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      Which Biomarker is the Best for Predicting Mortality in Incident Peritoneal Dialysis Patients: NT-ProBNP, Cardiac TnT, or hsCRP? : A Prospective Observational Study

      , MD, , MD, , MD, , MD, , MD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD

      Medicine

      Wolters Kluwer Health

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          Abstract

          Although numerous previous studies have explored various biomarkers for their ability to predict mortality in end-stage renal disease (ESRD) patients, these studies have been limited by retrospective analyses, mostly prevalent dialysis patients, and the measurement of only 1 or 2 biomarkers. This prospective study was aimed to evaluate the association between 3 biomarkers and mortality in incident 335 ESRD patients starting continuous ambulatory peritoneal dialysis (CAPD) in Korea. According to the baseline NT-proBNP, cTnT, and hsCRP levels, the patients were stratified into tertiles, and cardiovascular (CV) and all-cause mortalities were compared. Additionally, time-dependent ROC curves were constructed, and the net reclassification index (NRI) and integrated discrimination improvement (IDI) of the models with various biomarkers were calculated. We found the upper tertile of NT-proBNP was significantly associated with increased risk of both CV and all-cause mortalities. However, the upper tertile of hsCRP was significantly related only to the high risk of all-cause mortality even after adjustment for age, sex, and white blood cell counts. Moreover, NT-proBNP had the highest predictive power for CV mortality, whereas hsCRP was the best prognostic marker for all-cause mortality among these biomarkers. In conclusions, NT-proBNP is a more significant prognostic factor for CV mortality than cTnT and hsCRP, whereas hsCRP is a more significant predictor than NT-proBNP and cTnT for all-cause mortality in incident peritoneal dialysis patients.

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          Most cited references 53

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          Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology.

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            Prediction of coronary heart disease using risk factor categories.

            The objective of this study was to examine the association of Joint National Committee (JNC-V) blood pressure and National Cholesterol Education Program (NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions. This work was designed as a prospective, single-center study in the setting of a community-based cohort. The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up. During the 12 years of follow-up, a total of 383 men and 227 women developed CHD, which was significantly associated with categories of blood pressure, total cholesterol, LDL cholesterol, and HDL cholesterol (all P or =130/85). The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol (> or =200 mg/dL) was 27% in men and 34% in women. Recommended guidelines of blood pressure, total cholesterol, and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.
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              Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

              Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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                Author and article information

                Affiliations
                From the Department of Internal Medicine, College of Medicine, Brain Korea 21 for Medical Science, Severance Biomedical Science Institute, Yonsei University, Seoul (HJO, MJL, YEK, KSP, LTP, SHH, T-HY, S-WK), Department of Internal Medicine, Kyungpook National University School of Medicine, Daegu (Y-LK), Department of Internal Medicine, Seoul National University of Medicine (YSK), Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, Seoul (CWY); and Department of Medicine, Chonnam National University Medical School, Gwangju, Korea (N-HK).
                Author notes
                Correspondence to Shin-Wook Kang, Department of Internal Medicine, College of Medicine, Brain Korea 21 for Medical Science, Severance Biomedical Science Institute, Yonsei University, 134 Shinchon-Dong, Seodaemoon-Gu, Seoul 120-752, Korea (e-mail: kswkidney@ 123456yuhs.ac ).
                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MEDI
                Medicine
                Wolters Kluwer Health
                0025-7974
                1536-5964
                November 2015
                06 November 2015
                : 94
                : 44
                26554763 4915864 10.1097/MD.0000000000001636 01636
                Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

                This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0, where it is permissible to download, share and reproduce the work in any medium, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0

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