CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Longitudinal Change in Galectin-3 and Incident Cardiovascular Outcomes Heart Failure With Recovered Left Ventricular Ejection Fraction: JACC Scientific Expert Panel Prognostic implications of baseline 6‐min walk test performance in intermediate risk patients undergoing transcatheter aortic valve replacement Colchicine Reduces Cardiovascular Events in Chronic Coronary Disease The Evolution of β-Blockers in Coronary Artery Disease and Heart Failure (Part 1/5) Incidence and Outcomes of Surgical Bailout During TAVR : Insights From the STS/ACC TVT Registry Impact of Positive and Negative Lesion Site Remodeling on Clinical Outcomes : Insights From PROSPECT A Combined Optical Coherence Tomography and Intravascular Ultrasound Study on Plaque Rupture, Plaque Erosion, and Calcified Nodule in Patients With ST-Segment Elevation Myocardial Infarction: Incidence, Morphologic Characteristics, and Outcomes After Percutaneous Coronary Intervention Cardiac resynchronization therapy with a defibrillator (CRTd) in failing heart patients with type 2 diabetes mellitus and treated by glucagon-like peptide 1 receptor agonists (GLP-1 RA) therapy vs. conventional hypoglycemic drugs: arrhythmic burden, hospitalizations for heart failure, and CRTd responders rate From organic and inorganic phosphates to valvular and vascular calcifications

Original Research30 Jul 2018 [Epub ahead]

JOURNAL:Circulation. Article Link

The Astronaut Cardiovascular Health and Risk Modification (Astro-CHARM) Coronary Calcium Atherosclerotic Cardiovascular Disease Risk Calculator

A Khera , MJ Budoff , CJ O’Donnell et al. Keywords: coronary artery calcium; risk prediction

ABSTRACT


BACKGROUND - Coronary artery calcium (CAC) is a powerful novel risk indicator for atherosclerotic cardiovascular disease (ASCVD). Currently, there is no available ASCVD risk prediction tool that integrates traditional risk factors and CAC.


METHODS - To develop a CAC ASCVD risk tool for younger individuals in the general population, subjects aged 40-65 without prior CVD from three population-based cohorts were included. Cox proportional hazards models were developed incorporating age, sex, systolic blood pressure, total and HDL cholesterol, smoking, diabetes, hypertension treatment, family history of MI, high-sensitivity CRP (hs-CRP), and CAC scores (Astro-CHARM model) as dependent variables and ASCVD (non-fatal/fatal MI or stroke) as the outcome. Model performance was assessed internally, and validated externally in a fourth cohort.

RESULTS - The derivation study comprised 7382 individuals with mean age 51 years, 45% female, and 55% non-white. The median CAC was 0 (25-75th [0,9]) and 304 ASCVD events occurred in median 10.9 years of follow-up. The c-statistic was 0.784 for the risk factor model, and 0.817 for Astro-CHARM (p<0.0001). Compared with the risk factor model, the Astro-CHARM model resulted in integrated discrimination improvement (IDI=0.0252) as well as net reclassification improvement (NRI=0.121, p<0.0001). The Astro-CHARM model demonstrated good discrimination (c=0.78) and calibration (Nam-D’Agostino χ2:13.2, p=0.16) in the validation cohort (n=2057; 55 events). A mobile application and web-based tool were developed to facilitate clinical application of this tool ( www.AstroCHARM.org).

CONCLUSIONS - The Astro-CHARM tool is the first integrated ASCVD risk calculator to incorporate risk factors, including hs-CRP and family history, and CAC data. It improves risk prediction compared with traditional risk factor equations and could be useful in risk-based decision making for CV disease prevention in the middle-aged general population.