GW24-e3664 Study of quantitative evaluation of individual risk of coronary heart disease

Heart ◽  
2013 ◽  
Vol 99 (Suppl 3) ◽  
pp. A133.2-A133
Author(s):  
T Hunag ◽  
HM Yin ◽  
HY Gao ◽  
XQ Wang ◽  
MJ Li
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Quantitative Evaluation ◽  
Individual Risk

Quantitative Evaluation of Myocardial Stress/Rest 201TISPECT: Results of a ROI-Based Method in 108 Patients with CHD

1987 ◽  
Vol 26 (06) ◽  
pp. 234-240 ◽  
Author(s):  
H. Stirner ◽  
J. Dahl ◽  
R. Uebis ◽  
E. Kleinhans ◽  
M. Biedermann ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Quantitative Evaluation ◽  
Myocardial Infarctions ◽  
Myocardial Spect ◽  
Luminal Diameter ◽  
Myocardial Stress ◽  
Threshold Criterion ◽  
Combined Evaluation ◽  
Severity Of The Disease

ROI-based polar maps (33 ROIs) were employed to evaluate quantitatively stress/rest myocardial 201TI SPECT in 108 patients with angiographically proven coronary heart disease (CHD) in comparison with 30 controls. Sensitivity in detecting a CHD with stenoses of > 50% of luminal diameter was determined versus normal regional values (± 2.5 SD) employing vitality (VI) and wash-out corrected redistribution (RD). The method was evaluated referring to the severity of the disease, to the number of ROIs displaying changes [(a) 1 ROI, (b) >2 ROIs], to validity of VI, RD or a combination thereof, and for specificity. Wash-out values were found to depend on degree of stress individually achieved and thus were not used as a threshold criterion. Sensitivity in supply areas with old myocardial infarctions was 95% (a) and 86% (b), resp. With no infarction, it was 96% (a) and 79% (b), resp. VI in stenosis > 75% was more sensitive than RD. However, combined evaluation of VI and RD yielded sensitivities from 91-100% (a) and 77-94% (b), resp. for different main supply areas. In stenosis < 50% with normal VI, RD was positive in 18-31 %. Specificity turned out to be 91 % (a) and 97% (b), resp. We conclude that the method presented is reliable to quantify numerically 201TI kinetics in myocardial SPECT, aimed at detecting and describing CHD.

scholarly journals Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

2005 ◽  
Vol 12 (5) ◽  
pp. 459-464 ◽  
Author(s):  
Guizhou Hu ◽  
Martin M. Root
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Empirical Model ◽  
Complex Disease ◽  
Prediction Models ◽  
Longitudinal Research ◽  
Study Data ◽  
Individual Risk ◽  
Data Set

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.

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 ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Individual Risk ◽  
Italian Sample

Inflammation and atherosclerosis in rheumatoid arthritis

2005 ◽  
Vol 7 (7) ◽  
pp. 1-24 ◽  
Author(s):  
Robert J. Stevens ◽  
Karen M.J. Douglas ◽  
Athanasios N. Saratzis ◽  
George D. Kitas
Keyword(s):  
Rheumatoid Arthritis ◽  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Individual Risk ◽  
Atherosclerotic Plaques ◽  
The Metabolic Syndrome ◽  
Inflammatory Processes ◽  
Individual Risk Factors

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.

scholarly journals Genomic prediction of coronary heart disease

2016 ◽  
Author(s):  
Gad Abraham ◽  
Aki S Havulinna ◽  
Oneil G Bhalala ◽  
Sean G Byars ◽  
Alysha M de Livera ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Score ◽  
Meta Analysis ◽  
Risk Scores ◽  
Individual Risk ◽  
Clinical Risk ◽  
Framingham Risk ◽  
Chd Risk ◽  
Genomic Risk

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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