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Transcatheter Aortic Valve Replacement in Patients With Multivalvular Heart Disease Diagnostic accuracy of cardiac positron emission tomography versus single photon emission computed tomography for coronary artery disease: a bivariate meta-analysis Contemporary Presentation and Management of Valvular Heart Disease: The EURObservational Research Programme Valvular Heart Disease II Survey Myocardial bridging of the left anterior descending coronary artery is associated with reduced myocardial perfusion reserve: a 13N-ammonia PET study Long-Term All-Cause and Cause-Specific Mortality in Asymptomatic Patients With CAC ≥1,000: Results From the CAC Consortium Long-term results after PCI of unprotected distal left main coronary artery stenosis: the Bifurcations Bad Krozingen (BBK)-Left Main Registry Valve‐in‐Valve for Degenerated Transcatheter Aortic Valve Replacement Versus Valve‐in‐Valve for Degenerated Surgical Aortic Bioprostheses: A 3‐Center Comparison of Hemodynamic and 1‐Year Outcome Comparison of 1-Year Pre- And Post-Transcatheter Aortic Valve Replacement Hospitalization Rates: A Population-Based Cohort Study Complex PCI procedures: challenges for the interventional cardiologist Impact of myocardial fibrosis on left ventricular remodelling, recovery, and outcome after transcatheter aortic valve implantation in different haemodynamic subtypes of severe aortic stenosis
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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.
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