Literature DB >> 11119458

Comparative accuracy of cardiovascular risk prediction methods in primary care patients.

A F Jones1, J Walker, C Jewkes, F L Game, W A Bartlett, T Marshall, G R Bayly.   

Abstract

OBJECTIVE: To compare the relative accuracy of cardiovascular disease risk prediction methods based on equations derived from the Framingham heart study.
DESIGN: Risk factor data were collected prospectively from subjects being evaluated by their primary care physicians for prevention of cardiovascular disease. Projected cardiovascular risks were calculated for each patient with the Framingham equations, and also estimated from the risk tables and charts based on the same equations.
SETTING: 12 primary care practices (46 doctors) in Birmingham. PATIENTS: 691 subjects aged 30-70 years. MAIN OUTCOME MEASURES: Sensitivity, specificity, and positive and negative predictive values of the Framingham based risk tables and charts for treatment thresholds based on projected cardiovascular disease or coronary heart disease risk.
RESULTS: 59 subjects (8.5%) had projected 10 year coronary heart disease risks >/= 30%, and 291 (42.1%) had risks >/= 15%. At equivalent projected risk levels (10 year coronary heart disease >/= 30% and five year cardiovascular disease >/= 20%), the original Sheffield tables and those from New Zealand have the same sensitivities (40.0%, 95% confidence interval (CI) 26.6% to 57.8% v 41.2%, 95% CI 28.7% to 57. 3%) and specificities (98.6%, 95% CI 97.2% to 99.3% v 99.7%, 95% CI 98.8% to 100%). Modifications to the Sheffield tables improve sensitivity (91.4%, 95% CI 81.3% to 96.9%) but reduce specificity (95.8%, 95% CI 93.9% to 97.3%). The revised joint British recommendations' charts have high specificity (98.7%, 95% CI 97.5% to 99.5%) and good sensitivity (84.7%, 95% CI 71.0% to 93.0%).
CONCLUSIONS: The revised joint British recommendations charts appear to have the best combination of sensitivity and specificity for use in primary care patients.

Entities:  

Mesh:

Year:  2001        PMID: 11119458      PMCID: PMC1729574          DOI: 10.1136/heart.85.1.37

Source DB:  PubMed          Journal:  Heart        ISSN: 1355-6037            Impact factor:   5.994


  19 in total

1.  Risk assessment in primary prevention of coronary heart disease: randomised comparison of three scoring methods.

Authors:  C G Isles; L D Ritchie; P Murchie; J Norrie
Journal:  BMJ       Date:  2000-03-11

2.  Using the Framingham model to predict heart disease in the United Kingdom: retrospective study.

Authors:  S Ramachandran; J M French; M P Vanderpump; P Croft; R H Neary
Journal:  BMJ       Date:  2000-03-11

3.  Updated New Zealand cardiovascular disease risk-benefit prediction guide.

Authors:  R Jackson
Journal:  BMJ       Date:  2000-03-11

4.  Use of statins. But New Zealand tables are better.

Authors:  A J McLeod; M Armitage
Journal:  BMJ       Date:  1998-08-15

5.  Targeting lipid-lowering drug therapy for primary prevention of coronary disease: an updated Sheffield table.

Authors:  L E Ramsay; I U Haq; P R Jackson; W W Yeo; D M Pickin; J N Payne
Journal:  Lancet       Date:  1996-08-10       Impact factor: 79.321

6.  Prediction of coronary heart disease using risk factor categories.

Authors:  P W Wilson; R B D'Agostino; D Levy; A M Belanger; H Silbershatz; W B Kannel
Journal:  Circulation       Date:  1998-05-12       Impact factor: 29.690

7.  Lipid-lowering for prevention of coronary heart disease: what policy now?

Authors:  I Ul Haq; L E Ramsay; D M Pickin; W W Yeo; P R Jackson; J N Payne
Journal:  Clin Sci (Lond)       Date:  1996-10       Impact factor: 6.124

8.  British Hypertension Society guidelines for hypertension management 1999: summary.

Authors:  L E Ramsay; B Williams; G D Johnston; G A MacGregor; L Poston; J F Potter; N R Poulter; G Russell
Journal:  BMJ       Date:  1999-09-04

9.  Laboratory-based calculation of coronary heart disease risk in a hospital diabetic clinic.

Authors:  G R Bayly; W A Bartlett; P H Davies; D Husband; A Haddon; F L Game; A F Jones
Journal:  Diabet Med       Date:  1999-08       Impact factor: 4.359

10.  Cardiovascular disease risk profiles.

Authors:  K M Anderson; P M Odell; P W Wilson; W B Kannel
Journal:  Am Heart J       Date:  1991-01       Impact factor: 4.749

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  10 in total

1.  Comparative evaluation of the new Sheffield table and the modified joint British societies coronary risk prediction chart against a laboratory based risk score calculation.

Authors:  K S Rabindranath; N R Anderson; R Gama; M R Holland
Journal:  Postgrad Med J       Date:  2002-05       Impact factor: 2.401

2.  Prevention of cardiovascular diseases: focus on modifiable cardiovascular risk.

Authors:  F El Fakiri; M A Bruijnzeels; A W Hoes
Journal:  Heart       Date:  2005-10-26       Impact factor: 5.994

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4.  [Comparison of international recommendations for the recognition of asymptomatic high risk patients for a heart attack in Germany].

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5.  Greater ability to express positive emotion is associated with lower projected cardiovascular disease risk.

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Review 6.  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

7.  Cardiovascular event risk estimation among residents of a rural setting in Bayelsa state, Nigeria.

Authors:  Tamaraemumoemi Emmanuella Okoro; Johnbull Jumbo
Journal:  Am J Cardiovasc Dis       Date:  2021-06-15

Review 8.  Need for better blood pressure measurement in developing countries to improve prevention of cardiovascular disease.

Authors:  Pietro Amedeo Modesti; Eleonora Perruolo; Gianfranco Parati
Journal:  J Epidemiol       Date:  2014-11-22       Impact factor: 3.211

9.  Assessment of total cardiovascular risk using WHO/ISH risk prediction charts in three low and middle income countries in Asia.

Authors:  Dugee Otgontuya; Sophal Oum; Brian S Buckley; Ruth Bonita
Journal:  BMC Public Health       Date:  2013-06-05       Impact factor: 3.295

10.  Diabetes prevention among American Indians: the role of self-efficacy, risk perception, numeracy and cultural identity.

Authors:  Vanessa W Simonds; Adam Omidpanah; Dedra Buchwald
Journal:  BMC Public Health       Date:  2017-10-02       Impact factor: 3.295

  10 in total

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