Predicting individual risk of coronary heart disease (CHD) on an italian sample of patients with hypercholesterolemia

2000 ◽  
Vol 151 (1) ◽  
pp. 274
Author(s):  
L. Denti ◽  
A. Cecchetti ◽  
F. Merli ◽  
R. Benedetti ◽  
G. Pasolini ◽  
...  
Author(s):  
Guizhou Hu ◽  
Martin M. Root

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.


2005 ◽  
Vol 7 (7) ◽  
pp. 1-24 ◽  
Author(s):  
Robert J. Stevens ◽  
Karen M.J. Douglas ◽  
Athanasios N. Saratzis ◽  
George D. Kitas

Rheumatoid arthritis (RA) associates with increased cardiovascular mortality. This appears to be predominantly due to ischaemic causes, such as myocardial infarction and congestive heart failure. The higher prevalence of cardiac ischaemia in RA is thought to be due to the accelerated development of atherosclerosis. There are two main reasons for this, which might be inter-related: the systemic inflammatory load, characteristic of RA; and the accumulation in RA of classical risk factors for coronary heart disease, which is reminiscent of the metabolic syndrome. We describe and discuss in the context of RA the involvement of local and systemic inflammatory processes in the development and rupture of atherosclerotic plaques, as well as the role of individual risk factors for coronary heart disease. We also present the challenges facing the clinical and scientific communities addressing this problem, which is receiving increasing attention.


Heart ◽  
2013 ◽  
Vol 99 (Suppl 3) ◽  
pp. A133.2-A133
Author(s):  
T Hunag ◽  
HM Yin ◽  
HY Gao ◽  
XQ Wang ◽  
MJ Li

2016 ◽  
Author(s):  
Gad Abraham ◽  
Aki S Havulinna ◽  
Oneil G Bhalala ◽  
Sean G Byars ◽  
Alysha M de Livera ◽  
...  

Background Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of a genomic risk score (GRS) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Methods We generated a GRS of 49,310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested this using five prospective population cohorts (three FINRISK cohorts, combined n=12,676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n=3,406, 587 incident CHD events). Results The GRS was strongly associated with time to CHD event (FINRISK HR=1.74, 95% CI 1.61-1.86 per S.D. of GRS; Framingham HR=1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for clinical risk scores or individual risk factors, including family history. Integration of the GRS with clinical risk scores (FRS and ACC/AHA13 score) improved prediction of CHD events within 10 years (meta-analysis C-index: +1.5-1.6%, P<0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P<0.001). Men in the top 20% of the GRS had 3-fold higher risk of CHD by age 75 in FINRISK and 2-fold in FHS, and attaining 10% cumulative CHD risk 18y earlier in FINRISK and 12y earlier in FHS than those in the bottom 20%. Furthermore, high genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. Conclusions A GRS based on a large number of SNPs substantially improves CHD risk prediction and encodes decades of variation in CHD risk not captured by traditional clinical risk scores.


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