Literature DB >> 23750335

Cardiovascular Disease Risk Assessment: Insights from Framingham.

Ralph B D'Agostino1, Michael J Pencina, Joseph M Massaro, Sean Coady.   

Abstract

Cardiovascular disease (CVD) is among the leading causes of death and disability worldwide. Since its beginning, the Framingham study has been a leader in identifying CVD risk factors. Clinical trials have demonstrated that when the modifiable risk factors are treated and corrected, the chances of CVD occurring can be reduced. The Framingham study also recognized that CVD risk factors are multifactorial and interact over time to produce CVD. In response, Framingham investigators developed the Framingham Risk Functions (also called Framingham Risk Scores) to evaluate the chance or likelihood of developing CVD in individuals. These functions are multivariate functions (algorithms) that combine the information in CVD risk factors such as sex, age, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking behavior, and diabetes status to produce an estimate (or risk) of developing CVD or a component of CVD (such as coronary heart disease, stroke, peripheral vascular disease, or heart failure) over a fixed time, for example, the next 10 years. These estimates of CVD risk are often major inputs in recommending drug treatments such as cholesterol-lowering drugs.

Entities:  

Year:  2013        PMID: 23750335      PMCID: PMC3673738          DOI: 10.1016/j.gheart.2013.01.001

Source DB:  PubMed          Journal:  Glob Heart


  90 in total

1.  Probability of stroke: a risk profile from the Framingham Study.

Authors:  P A Wolf; R B D'Agostino; A J Belanger; W B Kannel
Journal:  Stroke       Date:  1991-03       Impact factor: 7.914

2.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.

Authors:  R B D'Agostino; S Grundy; L M Sullivan; P Wilson
Journal:  JAMA       Date:  2001-07-11       Impact factor: 56.272

3.  The need for reorientation toward cost-effective prediction: comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond' by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929).

Authors:  Sander Greenland
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

4.  An updated coronary risk profile. A statement for health professionals.

Authors:  K M Anderson; P W Wilson; P M Odell; W B Kannel
Journal:  Circulation       Date:  1991-01       Impact factor: 29.690

5.  Invited commentary: Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance.

Authors:  Ralph B D'Agostino; Michael J Pencina
Journal:  Am J Epidemiol       Date:  2012-07-19       Impact factor: 4.897

6.  Quantifying discrimination of Framingham risk functions with different survival C statistics.

Authors:  Michael J Pencina; Ralph B D'Agostino; Linye Song
Journal:  Stat Med       Date:  2012-02-17       Impact factor: 2.373

7.  The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: design, recruitment, and initial examination.

Authors:  Greta Lee Splansky; Diane Corey; Qiong Yang; Larry D Atwood; L Adrienne Cupples; Emelia J Benjamin; Ralph B D'Agostino; Caroline S Fox; Martin G Larson; Joanne M Murabito; Christopher J O'Donnell; Ramachandran S Vasan; Philip A Wolf; Daniel Levy
Journal:  Am J Epidemiol       Date:  2007-03-19       Impact factor: 4.897

8.  Cardiovascular risk prediction tools for populations in Asia.

Authors:  F Barzi; A Patel; D Gu; P Sritara; T H Lam; A Rodgers; M Woodward
Journal:  J Epidemiol Community Health       Date:  2007-02       Impact factor: 3.710

9.  Coronary calcium as a predictor of coronary events in four racial or ethnic groups.

Authors:  Robert Detrano; Alan D Guerci; J Jeffrey Carr; Diane E Bild; Gregory Burke; Aaron R Folsom; Kiang Liu; Steven Shea; Moyses Szklo; David A Bluemke; Daniel H O'Leary; Russell Tracy; Karol Watson; Nathan D Wong; Richard A Kronmal
Journal:  N Engl J Med       Date:  2008-03-27       Impact factor: 91.245

10.  Evaluation of the Framingham risk score in the European Prospective Investigation of Cancer-Norfolk cohort: does adding glycated hemoglobin improve the prediction of coronary heart disease events?

Authors:  Rebecca K Simmons; Stephen Sharp; S Matthijs Boekholdt; Lincoln A Sargeant; Kay-Tee Khaw; Nicholas J Wareham; Simon J Griffin
Journal:  Arch Intern Med       Date:  2008-06-09
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  72 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

Review 2.  Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

Authors:  Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis
Journal:  J Am Med Inform Assoc       Date:  2016-05-17       Impact factor: 4.497

3.  In vivo triglyceride synthesis in subcutaneous adipose tissue of humans correlates with plasma HDL parameters.

Authors:  Demidmaa Tuvdendorj; Alejandro O Munoz; Viviana Ruiz-Barros; Jean-Marc Schwarz; Giuseppe Montalto; Manisha Chandalia; Lawrence C Sowers; Manfredi Rizzo; Elizabeth J Murphy; Nicola Abate
Journal:  Atherosclerosis       Date:  2016-06-13       Impact factor: 5.162

Review 4.  Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

Authors:  Aleksandra Ignjatović; Miodrag Stojanović; Zoran Milošević; Marija Anđelković Apostolović
Journal:  Ir J Med Sci       Date:  2017-12-02       Impact factor: 1.568

Review 5.  Dietary Advanced Glycation End Products and Cardiometabolic Risk.

Authors:  Claudia Luévano-Contreras; Armando Gómez-Ojeda; Maciste Habacuc Macías-Cervantes; Ma Eugenia Garay-Sevilla
Journal:  Curr Diab Rep       Date:  2017-08       Impact factor: 4.810

Review 6.  The Evolving Cardiovascular Disease Risk Scores for Persons with Diabetes Mellitus.

Authors:  Yanglu Zhao; Nathan D Wong
Journal:  Curr Cardiol Rep       Date:  2018-10-11       Impact factor: 2.931

7.  Cardiovascular Risk Prediction Functions Underestimate Risk in HIV Infection.

Authors:  Virginia A Triant; Jeremiah Perez; Susan Regan; Joseph M Massaro; James B Meigs; Steven K Grinspoon; Ralph B D'Agostino
Journal:  Circulation       Date:  2018-02-14       Impact factor: 29.690

8.  Personalized Prediction of Psychosis: External Validation of the NAPLS-2 Psychosis Risk Calculator With the EDIPPP Project.

Authors:  Ricardo E Carrión; Barbara A Cornblatt; Cynthia Z Burton; Ivy F Tso; Andrea M Auther; Steven Adelsheim; Roderick Calkins; Cameron S Carter; Tara Niendam; Tamara G Sale; Stephan F Taylor; William R McFarlane
Journal:  Am J Psychiatry       Date:  2016-07-01       Impact factor: 18.112

9.  Genome-wide DNA Methylation Profiling of Blood from Monozygotic Twins Discordant for Myocardial Infarction.

Authors:  Aylin Koseler; Feiyang Ma; Ismail Dogu Kilic; Marco Morselli; Oguz Kilic; Matteo Pellegrini
Journal:  In Vivo       Date:  2020 Jan-Feb       Impact factor: 2.155

10.  Developing and validating a new precise risk-prediction model for new-onset hypertension: The Jichi Genki hypertension prediction model (JG model).

Authors:  Hiroshi Kanegae; Takamitsu Oikawa; Kenji Suzuki; Yukie Okawara; Kazuomi Kario
Journal:  J Clin Hypertens (Greenwich)       Date:  2018-03-31       Impact factor: 3.738

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