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      Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis

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          Abstract

          Background

          The utility of estimated glomerular filtration rate (eGFR) and albuminuria for cardiovascular prediction is controversial.

          Methods

          We meta-analyzed individual-level data from 24 cohorts (with a median follow-up time longer than 4 years, varying from 4.2 to 19.0 years) in the Chronic Kidney Disease Prognosis Consortium (637,315 participants without a history of cardiovascular disease) and assessed C-statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in 5-year timeframe, contrasting prediction models consisting of traditional risk factors with and without creatinine-based eGFR and/or albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria).

          Findings

          The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR and more evident for cardiovascular mortality (c-statistic difference 0.0139 [95%CI 0.0105–0.0174] and 0.0065 [0.0042–0.0088], respectively) and heart failure (0.0196 [0.0108–0.0284] and 0.0109 [0.0059–0.0159]) than for coronary disease (0.0048 [0.0029–0.0067] and 0.0036 [0.0019–0.0054]) and stroke (0.0105 [0.0058–0.0151] and 0.0036 [0.0004–0.0069]). Dipstick proteinuria demonstrated smaller improvement than ACR. The discrimination improvement with kidney measures was especially evident in individuals with diabetes or hypertension but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these conditions. In participants with chronic kidney disease (CKD), the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the c-statistic for cardiovascular mortality declined by 0.023 [0.016–0.030] vs. <0.007 when omitting eGFR and ACR vs. any single modifiable traditional predictors, respectively.

          Interpretation

          Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when they are already assessed for clinical purpose and/or cardiovascular mortality and heart failure are the outcomes of interest (e.g., the European guidelines on cardiovascular prevention). ACR may have particularly broad implications for cardiovascular prediction. In CKD populations, the simultaneous assessment of eGFR and ACR will facilitate improved cardiovascular risk classification, supporting current CKD guidelines.

          Funding

          US National Kidney Foundation and NIDDK

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          Most cited references17

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.

            M. S. Pepe (2004)
            A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker's ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. In addition, the serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.
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              Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward.

              The 2012 KDIGO Guideline for CKD evaluation, classification, and management has updated the original 2002 KDOQI Guidelines, using newer data and addressing issues raised over the last decade concerning definitions and assessment. This review highlights the key aspects of the CKD guideline, and describes the rationale for specific wording and the scope of the document. A précis of key concepts in each of the five sections of the guideline is presented. The guideline document is intended for general practitioners and nephrologists, and covers CKD evaluation, classification, and management for both adults and children. Throughout the guideline, we have attempted to overtly address areas of controversy or non-consensus, international relevance, and impact on practice and public policy.
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                Author and article information

                Journal
                101618821
                41837
                Lancet Diabetes Endocrinol
                Lancet Diabetes Endocrinol
                The lancet. Diabetes & endocrinology
                2213-8587
                2213-8595
                17 July 2015
                28 May 2015
                July 2015
                01 July 2016
                : 3
                : 7
                : 514-525
                Affiliations
                Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K Matsushita MD, Prof J Coresh MD, Y Sang MS, Prof E Guallar MD, Prof M Woodward PhD); The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia (Prof J Chalmers MD, Prof M Woodward PhD); National Heart, Lung, and Blood Institute’s Framingham Heart Study and the Center for Population Studies Framingham, MA (C Fox MD); Duke-NUS Graduate Medical School, Singapore (Prof T Jafar MD); VA San Diego Healthcare and University of California San Diego, San Diego, CA (Prof SK Jassal MD); Diabetes Centre Zwolle, Isala hospital, Zwolle, the Netherlands (GWD Landman MD); Department of Medicine, University of Alabama at Birmingham, AL (Prof P Muntner PhD, Prof DG Warnock MD); Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK (Prof P Roderick MD); Department of Public Health, Dokkyo Medical University School of Medicine, Tochigi, Japan (T Sairenchi PhD); Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany (B Schöttker PhD); 8816 Manchester Rd, St. Louis, MO 63144 (A Shankar MD); Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, and Departments of Medicine, Epidemiology, and Biostatistics, University of California San Francisco, San Francisco, CA (Prof M Shlipak MD); Department of Medicine, University of Calgary, Calgary, Alberta, Canada (Prof M Tonelli MD); Department of Cardiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK (Prof J Townend MD); Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, the Netherlands (A van Zuilen MD); Department of Public Health Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan (K Yamagishi MD); Department of Cardiology, Nagoya University Graduate School of Medicine, Nagoya, Japan (K Yamashita MD); Department of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands (Prof R Gansevoort MD); Division of Nephrology, Tufts Medical Center, Boston, MA (Prof M Sarnak MD); Nuffield Department of Population Health, University of Oxford, Oxford, UK (Prof M Woodward PhD); Department of Medical Sciences, Uppsala University, Uppsala, and the School of Health and Social Studies, Dalarna University, Falun, Sweden (Prof J Ärnlöv MD)
                Author notes
                Address for Correspondence: Chronic Kidney Disease Prognosis Consortium Data Coordinating Center (Principal Investigator, Josef Coresh, MD, PhD), 615 N. Wolfe Street, Baltimore, MD 21205, USA; Tel: 410-955-9917, Fax: 443-683-8357, ckdpc@ 123456jhmi.edu
                Article
                NIHMS706372
                10.1016/S2213-8587(15)00040-6
                4594193
                26028594
                d7a726b3-e74e-408c-901d-9b7c3d11d396

                This manuscript version is made available under the CC BY-NC-ND 4.0 license.

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