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      Graft Function Variability and Slope and Kidney Transplantation Outcomes

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          Abstract

          Introduction

          It is critical to identify kidney transplant recipients (KTRs) at higher risk for adverse outcomes, to focus on monitoring and interventions to improve outcomes. We examined the associations between graft function variability and long-term outcomes in KTRs in an observational study.

          Methods

          We identified 2919 KTRs in the Wisconsin Allograft Recipient Database (WisARD) who had a functioning allograft 2 years posttransplantation and at least 3 outpatient measurements of estimated glomerular filtration rate (eGFR) from 1 to 2 years posttransplantation. Graft function slope was calculated from a linear regression of eGFR, and variability was defined as the coefficient of variation around this regression line. Associations of eGFR variability and slope with death, graft failure, cardiovascular events, and acute rejection were estimated.

          Results

          Compared to the lowest quartile, the highest quartile of eGFR variability was associated with a higher risk of death (adjusted hazard ratio [HR] = 1.85; 95% CI = 1.23−2.76), but not with a higher risk of graft failure (subhazard ratio = 1.16; 95% CI = 0.85−1.58), independent of eGFR and slope of eGFR. Greater eGFR variability was associated with higher risk of cardiovascular- and infection-related death and cardiovascular events but not malignancy-related death or allograft rejection. Including variability of eGFR significantly improved prediction of mortality but not prediction of graft failure.

          Conclusion

          Variability of eGFR is independently associated with risk of death, especially cardiovascular disease−related death and cardiovascular events, but not graft failure. Variability of eGFR may help identify KTRs at higher risk for death and cardiovascular events.

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          Most cited references20

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

            Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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              Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.

              Appropriate quantification of added usefulness offered by new markers included in risk prediction algorithms is a problem of active research and debate. Standard methods, including statistical significance and c statistic are useful but not sufficient. Net reclassification improvement (NRI) offers a simple intuitive way of quantifying improvement offered by new markers and has been gaining popularity among researchers. However, several aspects of the NRI have not been studied in sufficient detail. In this paper we propose a prospective formulation for the NRI which offers immediate application to survival and competing risk data as well as allows for easy weighting with observed or perceived costs. We address the issue of the number and choice of categories and their impact on NRI. We contrast category-based NRI with one which is category-free and conclude that NRIs cannot be compared across studies unless they are defined in the same manner. We discuss the impact of differing event rates when models are applied to different samples or definitions of events and durations of follow-up vary between studies. We also show how NRI can be applied to case-control data. The concepts presented in the paper are illustrated in a Framingham Heart Study example. In conclusion, NRI can be readily calculated for survival, competing risk, and case-control data, is more objective and comparable across studies using the category-free version, and can include relative costs for classifications. We recommend that researchers clearly define and justify the choices they make when choosing NRI for their application. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Journal
                Kidney Int Rep
                Kidney Int Rep
                Kidney International Reports
                Elsevier
                2468-0249
                30 March 2021
                June 2021
                30 March 2021
                : 6
                : 6
                : 1642-1652
                Affiliations
                [1 ]Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
                [2 ]Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
                [3 ]Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
                Author notes
                [] Correspondence: Brad C. Astor, 5149 MFCB, 1685 Highland Avenue, Madison, Wisconsin 53705, USA. bcastor@ 123456medicine.wisc.edu
                Article
                S2468-0249(21)01028-7
                10.1016/j.ekir.2021.03.880
                8207313
                a4267d9e-a73a-4c54-97a5-92ae9d2a4e0f
                © 2021 International Society of Nephrology. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 30 October 2020
                : 8 February 2021
                : 8 March 2021
                Categories
                Clinical Research

                allograft survival,cardiovascular events,graft function variability,mortality,rejection,transplantation

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