Role of Coronary Artery Calcium in Cardiovascular Risk Stratification and Management in the Aging Population

2014 ◽  
pp. 249-257
Author(s):  
Craig R. Butler ◽  
Paolo Raggi
Keyword(s):  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Risk Stratification ◽  
Coronary Artery Calcium ◽  
Aging Population ◽  
Cardiovascular Risk Stratification

scholarly journals Soluble Tumor Necrosis Factor Receptor 1 is Associated With Cardiovascular Risk in Persons With Coronary Artery Calcium Score of Zero

2021 ◽  
Vol 6 (2) ◽  
pp. 135-148
Author(s):  
Tony Dong ◽  
Graham Bevan ◽  
David Zidar ◽  
Miguel Cainzos Achirica ◽  
Khurram Nasir ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Tumor Necrosis Factor ◽  
Risk Stratification ◽  
Coronary Artery Calcium ◽  
Tumor Necrosis ◽  
Coronary Revascularization ◽  
Cardiovascular Risk Stratification ◽  
Asymptomatic Patients ◽  
Necrosis Factor

Background: A coronary artery calcium (CAC) score of zero confers a low but nonzero risk of atherosclerotic cardiovascular events (CVD) in asymptomatic patient populations, and additional risk stratification is needed to guide preventive interventions. Soluble tumor necrosis factor receptors (sTNFR-1 and sTNFR-2) are shed in the context of TNF-alpha signaling and systemic inflammation, which play a role in atherosclerosis and plaque instability. We hypothesized that serum sTNFR-1 concentrations may aid in cardiovascular risk stratification among asymptomatic patients with a CAC score of zero.  Methods: We included all participants with CAC=0 and baseline sTNFR-1 measurements from the prospective cohort Multi-Ethnic Study of Atherosclerosis (MESA). The primary outcome was a composite CVD event (myocardial infarction, stroke, coronary revascularization, cardiovascular death).  Results: The study included 1471 participants (mean age 57.6 years, 64% female), with measured baseline sTNFR-1 ranging from 603 pg/mL to 5544 pg/mL (mean 1294 pg/mL ±378.8 pg/mL). Over a median follow-up of 8.5 years, 37 participants (2.5%) experienced a CVD event. In multivariable analyses adjusted for Framingham Score, doubling of sTNFR-1 was associated with a 3-fold increase in the hazards of CVD (HR 3.0, 95% CI: 1.48- 6.09, P = 0.002), which remained significant after adjusting for traditional CVD risk factors individually (HR 2.29; 95% CI: 1.04-5.06, P=0.04). Doubling of sTNFR-1 was also associated with progression of CAC >100, adjusted for age (OR 2.84, 95% CI: 1.33-6.03, P=0.007).  Conclusions: sTNFR-1 concentrations are associated with more CVD events in participants with a CAC score of zero. Utilizing sTNFR-1 measurements may improve cardiovascular risk stratification and guide primary prevention in otherwise low-risk individuals. 

scholarly journals Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

2020 ◽  
Vol 1 (1) ◽  
pp. 51-61
Author(s):  
Peter D Farjo ◽  
Naveena Yanamala ◽  
Nobuyuki Kagiyama ◽  
Heenaben B Patel ◽  
Grace Casaclang-Verzosa ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Risk Stratification ◽  
Coronary Artery Calcium ◽  
Invasive Coronary Angiography ◽  
Area Under The Curve ◽  
Suspected Coronary Artery Disease ◽  
Major Adverse Cardiovascular Events ◽  
Atherosclerotic Cardiovascular Disease ◽  
Cardiovascular Risk Stratification

Abstract Aims Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms. Methods and results In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years. Conclusion ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.

scholarly journals Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs

2021 ◽  
Vol 3 (5) ◽  
pp. e306-e316
Author(s):  
Tyler Hyungtaek Rim ◽  
Chan Joo Lee ◽  
Yih-Chung Tham ◽  
Ning Cheung ◽  
Marco Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Risk Stratification ◽  
Coronary Artery Calcium ◽  
Cardiovascular Risk Stratification ◽  
Retinal Photographs
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