Prediction of Coronary Heart Disease Using Risk Factor Categories

Abstract

Background-The objective of this study was to examine the association of Joint National Committee (JNC-V) blood pressure and National Cholesterol Education Program (NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions. Methods and Results-This work was designed as a prospective, single-center study in the setting of a community-based cohort. The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up. During the 12 years of follow-up, a total of 383 men and 227 women developed CHD, which was significantly associated with categories of blood pressure, total cholesterol, LDL cholesterol, and HDL cholesterol (all PϽ.001). Sex-specific prediction equations were formulated to predict CHD risk according to age, diabetes, smoking, JNC-V blood pressure categories, and NCEP total cholesterol and LDL cholesterol categories. The accuracy of this categorical approach was found to be comparable to CHD prediction when the continuous variables themselves were used. After adjustment for other factors, Ϸ28% of CHD events in men and 29% in women were attributable to blood pressure levels that exceeded high normal (Ն130/85). The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol (Ն200 mg/dL) was 27% in men and 34% in women. Conclusions-Recommended guidelines of blood pressure, total cholesterol, and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.

Cardiac Time Intervals Measured by Tissue Doppler Imaging M‐mode: Association With Hypertension, Left Ventricular Geometry, and Future Ischemic Cardiovascular Diseases

“…Further, only the MPI remained an independent predictor of ICVD in participants with hypertension (MPI: SHR, 1.12 [95% CI 1.01–1.23] per 0.1 increase, P =0.027) (Figure 8D). In addition, reclassification analysis demonstrated that adding IVRT/ET or MPI to the clinical predictors from the Framingham Risk Score32 and the SCORE risk chart34 (age, sex, cholesterol, smoking status, and SBP) yielded better predicting models with significant increase in the categorical net reclassification improvement of 0.0227 (95% CI 0.0017–0.0437, P =0.034) for IVRT/ET and 0.402 (95% CI 0.0150–0.0653, P =0.002) for the MPI, respectively. Additionally, reclassification analysis demonstrated that adding IVRT/ET or MPI to a model including the clinical predictors from the newer ESH/ESC risk chart1 (age, sex, smoking status, cholesterol, diabetes, SBP, DBP, LVH, chronic kidney disease [defined as estimated glomerular filtration rate ≤60 mL/min per 1.73 m 2 ], IHD, and ischemic stroke) yielded better predicting models with significant increase in the categorical net reclassification improvement of 0.0341 (95% CI 0.0079–0.0603, P =0.011) for IVRT/ET and 0.344 (95% CI 0.0039–0.0648, P =0.027) for the MPI, respectively.…”

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

“…2 (center) compares the lifespan‐predictive ability of the well‐known Framingham Risk Score (Wilson et al ., 1998; Lloyd‐Jones et al ., 2004) and Pooled Cohort Score (Goff et al ., 2014) with our ‘focal exam’ estimates. These clinical scores were designed specifically to assess cardiovascular risk in a 5‐ to 10‐year timeframe, using some of the parameters we examined as well as smoking status, blood cholesterol, and age.…”

Endothelial Dysfunction, Increased Arterial Stiffness, and Cardiovascular Risk Prediction in Patients With Coronary Artery Disease: FMD‐J (Flow‐Mediated Dilation Japan) Study A

“…For asymptomatic patients without a history of atherosclerotic cardiovascular disease (ASCVD), risk stratification tools have been developed and validated to provide the foundation for targeted preventive efforts based on the individual's predicted risk with the concept of targeting the intensity of drug treatment interventions to the severity of the patient's cardiovascular risk 22, 23, 24, 25. On the other hand, patients with ASCVD have been referred to as high‐risk patients for whom prompt initiation of guideline‐recommended therapies should be considered to reduce the risk.…”