Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

Mannan, Haider, Stevenson, Chris, Peeters, Anna, Walls, Helen, and McNeil, John (2010) Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking. Heart International, 5 (2). pp. 49-57.

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Abstract



Abstract
Current prediction models for risk of cardiovascular disease (CVD) incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting.
The risk of developing a first CVD event was evaluated using a Cox's model for participants in the Framingham offspring cohort who attended the fourth examination (1988-92) between the ages of 30 and 74 years and were free of CVD (n=3751). The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and packyears among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.

Item ID: 35541
Item Type: Article (Research - C1)
ISSN: 2036-2579
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This work is licensed under a Creative Commons Attribution 3.0 License (by-nc 3.0).

Date Deposited: 17 Oct 2014 01:32
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111706 Epidemiology @ 50%
01 MATHEMATICAL SCIENCES > 0104 Statistics > 010402 Biostatistics @ 50%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920499 Public Health (excl. Specific Population Health) not elsewhere classified @ 100%
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