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      Does Inclusion of Education and Marital Status Improve SCORE Performance in Central and Eastern Europe and Former Soviet Union? Findings from MONICA and HAPIEE Cohorts

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          Abstract

          Background and Objective

          The SCORE scale predicts the 10-year risk of fatal atherosclerotic cardiovascular disease (CVD), based on conventional risk factors. The high-risk version of SCORE is recommended for Central and Eastern Europe and former Soviet Union (CEE/FSU), due to high CVD mortality rates in these countries. Given the pronounced social gradient in cardiovascular mortality in the region, it is important to consider social factors in the CVD risk prediction. We investigated whether adding education and marital status to SCORE benefits its prognostic performance in two sets of population-based CEE/FSU cohorts.

          Methods

          The WHO MONICA (MONItoring of trends and determinants in CArdiovascular disease) cohorts from the Czech Republic, Poland (Warsaw and Tarnobrzeg), Lithuania (Kaunas), and Russia (Novosibirsk) were followed from the mid-1980s (577 atherosclerotic CVD deaths among 14,969 participants with non-missing data). The HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study follows Czech, Polish (Krakow), and Russian (Novosibirsk) cohorts from 2002–05 (395 atherosclerotic CVD deaths in 19,900 individuals with non-missing data).

          Results

          In MONICA and HAPIEE, the high-risk SCORE ≥5% at baseline strongly and significantly predicted fatal CVD both before and after adjustment for education and marital status. After controlling for SCORE, lower education and non-married status were significantly associated with CVD mortality in some samples. SCORE extension by these additional risk factors only slightly improved indices of calibration and discrimination (integrated discrimination improvement <5% in men and ≤1% in women).

          Conclusion

          Extending SCORE by education and marital status failed to substantially improve its prognostic performance in population-based CEE/FSU cohorts.

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          Most cited references77

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          A Proportional Hazards Model for the Subdistribution of a Competing Risk

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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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              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

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                8 April 2014
                : 9
                : 4
                : e94344
                Affiliations
                [1 ]Epidemiology and Public Health Department, University College London, London, United Kingdom
                [2 ]Department of CVD Epidemiology, Prevention, and Health Promotion, the Cardinal Stefan Wyszynski Institute of Cardiology, Warsaw, Poland
                [3 ]Environmental Health Monitoring System, National Institute of Public Health, Prague, Czech Republic
                [4 ]Institute of Internal and Preventive Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia
                [5 ]State Novosibirsk Medical University, Novosibirsk, Russia
                [6 ]Department of Epidemiology and Population Sciences, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland
                [7 ]Laboratory of Population Research, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
                [8 ]Preventive Cardiology Department, Institute of Clinical and Experimental Medicine, Prague, Czech Republic
                Bielefeld Evangelical Hospital, Germany
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: OV GB RK SM AP AT ZS GS MB HP. Performed the experiments: OV GB RK SM AP AT ZS GS MB HP. Analyzed the data: OV. Wrote the paper: OV MB HP. Revised the manuscript critically for important intellectual content and approved the final draft: OV GB RK SM AP AT ZS GS MB HP.

                Article
                PONE-D-13-52154
                10.1371/journal.pone.0094344
                3979770
                24714549
                9bad7582-453f-4efa-820a-0710ca9ea3b6
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 December 2013
                : 12 March 2014
                Page count
                Pages: 11
                Funding
                The WHO MONICA Project in the Czech Republic, Poland, Lithuania, and Russia was supported by the Czech and Polish Ministries of Health, the Polish National Committee for Scientific Research, the Lithuanian Ministry of Education and Science, and the Russian Academy of Medical Sciences. The HAPIEE study has been supported by the Wellcome Trust (grant numbers 064947/Z/01/Z, 081081/Z/06/Z); the National Institute of Aging (1R01 AG23522); and the MacArthur Foundation (71208). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Cardiology
                Epidemiology
                Cardiovascular Disease Epidemiology
                Social Epidemiology
                Health Care
                Socioeconomic Aspects of Health
                Public and Occupational Health
                Behavioral and Social Aspects of Health
                Vascular Medicine
                Atherosclerosis
                Research and Analysis Methods
                Research Design
                Clinical Research Design
                Cohort Studies

                Uncategorized
                Uncategorized

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