Literature DB >> 19628409

Overview of risk prediction models in cardiovascular disease research.

Jisheng Cui1.   

Abstract

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.

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Year:  2009        PMID: 19628409     DOI: 10.1016/j.annepidem.2009.05.005

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  12 in total

Review 1.  Cardiovascular risk assessment: a global perspective.

Authors:  Dong Zhao; Jing Liu; Wuxiang Xie; Yue Qi
Journal:  Nat Rev Cardiol       Date:  2015-03-10       Impact factor: 32.419

2.  Adopting nested case-control quota sampling designs for the evaluation of risk markers.

Authors:  Yingye Zheng; Tianxi Cai; Margaret S Pepe
Journal:  Lifetime Data Anal       Date:  2013-06-27       Impact factor: 1.588

3.  Prediction models that include genetic data.

Authors:  Paola Sebastiani; Thomas T Perls
Journal:  Circ Cardiovasc Genet       Date:  2010-02

4.  Evaluating incremental values from new predictors with net reclassification improvement in survival analysis.

Authors:  Yingye Zheng; Layla Parast; Tianxi Cai; Marshall Brown
Journal:  Lifetime Data Anal       Date:  2012-12-20       Impact factor: 1.588

5.  Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!

Authors:  Paola Sebastiani; Nadia Solovieff; Jenny X Sun
Journal:  Front Genet       Date:  2012-02-29       Impact factor: 4.599

Review 6.  Development and application of chronic disease risk prediction models.

Authors:  Sun Min Oh; Katherine M Stefani; Hyeon Chang Kim
Journal:  Yonsei Med J       Date:  2014-07       Impact factor: 2.759

7.  Independent external validation of cardiovascular disease mortality in women utilising Framingham and SCORE risk models: a mortality follow-up study.

Authors:  Louise Gek Huang Goh; Timothy Alexander Welborn; Satvinder Singh Dhaliwal
Journal:  BMC Womens Health       Date:  2014-09-26       Impact factor: 2.809

8.  Predicting risk of substantial weight gain in German adults-a multi-center cohort approach.

Authors:  Ursula Bachlechner; Heiner Boeing; Marjolein Haftenberger; Anja Schienkiewitz; Christa Scheidt-Nave; Susanne Vogt; Barbara Thorand; Annette Peters; Sabine Schipf; Till Ittermann; Henry Völzke; Ute Nöthlings; Jasmine Neamat-Allah; Karin-Halina Greiser; Rudolf Kaaks; Annika Steffen
Journal:  Eur J Public Health       Date:  2017-08-01       Impact factor: 3.367

9.  Cardiovascular risk estimation in women with a history of hypertensive pregnancy disorders at term: a longitudinal follow-up study.

Authors:  Wietske Hermes; Jouke T Tamsma; Diana C Grootendorst; Arie Franx; Joris van der Post; Maria G van Pampus; Kitty Wm Bloemenkamp; Martina Porath; Ben W Mol; Christianne J M de Groot
Journal:  BMC Pregnancy Childbirth       Date:  2013-06-04       Impact factor: 3.007

10.  Development and validation of a risk score predicting substantial weight gain over 5 years in middle-aged European men and women.

Authors:  Annika Steffen; Thorkild I A Sørensen; Sven Knüppel; Noemie Travier; María-José Sánchez; José María Huerta; J Ramón Quirós; Eva Ardanaz; Miren Dorronsoro; Birgit Teucher; Kuanrong Li; H Bas Bueno-de-Mesquita; Daphne van der A; Amalia Mattiello; Domenico Palli; Rosario Tumino; Vittorio Krogh; Paolo Vineis; Antonia Trichopoulou; Philippos Orfanos; Dimitrios Trichopoulos; Bo Hedblad; Peter Wallström; Kim Overvad; Jytte Halkjær; Anne Tjønneland; Guy Fagherazzi; Laureen Dartois; Francesca Crowe; Kay-Tee Khaw; Nick Wareham; Lefkos Middleton; Anne M May; Petra H M Peeters; Heiner Boeing
Journal:  PLoS One       Date:  2013-07-16       Impact factor: 3.240

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