and validated a prognostic model to predict the risk of PTDM, which performed well in discrimination and calibration, and is a simple score for use and implementation by means of a nomogram for routine clinical application.

          Abstract

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          Most cited references51

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          A Proportional Hazards Model for the Subdistribution of a Competing Risk

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            Assessing the performance of prediction models: a framework for traditional and novel measures.

            The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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              Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

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                Author and article information

                Contributors
                Journal
                Transplant Direct
                Transplant Direct
                TXD
                Transplantation Direct
                Lippincott Williams & Wilkins (Hagerstown, MD )
                2373-8731
                08 November 2023
                December 2023
                : 9
                : 12
                : e1558
                Affiliations
                [1 ] Department of Clinical Epidemiology and Applied Biostatistics, University Hospital Tübingen, Germany.
                [2 ] Faculty of Medicine, University of Antioquia, Medellín, Colombia.
                [3 ] Department of Nephrology, Hospital Pablo Tobón Uribe, Medellín, Colombia.
                [4 ] Department of Diabetology, Endocrinology, Nephrology, University of Tübingen, Tübingen, Germany.
                [5 ] Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany.
                [6 ] German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
                Author notes
                Correspondence: Maria Carolina Isaza-López, MD, Clínica Universitaria Bolivariana, Ave 72 A St 78 B – 50, Medellín, Colombia. ( mariac.isaza@ 123456upb.edu.co ).
                Author information
                https://orcid.org/0000-0002-6140-9817
                Article
                00006
                10.1097/TXD.0000000000001558
                10635612
                00acd539-49a1-4cc6-9dbd-ced1089340a7
                Copyright © 2023 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 August 2023
                : 20 September 2023
                Funding
                Funded by: Medizinischen Fakultät, Eberhard Karls Universität Tübingen, doi 10.13039/501100009397;
                Award Recipient : Lina Maria Serna
                Categories
                016
                Kidney Transplantation
                Custom metadata
                TRUE
                T

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