Coronary Artery Calcium Scanning Research Articles

Coronary Calcium Is Elevated in Patients with Myocardial Infarction without Standard Modifiable Risk Factors.

Objectives: Recent reports have highlighted myocardial infarction (MI) patients without standard modifiable risk factors (SMRF), noting them to be surprisingly common and to have a substantial risk of adverse outcomes. The objective of this study was to address the challenge of identifying at-risk patients without SMRF and providing preventive therapy. Methods: Patients presenting between 2001 and 2021 to Intermountain Health catheterization laboratories with a diagnosis of MI were included if they also had a coronary artery calcium (CAC) scan by computed tomography within 2 years. SMRF were defined as a clinical diagnosis or treatment of hypertension, hyperlipidemia, diabetes, or smoking. The co-primary endpoints in SMRF-less patients were: (1) proportion of patients with an elevated (>50%ile) CAC score, and (2) an indication for statin therapy (i.e., CAC ≥ 100 AU or ≥75%ile). The 60-day and long-term major adverse cardiovascular events were determined. A comparison set included MI patients with SMRF. Results: We identified 429 MI patients with a concurrent CAC scan, of which 60 had no SMRF. SMRF status did not distinguish most risk factors or interventions. No-SMRF patients had a high CAC prevalence and percentile (82% ≥ 50%ile; median, 80%ile), and 77% met criteria for preventive therapy. As expected, patients with SMRF had high CAC scores and percentiles. Outcomes were more favorable for No-SMRF status and for lower CAC scores. Conclusions: Patients without SMRF presenting with an MI have a high prevalence and percentile of CAC. Wider application of CAC scans, including in those without SMRF, is promising as a method to identify an additional at-risk population for MI and to provide primary preventive therapy.

Coronary Artery Calcification and High-Volume Physical Activity: Role of Lower Intensity versus Longer Duration of Exercise.

While high-volume physical activity (PA) has been linked to elevated coronary artery calcification (CAC), the role of intensity versus duration of PA has not been investigated. The purpose of the study was to examine the role of intensity versus duration of PA in relation to CAC. Data are from 23,383 apparently healthy men who completed a PA questionnaire and underwent CAC scanning as part of a preventive exam. Self-reported PA was categorized into 4 groups of average intensity and weekly duration of PA and (average intensity: 1, 3-5.9, 6-8.9, and 9-12 metabolic equivalents of task [METs]; weekly duration: 0, > 0-<2, 2-<5, and ≥5 hours/week). Mean CAC and CAC ≥ 100 Agatston Units (AU) were regressed separately on continuous or categorical average intensity and weekly duration of PA. The mean and standard deviation (SD) age was 51.7 (8.3) years, and mean CAC was 174.8 (543.6) AU with 23.5% of men presenting with CAC ≥ 100 AU. Higher average intensity of PA was related to lower mean CAC (-3.1%/MET, 95% confidence interval [CI]: -4.6, -1.6%/MET) and lower relative risk (RR) of CAC ≥ 100 AU (RR: 0.99, 95% CI: 0.98, 1.00/MET). Opposite trend was observed for the duration component wherein higher weekly duration of PA was significantly associated with greater mean CAC and RR of CAC ≥ 100 AU. Elevated CAC was associated with lower average intensity and longer duration of PA in men, providing new insight into the complex relationship between leisure-time PA behaviors and risk of CAC.

AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis

BackgroundCoronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. MethodsWe have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45–84) that were previously obtained for CAC scoring in the baseline examination (2000–2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 ​s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 ​± ​1.4 years. ResultsAt 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p ​< ​0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1–5 (all p ​< ​0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1–5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p ​< ​0.01). ConclusionAI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

IntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF). MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000–2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years. ResultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001). ConclusionAI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Trabecular bone mineral density as measured by thoracic vertebrae predicts incident hip and vertebral fractures: the multi-ethnic study of atherosclerosis.

Osteoporosis affects an enormous number of patients, of all races and both sexes, and its prevalence increases as the population ages. Few studies have evaluated the association between the vertebral trabecular bone mineral density(vBMD) and osteoporosis-related hip fracture in a multiethnic population, and no studies have demonstrated the predictive value of vBMD for fractures. We sought to determine the predictive value of QCT-based trabecular vBMD of thoracic vertebrae derived from coronary artery calcium scan for hip fractures in the Multi-Ethnic Study of Atherosclerosis(MESA), a nationwide multicenter cohort included 6814 people from six medical centers across the USA and assess if low bone density by QCT can predict future fractures. Measures were done using trabecular bone measures, adjusted for individual patients, from three consecutive thoracic vertebrae (BDI Inc, Manhattan Beach CA, USA) from non-contrast cardiac CT scans. Six thousand eight hundred fourteen MESA baseline participants were included with a mean age of 62.2 ± 10.2years, and 52.8% were women. The mean thoracic BMD is 162.6 ± 46.8mg/cm3 (95% CI 161.5, 163.7), and 27.6% of participants (n = 1883) had osteoporosis (T-score 2.5 or lower). Over a median follow-up of 17.4years, Caucasians have a higher rate of vertebral fractures (6.9%), followed by Blacks (4.4%), Hispanics (3.7%), and Chinese (3.0%). Hip fracture patients had a lower baseline vBMD as measured by QCT than the non-hip fracture group by 13.6mg/cm3 [P < 0.001]. The same pattern was seen in the vertebral fracture population, where the mean BMD was substantially lower 18.3mg/cm3 [P < 0.001] than in the non-vertebral fracture population. Notably, the above substantial relationship was unaffected by age, gender, race, BMI, hypertension, current smoking, medication use, or activity. Patients with low trabecular BMD of thoracic vertebrae showed a 1.57-fold greater risk of first hip fracture (HR 1.57, 95% CI 1.38-1.95) and a nearly threefold increased risk of first vertebral fracture (HR 2.93, 95% CI 1.87-4.59) compared to normal BMD patients. There is significant correlation between thoracic trabecular BMD and the incidence of future hip and vertebral fracture. This study demonstrates that thoracic vertebrae BMD, as measured on cardiac CT (QCT), can predict both hip and vertebral fractures without additional radiation, scanning, or patient burden. Osteopenia and osteoporosis are markedly underdiagnosed. Finding occult disease affords the opportunity to treat the millions of people undergoing CT scans every year for other indications.

Abstract P513: Coronary Artery Calcium Burden in Rural Alabama: Initial Findings From the Risk Underlying Rural Areas Longitudinal Cohort Study

Introduction: Individuals living in rural areas have a disproportionately high rate of cardiovascular disease (CVD) mortality. Coronary artery calcium (CAC) scanning may inform CVD risk stratification. However, the prevalence of CAC in rural areas is unknown. Research Question: What is the prevalence of CAC according to age, sex, and self-reported race (Black, White) in two rural counties in Alabama? Aims: To determine the prevalence of a CAC score &gt;0, a CAC score ≥100, and premature CAC among rural persons without clinical CVD and the extent to which they differ by demographic group. Methods: We studied 789 participants between the age of 25 and 64 years in the Risk Underlying Rural Areas Longitudinal (RURAL) Cohort Study sample in Alabama who underwent CAC scoring on non-contrast cardiac computed tomography. Premature CAC was defined as CAC &gt;0 for men &lt;45 years old, and women &lt;55 years old. Multivariable logistic regression (including age, sex, race) estimated adjusted odds ratios (aOR) for CAC &gt;0 and CAC ≥100. Results: The median age was 51 years, 70% were women, and 80% were Black. Among the were 37% with CAC &gt;0 the median CAC score was 64, while 14% had CAC ≥100 and 12% had premature CAC. Significant differences in all three CAC categories were consistently observed across age and sex, but not race ( Figure ). After adjusting for age and race, women had an approximate 70% lower odds of CAC &gt;0 (aOR=0.29, 95% CI: 0.19-0.44) and CAC ≥100 (aOR=0.28, 95% CI: 0.18-0.43) compared to men. There were no significant differences in CAC &gt;0 (aOR=0.70, 95% CI: 0.43-1.12) or CAC ≥100 (aOR=0.76, 95% CI: 0.46-1.25) for Black versus White persons independent of age and sex. Conclusion(s): In a Southern rural cohort of young and middle-aged persons, approximately one in three had prevalent CAC and one in eight had premature CAC. In contrast to previously published urban cohorts, rural Black adults had a similar CAC burden to their White counterparts. There is a need to explore the factors implicated in the high CAC burden within Southern rural communities.

A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.

This work proposed a convolutional neural network (CNN)-based method trained with images acquired with electron density phantoms to reduce quantum noise for coronary artery calcium (CAC) scans reconstructed with slice thickness less than 3mm. A DenseNet model was used to estimate quantum noise for CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm. Training data was acquired using electron density phantoms in three different sizes. The label images of the CNN model were real noise maps, while the input images of the CNN model were pseudo noise maps. Image denoising was conducted by subtracting the CNN output images from thin-sliced CAC scans. The efficacy of the proposed method was verified through both phantom study and patient study. By means of phantom study, the proposed method was proven effective in reducing quantum noise in CAC scans reconstructed with 1.5-mm slice thickness without causing significant texture change or variation in HU values. With regard to patient study, calcifications were more clear on the denoised CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm than on 3-mm slice images, while over-smooth changes were not observed in the denoised CAC scans reconstructed with 1.5-mm slice thickness. Our results demonstrated that the electron density phantoms can be used to generate training data for the proposed CNN-based denoising method to reduce quantum noise for CAC scans reconstructed with 1.5-mm slice thickness. Because anthropomorphic phantom is not a necessity, our method could make image denoising more practical in routine clinical practice.

Differences and Disparities Among Self-Referred and Physician-Referred Populations Undergoing Coronary Artery Calcium Scanning.

Coronary artery calcium computed tomography (CAC) is an important tool for identifying subclinical atherosclerosis and cardiovascular risk stratification. Despite robust evidence and inclusion in current guidelines, CAC is considered investigational by some US insurance carriers and requires out-of-pocket expenses. CAC can be obtained via self-referral (SR) or physician referral (PR). We aimed to examine differences in patient, socioeconomic, and CAC characteristics between referral groups. We evaluated demographic, medical history, and CAC results of consecutive patients with a CAC completed at one of multiple Wisconsin sites from March 1, 2019, to June 30, 2021. We separated patients into SR and PR groups. Through census data, we analyzed socioeconomic variables at the block level including race and ethnicity, median income, average household size, and high school completion in the areas where patients resided at the time of CAC. The final analysis included 19 726 patients: 13 835 (70.1%) PR and 5891 (29.9%) SR. Most patients in both groups were White (95.2% versus 95.1%), with the Black/African American population representing 2.7% (SR) and 2.3% (PR). The PR group had a higher prevalence of cardiovascular risk factors. SR patients were more likely to have a score of 0 (41.2% versus 38.1%; P<0.001); PR patients had a higher prevalence of CAC >300 (16.8% versus 14.8%; P<0.001). SR patients were more likely to be women (55.1% versus 48.9%; P<0.001) and were found to live in higher income areas (19.5% versus 16.4%; P<0.001). Patients from low-income areas comprised the smallest proportion in both groups (7.5%). Patients who obtain out-of-pocket CAC live predominantly in medium- and high-income areas, and patients from lower income locations are less likely to obtain CAC despite having more cardiovascular disease risk factors. Consideration should be made from a policy perspective to promote health equity and improve utilization of CAC testing among underrepresented groups.

Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.

Nontraditional Risk Markers for Incident Coronary Artery Calcium Among Persons ≥65 Years of Age

BackgroundThe initiation of coronary artery calcium (CAC) is an important physiologic milestone associated with increased cardiovascular disease risk. However, traditional risk factors (RF) do not perform well for predicting incident CAC among the 54 million older U.S. adults. ObjectivesThe authors sought to assess the association between nontraditional cardiovascular disease RF and incident CAC in older persons. MethodsThere were 815 MESA (Multi-Ethnic Study of Atherosclerosis) participants ≥65 years of age who had CAC = 0 at Visit 1 and a follow-up CAC scan. Multivariable adjusted Cox hazards ratios (aHR) and C-statistics were calculated to examine the association of nontraditional RF with incident CAC. ResultsThe mean age was 70.2 years and 67% were women. The median follow-up time to repeat CAC scan was 3.6 years (IQR: 2.6-9.2 years) and 45% of participants developed incident CAC. Albuminuria (aHR: 1.50, 95% CI: 1.07-2.09), carotid plaque (aHR: 1.32, 95% CI: 1.04-1.66), and thoracic aortic calcification (TAC) (aHR: 1.38, 95% CI: 1.10-1.75) were significantly associated with incident CAC, while higher levels of nontraditional RF including apolipoprotein-B, lipoprotein(a), high-sensitivity troponin T, and N-terminal pro-brain natriuretic peptide were not. When added to demographics, albuminuria, carotid plaque, and TAC provided a greater C-statistic improvement (+0.047, P = 0.004) vs all traditional RF combined (+0.033, P = 0.05). ConclusionsAmong nontraditional RF and measures of subclinical atherosclerosis, only albuminuria, carotid plaque, and TAC were significantly associated with incident CAC in persons ≥65 years of age. Identification of albuminuria or extracoronary atherosclerosis may help guide the timing of repeat CAC scoring in older persons with baseline CAC = 0.

Abstract 13881: Coronary Artery Calcium as a Risk Marker in Patients With Myocardial Infarction but Without Standard Modifiable Risk Factors (SMuRF-Less)

Introduction: Recent publications have reported that myocardial infarction (MI) patients (pts) without standard modifiable risk factors (SMuRF-less) are surprisingly common (14-27%) and have a notable risk of adverse outcomes. These reports raise the question of how these pts can be identified in advance and provided with effective preventive therapies (e.g., statins, aspirin). Methods: Pts presenting between 2001-2021 to Intermountain Healthcare catheterization laboratories with a diagnosis of MI were included if they also had a coronary artery calcium scan (CACS) by computed tomography within 2 years. SMuRF included a clinical diagnosis of, or treatment for, hypertension, hyperlipidemia, diabetes, and/or smoking. The co-primary endpoints for this study were the proportion of patients with an elevated (&gt;50%ile) CACS score (MESA criteria) and the presence of an indication for statin therapy with or without aspirin (i.e., &gt;=75%ile score or CAC&gt;=100 AU). Major adverse cardiovascular event (MACE) outcomes at 60-days and long-term were evaluated and compared to MI pts with SMuRF. Results: We identified 429 MI pts with a concurrent CACS, of which 60 were SMuRF-less and 369 had SMuRF. The 2 groups were similar in age, sex, most risk factors, and interventions, but fewer SMuRF-less pts had a history of heart failure or a family history (Table 1). SMuRF-less pts had a high prevalence and an increased percentile of CAC (82% &gt;=50%ile; median, 80%ile;), and 77% met criteria for preventive therapy. Pts with SMuRF, as expected, had high CAC scores and percentiles (Table 1). Outcomes were more favorable for SMuRF-less status (Table 2A) and for lower CAC scores (Table 2B). Conclusions: Despite absence of SMuRF, SMuRF-less pts presenting with an MI have a high prevalence and percentile of CAC. Wider application of CACS, to include those without SMuRF, appears promising as a method to identify an additional at-risk population for consideration of preventive therapy.

Abstract 19109: AI-Enabled Automated Cardiac Chambers Volumetry in ECG-Gated Coronary Calcium Scans vs. Non-Gated Lung Cancer Screening Scans

Introduction: We have developed an AI-enabled automated volume measurement of cardiac chambers (AutoChamber) that works on ECG-gated non-contrast coronary artery calcium scans and correlates well with contrast enhanced coronary CT angiography scans. Automated LA and LV volumes have shown to predict atrial fibrillation (Afib) and congestive heart failure. Hypothesis: In this study, we hypothesize that AutoChamber volumetry results in ECG-gated CAC scans correlated well with those of non-gated full-chest lung cancer screening scans in the same individuals. Methods: We have studied 169 cases of paired ECG-gated cardiac CT scans and non-gated lung scans. Mean±SD for age was 62±10 with 52% female. All caseswere asymptomatic and were scanned for preventive health assessment. AutoChamber was run on all cases by an independent operator who was not involvedin data analysis. P value was calculated using a two-tailed test of significance with α=0.05. Results: AutoChamber in cardiac scans vs lung scans reported volume (Mean±SD) for left atrium (LA) as 67.2 and 70.1, left ventricle (LV) 102.8 and 105.5 right atrium (RA) 81.9 and 85.7 right ventricle (RV) 140.7 and 134.2 left ventricle wall (LVW) 113.0 and 109.7 respectively (P &lt; 0.0001). Correlations between cardiac and lung scans for each cardiac chamber: LA (R2= 0.85), LV (R 2= 0.93), RA (R2= 0.91), RV (R2= 0.92), and left ventricular wall (LVW) R2= 0.95. (P &lt;0.0001 for all). Conclusions: AutoChamber volumetry results are similar in ECG-gated cardiac scans vs non-gated full-chest lung scans. This AI-enabled automated tool is promising as it can provide added value to patients undergoing coronary calcium and lung cancer screening scans flagging enlarged cardiac chambers at risk of future Afib and heart failure.

Utility of coronary artery calcium in refining 10-year ASCVD risk prediction using a Thai CV risk score.

Coronary artery calcium (CAC) scanning is a valuable additional tool for calculating the risk of cardiovascular (CV) events. We aimed to determine if a CAC score could improve performance of a Thai CV risk score in prediction of 10-year atherosclerotic cardiovascular disease (ASCVD) risk for asymptomatic patients with CV risk factors. This was a retrospective cohort study that enrolled asymptomatic patients with CV risk factors who underwent CAC scans between 2005 and 2013. The patients were classified as low-, intermediate-, or high-risk (<10%, 10%-<20%, and ≥20%, respectively) of having ASCVD within 10-years based on a Thai CV risk score. In each patient, CAC score was considered as a categorical variable (0, 1-99, and ≥100) and natural-log variable to assess the risk of developing CV events (CV death, non-fatal MI, or non-fatal stroke). The C statistic and the net reclassification improvement (NRI) index were applied to assess whether CAC improved ASCVD risk prediction. A total of 6,964 patients were analyzed (mean age: 59.0 ± 8.4 years; 63.3% women). The majority of patients were classified as low- or intermediate-risk (75.3% and 20.5%, respectively), whereas only 4.2% were classified as high-risk. Nearly half (49.7%) of patients had a CAC score of zero (no calcifications detected), while 32.0% had scores of 1-99, and 18.3% of ≥100. In the low- and intermediate-risk groups, patients with a CAC ≥100 experienced higher rates of CV events, with hazard ratios (95% CI) of 1.95 (1.35, 2.81) and 3.04 (2.26, 4.10), respectively. Incorporation of ln(CAC + 1) into their Thai CV risk scores improved the C statistic from 0.703 (0.68, 0.72) to 0.716 (0.69, 0.74), and resulted in an NRI index of 0.06 (0.02, 0.10). To enhance the performance of the Thai CV risk score, a revision of the CV risk model was performed, incorporating ln(CAC + 1), which further increased the C statistic to 0.771 (0.755, 0.788). The addition of CAC to traditional risk factors improved CV risk stratification and ASCVD prediction. Whether this adjustment leads to a reduction in CV events and is cost-effective will require further assessment.

Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score.

To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR). We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS. The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions. The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625mm compared to 2.5mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625mm slices compared to 2.5mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

The novel H2VK-65 clinical risk assessment tool predicts high coronary artery calcium score in symptomatic patients referred for coronary computed tomography angiography.

Coronary computed tomographic angiography (CCTA) has emerged as a powerful imaging modality for the detection and prognostication of individuals with suspected coronary artery disease (CAD). High amounts of coronary artery calcium (CAC) significantly obscure the interpretation of CCTA. Clinical risk assessment tools and data specific to predictors of high CAC in symptomatic patients are limited. Consecutive patients who underwent CAC scan and CCTA to diagnose CAD during 2016-2020 were included. A high CAC score was defined as >400 by Agatston method. Univariate and multivariate analyses were performed to determine the predictors of high CAC. The clinical risk score was derived from factors independently associated with high CAC. The derivation cohort was composed of 465 patients; this score was validated in 98 patients. The mean age was 63 ± 11 years, 53% were female, and 15.9% had high CAC scores. The independent predictors of high CAC scores were age >65 years (odds ratio [OR] 3.02, 95% confidence interval (95%CI) 1.56-5.85, p = 0.001), chronic kidney disease (CKD) (OR 11.09, 95%CI 3.38-36.38, p < 0.001), heart failure (OR 6.52, 95%CI 2.23-19.09, p = 0.001), hypertension (OR 26.44, 95%CI 9.02-77.44, p < 0.001), and vascular diseases, including ischemic stroke/transient ischemic attack and peripheral arterial disease (OR 20.96, 95%CI 4.19-104.86, p < 0.001). The H2VK-65 (Hypertension, Heart failure, Vascular diseases, CKD, and Age > 65) score allocates 1 point for age >65, 2 points for CKD or heart failure, and 3 points for hypertension or vascular diseases. Using a threshold of ≥4 points, the sensitivity and specificity to detect high CAC was 81% and 80%, respectively. The area under the curve was 0.88 and 0.85 in the derivation and validation cohorts, respectively. The novel H2VK-65 score demonstrated good performance for predicting high CAC scores in symptomatic patients referred for CCTA.

Validation of Opportunistic Artificial Intelligence-Based Bone Mineral Density Measurements in Coronary Artery Calcium Scans

BackgroundPreviously we reported a manual method of measuring thoracic vertebral bone mineral density (BMD) using quantitative CT in noncontrast cardiac CT scans used for coronary artery calcium (CAC) scoring. In this report, we present validation studies of an artificial intelligence–based automated BMD measurement (AutoBMD) that recently received FDA approval as an opportunistic add-on to CAC scans. MethodsA deep learning model was trained to detect vertebral bodies. Subsequently, signal processing techniques were developed to detect intervertebral discs and the trabecular components of the vertebral body. The model was trained using 132 CAC scans comprising 7,649 slices. To validate AutoBMD, we used 5,785 cases of manual BMD measurements previously reported from CAC scans in the Multi-Ethnic Study of Atherosclerosis. ResultsMean ± SD for AutoBMD and manual BMD were 166.1 ± 47.9 mg/cc and 163.1 ± 46 mg/cc, respectively (P = .006). Multi-Ethnic Study of Atherosclerosis cases were 47.5% male and 52.5% female, with age 62.2 ± 10.3. A strong correlation was found between AutoBMD and manual measurements (R = 0.85, P < .0001). Accuracy, sensitivity, specificity, positive predictive value and negative predictive value for AutoBMD-based detection of osteoporosis were 99.6%, 96.7%, 97.7%, 99.7% and 99.8%, respectively. AutoBMD averaged 15 seconds per report versus 5.5 min for manual measurements (P < .0001). ConclusionsAutoBMD is an FDA-approved, artificial intelligence–enabled opportunistic tool that reports BMD with Z-scores and T-scores and accurately detects osteoporosis and osteopenia in CAC scans, demonstrating results comparable to manual measurements. No extra cost of scanning and no extra radiation to patients, plus the high prevalence of asymptomatic osteoporosis, make AutoBMD a promising candidate to enhance patient care.

Effect of insulin resistance on CAC scores in cancer survivors.

e24056 Background: Many cancer (ca) survivors exhibit signs of insulin resistance (IR), an important risk factor for the development of coronary artery disease (CAD). Paramount in survivorship care is prevention of cardiovascular disease. Coronary artery calcium (CAC) scans offer a risk assessment of cardiovascular (CV) disease before cardiac damage has occurred. We investigated how IR affects CAC scores in cancer survivors. We hypothesized that CAC scores differ significantly between insulin-sensitive- and -resistant cancer survivors. Methods: We enrolled 90 cancer survivors of a large community hospital from March 2021 to January 2022 into this pilot study. Patients were subdivided into three groups: insulin-sensitive (IS), insulin-resistant/prediabetic and insulin-resistant/diabetic. Patients were tested for fasting insulin, -glucose, HgbA1c and lipids. Patients without evidence for prediabetes or diabetes also underwent an oral glucose tolerance test (oGTT). All patients received a CAC scan. Results: 32 patients were IS, 29 patients were IR/prediabetic and 29 patients were IR/diabetic. 17 CAC scans in the IS group, 6 CAC scans in the IR/prediabetic group and 5 CAC scans in the IR/diabetic group showed an Agatston score of 0. The p-value between the three groups was statistically significant ( p=0.005) where as the IR/prediabetic- and the IR/diabetic group did not differ statistically from each other. The mean MESA 10-year CHD risk with CAC was 7.8. There was a highly significant difference between the 3 groups: the IS group had a mean of 5.3, the IR/prediabetic group had a mean of 7.3, and the IR/diabetic group had a mean of 11.0 ( p &lt; 0.001). The two IR groups did not differ statistically (p=0.076). Conclusions: Our study showed that IR including prediabetes significantly increases the MESA 10-yr. CHD Risk with CAC in cancer survivors. Survivors with IR also have less frequent zero CAC scores than insulin-sensitive survivors. Survivors disproportionately exhibit insulin resistance, partly due to the association of certain types of cancer with IR. This trial highlights the importance of screening survivors for IR. Survivors diagnosed with IR should be screened for CAD more frequently than the general population. CAC scans are an inexpensive and efficient way of screening asymptomatic cancer survivors for CAD.

Risk Assessment and Coronary Artery Calcium Scoring in Transgender and Gender-Diverse Individuals Receiving Gender-Affirming Hormone Therapy

ObjectiveTo evaluate cardiovascular risk factors and prevalent coronary artery disease (CAD) using Coronary Artery Calcium (CAC) scoring in transgender and gender-diverse (TGD) individuals receiving gender-affirming hormone therapy (GAHT) and compare the CAC scores of TGD individuals with those of the general population. MethodsTGD individuals aged ≥30 years, without known risk factors for cardiovascular disease (CVD), other than tobacco use and family history of CVD, on GAHT were recruited, and baseline information, including metabolic parameters, was collected. CAC scores were obtained and compared with those of a cisgender age-matched population. ResultsOf 25 transwomen recruited, 24 underwent CAC scans. Of them, 2 (8.3%) had a CAC score of >0 to 99 and 1 (4.1%) had a CAC score of ≥100. Of 22 transmen recruited, 16 underwent CAC scans. Of them, 26 (12.5%) had a CAC score of >0 to 99 and none had a CAC score of ≥100. Framingham Risk Scores were not correlated with the presence of CAC. ConclusionThe presence of CAC in this small cohort of TGD individuals on GAHT was similar to that in the cisgender age-matched population. CAC scoring is a means to assess the prevalence of CAD in TGD individuals and identify those in whom aggressive risk reduction is indicated.

Predicting Significant Coronary Obstruction in a Population with Suspected Coronary Disease and Absence of Coronary Calcium: CORE-64 / CORE320 Studies.

Coronary artery calcium (CAC) scanning can be performed using non-contrast computed tomography to predict cardiovascular events, but has less value for risk stratification in symptomatic patients. To identify and validate predictors of significant coronary obstruction (SCO) in symptomatic patients without coronary artery calcification. A total of 4,258 participants were screened from the CORE64 and CORE320 studies that enrolled patients referred for invasive angiography, and from the Quanta Registry that included patients referred for coronary computed tomography angiography (CTA). Logistic regression models evaluated associations between cardiovascular risk factors, CAC, and SCO. An algorithm to assess the risk of SCO was proposed for patients without CAC. Significance level of 5% was used in the analyses. Of the 509 participants of the CORE study, 117 (23%) had zero coronary calcium score; 13 (11%) patients without CAC had SCO. Zero calcium score was related to younger age, female gender, lower body mass index, no diabetes, and no dyslipidemia. Being a current smoker increased ~3.5 fold the probability of SCO and other CV risk factors were not significantly associated. Considering the clinical findings, an algorithm to further stratify zero calcium score patients was proposed and had a limited performance in the validation cohort (AUC 58; 95%CI 43, 72). A lower cardiovascular risk profile is associated with zero calcium score in a setting of high-risk patients. Smoking is the strongest predictor of SCO in patients without CAC.