Literature DB >> 10666350

Comparison of the Framingham risk function-based coronary chart with risk function from an Italian population study.

A Menotti1, P E Puddu, M Lanti.   

Abstract

AIMS: The aim is to compare the coronary risk chart published by the European Task Force for Prevention of Coronary Heart Disease and produced using a Framingham risk function, with a risk function derived from an Italian population study. METHODS AND
RESULTS: Coronary risk function in this study was the result of longitudinal experience in an Italian middle-aged population of 1656 male subjects followed-up for 25 years. To comply with the Framingham equation the same risk factors (age, systolic blood pressure, total serum cholesterol and smoking habits), end-points (any possible coronary event including angina pectoris), and length of follow-up (10 years) were used, and the model (log-linear accelerated time failure model, accommodating the Weibull distribution) was similar. Comparisons were made computing the coronary risk for each cell of the coronary risk chart for men aged 40, 50 and 60 years. The Italian risk function produced highly significant coefficients for all four risk factors. Forty-four out of a total of 120 cells had a coronary risk of 20% or more in 10 years following the coronary risk chart, whereas this was reduced to four while using the Italian risk function (P<0.001). The Italian risk function largely underestimated the corresponding levels produced by the coronary risk chart and vice versa.
CONCLUSION: The Framingham risk function-based coronary risk chart overestimates absolute coronary risk in countries characterized by a lower incidence of coronary events and should be used with caution. Copyright 2000 The European Society of Cardiology.

Entities:  

Mesh:

Year:  2000        PMID: 10666350     DOI: 10.1053/euhj.1999.1864

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


  48 in total

1.  Risk factor scoring for coronary heart disease.

Authors:  Hans-Werner Hense
Journal:  BMJ       Date:  2003-11-29

2.  Primary prevention of coronary heart disease.

Authors:  Peter Brindle; Tom Fahey
Journal:  BMJ       Date:  2002-07-13

3.  Performance of the Framingham and SCORE cardiovascular risk prediction functions in a non-diabetic population of a Spanish health care centre: a validation study.

Authors:  Lourdes Cañón Barroso; Eloísa Cruces Muro; Natalio Díaz Herrera; Gerardo Fernández Ochoa; Juan Ignacio Calvo Hueros; Francisco Buitrago
Journal:  Scand J Prim Health Care       Date:  2010-09-27       Impact factor: 2.581

4.  Can non-physician health-care workers assess and manage cardiovascular risk in primary care?

Authors:  Dele O Abegunde; Bakuti Shengelia; Anne Luyten; Alexandra Cameron; Francesca Celletti; Sania Nishtar; Vasu Pandurangi; Shanthi Mendis
Journal:  Bull World Health Organ       Date:  2007-06       Impact factor: 9.408

5.  Cardiovascular risk engines can help in selecting patients to be evaluated by dynamic penile color doppler ultrasound.

Authors:  G Corona; E Mannucci; A D Fisher; F Lotti; E Bandini; L Vignozzi; G Balercia; F Paggi; L Petrone; G Forti; M Maggi
Journal:  J Endocrinol Invest       Date:  2008-12       Impact factor: 4.256

Review 6.  What is the ideal blood pressure goal for patients with stage III or higher chronic kidney disease?

Authors:  Yazan Khouri; Susan P Steigerwalt; Mershed Alsamara; Peter A McCullough
Journal:  Curr Cardiol Rep       Date:  2011-12       Impact factor: 2.931

7.  Original and REGICOR Framingham functions in a nondiabetic population of a Spanish health care center: a validation study.

Authors:  Francisco Buitrago; Juan Ignacio Calvo-Hueros; Lourdes Cañón-Barroso; Gerónimo Pozuelos-Estrada; Luis Molina-Martínez; Manuel Espigares-Arroyo; Juan Antonio Galán-González; Francisco J Lillo-Bravo
Journal:  Ann Fam Med       Date:  2011 Sep-Oct       Impact factor: 5.166

Review 8.  Framingham-based tools to calculate the global risk of coronary heart disease: a systematic review of tools for clinicians.

Authors:  Stacey Sheridan; Michael Pignone; Cynthia Mulrow
Journal:  J Gen Intern Med       Date:  2003-12       Impact factor: 5.128

9.  Improving global vascular risk prediction with behavioral and anthropometric factors. The multiethnic NOMAS (Northern Manhattan Cohort Study).

Authors:  Ralph L Sacco; Minesh Khatri; Tatjana Rundek; Qiang Xu; Hannah Gardener; Bernadette Boden-Albala; Marco R Di Tullio; Shunichi Homma; Mitchell S V Elkind; Myunghee C Paik
Journal:  J Am Coll Cardiol       Date:  2009-12-08       Impact factor: 24.094

10.  Long-term mortality prediction after operations for type A ascending aortic dissection.

Authors:  Francesco Macrina; Paolo E Puddu; Alfonso Sciangula; Marco Totaro; Fausto Trigilia; Mauro Cassese; Michele Toscano
Journal:  J Cardiothorac Surg       Date:  2010-05-25       Impact factor: 1.637

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.