Faculty Bibliography

OBJECTIVES:This study compared risk discrimination for the prediction of coronary heart disease (CHD) and cardiovascular disease (CVD) deaths for the Pooled Cohort Equations (PCE), the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score (with and without coronary artery calcium [CAC]), and of simple addition of CAC to the PCE. BACKGROUND:The PCE predict 10-year risk of atherosclerotic CVD events, and the MESA Risk Score predicts risk of CHD. Their comparative performance for the prediction of fatal events is poorly understood. METHODS:We evaluated 53,487 patients ages 45 to 79 years from the CAC Consortium, a retrospective cohort study of asymptomatic individuals referred for clinical CAC scoring. Risk discrimination was measured using C-statistics. RESULTS:Mean age was 57 years, 35% were women, and 39% had CAC of 0. There were 421 CHD and 775 CVD deaths over a mean 12-year follow-up. In the overall study population, discrimination with the MESA Risk Score with CAC and the PCE was almost identical for both outcomes (C-statistics: 0.80 and 0.79 for CHD death, 0.77 and 0.78 for CVD death, respectively). Addition of CAC to the PCE improved risk discrimination, yielding the largest C-statistics. The MESA Risk Score with CAC and the PCE plus CAC showed the best discrimination among the 45% of patients with 5% to 20% estimated risk. Secondary analyses by estimated CVD risk strata showed modestly improved risk discrimination with CAC also among low- and high-estimated risk groups. CONCLUSIONS:Our findings support the current guideline recommendation to use, among available risk scores, the PCE for initial risk assessment and to use CAC for further risk assessment in a broad borderline and intermediate risk group. Also, in select individuals at low or high estimated risk, CAC modestly improved discrimination. Studies in unselected populations will lead to further understanding of the potential value of tools combining risk scores and CAC for optimal risk assessment.

BACKGROUND:Coronary calcium is a marker of coronary atherosclerosis and established predictor of cardiovascular risk in general populations; however, there are limited studies examining its prognostic value among older adults (≥75 years) and even less regarding its utility in older males compared with females. Accordingly, we sought to examine the prognostic significance of both absolute and percentile coronary calcium scores among older adults. METHODS:The multicenter Coronary Artery Calcium Consortium consists of 66,636 asymptomatic patients without cardiovascular disease. Participants ages ≥75 were included in this study and stratified by sex. Multivariable Cox regression models were constructed to assess cardiovascular and all-cause mortality risk by Agatston coronary calcium scores and percentiles. RESULTS:Among 2,474 asymptomatic patients (mean age 79 years, 10.4-year follow-up), prevalence of coronary artery calcium was 92%. For both sexes, but in females more so than males, higher coronary calcium score and percentiles were associated with increased cardiovascular and all-cause mortality risk. Those at the lowest coronary calcium categories (0-9 and <25 percentile) had significantly lower risk of cardiovascular and all-cause mortality relative to the rest of the population. Multivariable analyses of traditional cardiovascular risk factors and coronary artery calcium variables revealed that age and coronary calcium were the strongest independent predictors for adverse outcomes. CONCLUSIONS:Both coronary artery calcium scores and percentiles are strongly predictive of cardiovascular and all-cause mortality among older adults, with greater risk-stratification among females than males. Both low coronary artery calcium scores 0-9 and <25th percentile define relatively low risk older adults.

BACKGROUND AND AIMS:Statins do not decrease coronary artery calcium (CAC) and may increase existing calcification or its density. Therefore, we examined the prognostic significance of CAC among statin users at the time of CAC scanning. METHODS:We included 28,025 patients (6151 statin-users) aged 40-75 years from the CAC Consortium. Cox regression models were used to assess the association of CAC with coronary heart disease (CHD) and cardiovascular disease (CVD) mortality. Models were adjusted for traditional CVD risk factors. Additionally, we examined the predictive performance of CAC components including CAC area, volume, and density using an age- and sex-adjusted Cox regression model. RESULTS:Participants (mean age 53.9 ± 10.3 years, 65.0% male) were followed for median 11.2 years. There were 395 CVD and 182 CHD deaths. One unit increase in log CAC score was associated with increased risk of CVD mortality (hazard ratio (HR), 1.2; 95% CI = 1.1-1.3) and CHD mortality (HR, 1.2; 95% CI = 1.1-1.4)) among statin users. There was a small but significant negative interaction between CAC score and statin use for the prediction of CHD (p-value = 0.036) and CVD mortality (p-value = 0.025). The volume score and CAC area were similarly associated with outcomes in statin users and non-users. Density was associated with CVD and CHD mortality in statin naïve patients, but with neither in statin users. CONCLUSION:CAC scoring retains robust risk prediction in statin users, and the changing relationship of CAC density with outcomes may explain the slightly weaker relationship of CAC with outcomes in statin users.

Thoracic aortic calcium(TAC) is an important marker of extracoronary atherosclerosis with established predictive value for all-cause mortality. We sought to explore the predictive value of TAC for stroke mortality, independent of the more established coronary artery calcium (CAC) score. The CAC Consortium is a retrospectively assembled database of 66,636 patients aged ≥18 years with no previous history of cardiovascular disease, baseline CAC scans for risk stratification, and follow-up for 12 ± 4 years. CAC scans capture the adjacent thoracic aorta, enabling assessment of TAC from the same images. TAC was available in 41,066 (62%), and was primarily analyzed as present or not present. To account for competing risks for nonstroke death, we utilized multivariable-adjusted Fine and Gray competing risk regression models adjusted for traditional cardiovascular risk factors and CAC score. The mean age of participants was 53.8 ± 10.3 years, with 34.4% female. There were 110 stroke deaths during follow-up. The unadjusted subdistribution hazard ratio (SHR) for stroke mortality in those who had TAC present compared with those who did not was 8.80 (95% confidence interval [CI]: 5.97, 12.98). After adjusting for traditional risk factors and CAC score, the SHR was 2.21 (95% CI:1.39,3.49). In sex-stratified analyses, the fully adjusted SHR for females was 3.42 (95% CI: 1.74, 6.73) while for males it was 1.55 (95% CI: 0.83, 2.90). TAC was associated with stroke mortality independent of CAC and traditional risk factors, more so in women. The presence of TAC appears to be an independent risk marker for stroke mortality.

OBJECTIVES/OBJECTIVE:The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data. BACKGROUND:The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment. METHODS:The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT). RESULTS:The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001). CONCLUSIONS:The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.

AIMS/OBJECTIVE:Aortic valve calcification (AVC) has been shown to be associated with increased cardiovascular disease (CVD) risk; however, whether this is independent of traditional risk factors and coronary artery calcification (CAC) remains unclear. METHODS AND RESULTS/RESULTS:From the multicentre CAC Consortium database, 10 007 patients (mean 55.8±11.7 years, 64% male) with concomitant CAC and AVC scoring were included in the current analysis. AVC score was quantified using the Agatston score method and categorized as 0, 1-99, and ≥100. The endpoints were all-cause, CVD, and coronary heart disease (CHD) deaths. AVC (AVC>0) was observed in 1397 (14%) patients. During a median 7.8 (interquartile range: 4.7-10.6) years of study follow-up, 511 (5.1%) deaths occurred; 179 (35%) were CVD deaths, and 101 (19.8%) were CHD deaths. A significant interaction between CAC and AVC for mortality was observed (P<0.001). The incidence of mortality events increased with higher AVC; however, AVC ≥100 was not independently associated with all-cause, CVD, and CHD deaths after adjusting for CVD risk factors and CAC (P=0.192, 0.063, and 0.206, respectively). When further stratified by CAC<100 or ≥100, AVC ≥100 was an independent predictor of all-cause and CVD deaths only in patients with CAC <100, after adjusting for CVD risk factors and CAC [hazard ratio (HR): 1.93, 95% confidence interval (CI): 1.14-3.27; P=0.013 and HR: 2.71, 95% CI: 1.15-6.34; P=0.022, respectively]. CONCLUSION/CONCLUSIONS:Although the overall prognostic significance of AVC was attenuated after accounting for CAC, high AVC was independently associated with all-cause and CVD deaths in patients with low coronary atherosclerosis burden.