Coronary Artery Calcium Research Articles

Cardiovascular Risk in Pediatric Renal Transplant Recipients.

The survival of pediatric chronic kidney disease (CKD) patients has improved in recent decades due to advances in dialysis and transplantation. However, cardiovascular disease (CVD) emerges as the main cause of mortality in patients with CKD. To estimate cardiovascular risk in children with CKD at least 1 year after kidney transplantation. In addition, the possible association of cardiovascular risk with classic biochemical markers and potential new markers of this outcome was investigated. An observational ambidirectional (retrospective capture of risk factors and prospective study of outcomes) research including 75 patients who underwent renal transplant between 2003 and 2013 with postoperative follow-up of at least 1 year was conducted. The outcome variables adopted were the LV mass Z-score and the presence of coronary calcification on computed tomography using calcium Agatston score. Only one patient had an elevated calcium score, and three children (4%) had an LV mass Z-score ≥ 2.0. After multivariable analysis, only gender, serum triglyceride, and serum renalase concentration remained significantly associated with LV mass. The low incidence of cardiovascular changes in the population studied confirms the benefit of transplantation for the cardiovascular health of children. Nevertheless, long-term follow-up of these patients is recommended, given the limited duration of kidney function provided by transplantation and the high likelihood of further dialysis and kidney transplants being required in these children.

High prevalence of coronary artery calcium and aortic calcification in adults with repaired coarctation of the aorta and interrupted aortic arch

Abstract Background There is a high rate of late cardiovascular mortality in patients with repaired coarctation of the aorta (CoA) and interrupted aortic arch (IAA). Coronary artery calcium (CAC) is a marker of cardiovascular disease and there is growing evidence for aortic calcification as a predictor. Recent research in the general population suggests any CAC in males <35 years old and females ≤45 years places them at ≥90th percentile. Aims We aimed (1) to determine the prevalence of CAC, thoracic aorta calcification (TAC), and abdominal aorta calcification (AAC) in patients with CoA or IAA repair, and (2) to compare patients to a control group of Tetralogy of Fallot (TOF) patients, a condition with no known increased risk of atherosclerotic cardiovascular disease. Methods Retrospective cohort study of adult survivors of CoA/IAA repair followed up at an adult congenital heart disease (ACHD) clinic. Patients with at least 1 chest, abdomen, or pelvis computerised tomography (CT) scan for any indication were included. The presence/absence of calcification in the coronary arteries or aorta was assessed qualitatively. Patients on antihypertensive and statin therapy were considered hypertensive and hypercholesterolaemic, respectively. Patients were compared to adult survivors of TOF repair with CT scans for any indication (controls). Results Ninety-three CoA/IAA patients (87% CoA/13% IAA, median age at latest follow-up 34 years) had a total of 169 chest and 37 abdomen/pelvis CTs; 82, 93, and 37 patients had at least 1 CT suitable for CAC, TAC, and AAC review, respectively. CAC was present in 22% (18/82), TAC in 33% (31/93), and AAC in 23% (5/22) of CoA/IAA patients. CAC was present in 12% (18/153) and TAC in 11% (18/158) of TOF patients. CoA/IAA patients were more likely to have CAC and TAC than TOF patients (p=0.04 and p<0.0001; respectively) and be younger at the time of diagnosis (CAC p=0.03, TAC p=0.0001) despite TOF patients being older at time of latest CT scan (p=0.0003) (Table 1). Males aged <35 years and females ≤45 years represented 44% (8/18) of CoA/IAA patients with CAC. CoA/IAA patients with CAC were more likely to be hypertensive and hypercholesterolaemic than those without (78% [14/18] vs 48% [31/64], p=0.03; and 28% [5/18] vs 0% [0/64], p<0.0001; respectively) and was similar for TAC (hypertension p=0.005, hypercholesterolaemia p=0.04) but not AAC. After a mean follow-up of 11±7 years from first ACHD clinic, of the 93 patients, 2 patients died, 5 had coronary artery disease (at least mild stenosis on invasive angiography), 3 had a stroke, and 70% (65/93) were considered hypertensive at latest follow-up. Conclusions There is a high prevalence of CAC, aortic calcification, and hypertension in young adults with repaired CoA/IAA. Coronary and thoracic aortic calcification occurs at a younger age compared to controls. Further studies are warranted to assess the impact of aggressive cardiovascular risk reduction in this high-risk group.

Opportunistic screening of coronary artery calcification on non-gated conventional CT scans using artificial intelligence

Abstract Background Recent advancements in artificial intelligence (AI) led to the development of automatic Coronary artery calcification (CAC) analysis based on chest CT scans. The objective of this study is to assess the impact of AI-based CAC evaluation on improving the allocation of add-on therapies and further evaluation. Methods We used a novel propriety AI software to estimate CAC from non-gated, non-contrast chest CT scans. Patients were categorized into groups: low CAC 0-99 Agatston unit (AU), moderate CAC 100-399AU, and high CAC ≥ 400 AU. Exclusion criteria included prior myocardial infarction, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), and life expectancy < 2 years. Patients classified as high CAC were invited to a dedicated clinic to perform a risk factor and clinical assessment, initiate appropriate medication, and refer to further testing as needed. For low-moderate CAC, referring physicians were informed of CAC status, encouraging guideline-based optimal preventive management. Results 1272 eligible patients between January 1th, 2023 to February 29th, 2024 were evaluated for inclusion. 641/1272 (50.3%) were excluded. Out of 631 enrolled patients, 84 (13%) patients were classified as high CAC, 163 (26%) as moderate and 348 (61%) as low CAC. At the dedicated clinic, appropriate low-density lipoprotein (LDL) goals were assigned and statins were initiated or dose adjusted where necessary. Twenty patients were referred to myocardial perfusion imaging, of which 2 were referred for invasive coronary angiography, one of them underwent PCI (Figure), and the other was referred for CABG. Conclusion This ongoing study indicates routine CAC quantification using AI software on chest CT scans can identify patients who may benefit from preventive cardiology services and lead them to appropriate cardiac treatment pathway. Further follow-up is required to assess its clinical impact.

Lipoprotein(a) as a predictive marker of coronary artery calcification (CAC). The results from the STAR-Lp(a) cohort study.

Abstract Background Lipoprotein(a) [Lp(a)] concentration is an independent risk factor for cardiovascular disease, including atherosclerosis. Purpose The aim of the study was to assess the relationship between the elevated Lp(a) levels, and the risk of developing atherosclerosis assessed using coronary artery calcium (CAC) score. Methods The analysis was conducted among 553 primary prevention patients (65.7% women, the average age was 65.8±10.1 years) from the outpatient specialist care who underwent coronary computed tomographic angiography (CTA). This group accounted for 22.3% of all patients (n=2475) included in the registry. Lp(a) levels were assessed in three groups: <30 mg/dL (<75 nmol/l) (normal level), 30-50 mg/dL (75-125 nmol/l) (moderate cardiovascular risk) and >50 (>125 nmol/l) (high cardiovascular risk). Patients were divided into groups: with normal CAC score (CAC=0), with CAC 1-100 (presence of atherosclerotic), and those with CAC>100 (advanced atherosclerosis). Statistical analysis was performed using STATISTICA 13.1. Quantitative variables were known by providing descriptive statistics (mean and standard deviation [SD]). The chi-square test was used to assess the relationship between quantitative variables and Mann-Whitney and Kruskal Wallis in the case of qualitative variables. Pearson's correlation coefficient was used to measure correlation. To determine the effect of factors on the probability of occurrence CAC=0 a multivariate logistic regression model was built. P<0.05 was judged as significant. Results Positive significant correlation between CAC score and age was revealed (r=0.266, p<0.001). An analogous relationship was observed regarding to glucose level (r=-0.123, p=0.012). This was not observed for Lp(a). However, we showed a significant relationship between the level of Lp(a) and CAC score (r=0.113, p=0.009) (Fig. 1). Lp(a) levels increased with the CAC score increase. For every 10 mg/dL (25 nmol/l) of Lp(a), CAC score increases by 15.7±0.57 (p=0.006). The mean CAC among patients with normal Lp(a) level was 203.1 (±412.6) and respectively 206.6 (±460.1) in those with moderate cardiovascular risk and 335.6 (±784.9) for people with high risk (p=0.183). In the regression model the factors determining the absence of atherosclerosis (CAC=0; "power of zero") included female gender (OR = 4.05; 95CI: 2.48-6.59), age under 65 (OR = 0.31; 95CI: 0.07-1.34 for aged 40-65), and Lp(A)≤50 mg/dl (≤125 nmol/l) (OR=2.26; 95CI: 1.12-4.61). People with hypertension had a significantly lower odds of CAC=0 (OR=0.58; 95CI: 0.38-0.89). Conclusions The study showed a significant linear relationship between increased Lp(a) levels, and the progression of atherosclerosis expressed by the CAC score. Results confirm some existing recommendations that if an elevated Lp(a) level is found in a patient for primary prevention, a CT scan of the coronary arteries with assessment of the CAC score should be considered for more appropriate risk stratification.

Incidental detection and quantification of coronary artery calcification in ungated thoracic computed tomography for prognostic assessment. Preliminary results from the ICARUS registry

Abstract Background Coronary artery calcium (CAC) score improves cardiovascular risk stratification in asymptomatic individuals. However, whether CAC can be quantified in ungated thoracic computed tomography (uTCT) performed for other indications, and if this CAC score associates with cardiovascular outcomes, hasn’t been confirmed yet. Purpose We aim to analyse the extension of CAC and its prognostic value in terms of major adverse cardiovascular events (MACE) in patients who underwent uTCT for other indications. Methods We analysed a preliminary sample of 115 uTCT performed in 2015 in our hospital. We studied the presence and extension of CAC and quantified the global CAC score by Agatston method using validated software. Patients were categorized according to Coronary Artery Calcium Data and Reporting System (CAC-DRS) categories. Cardiovascular risk factors, lipidic control and pharmacological therapy of the cohort were registered. We analysed the time to first 3P-MACE, defined as cardiovascular death, non-fatal myocardial infarction and non-fatal stroke, whichever occurred first. A p-value <0.05 was considered statistically significant. Results Mean age of the cohort was 60.8±16.1 years (53% male, 53% smokers). Mean LDL-cholesterol was 121.67±34.91 mg/dL, and 35.1% received lipid-lowering therapy. In more than half (n=59, 51.3%) of the population CAC was detected. Mean CAC score was 312.99±774.48 Agatston units, and patients were categorized in CAC-DRS category 1 (n=19, 16.5%), category 2 (n=15, 13%) and category 3 (n=25, 21.7%). Patients with CAC were older (71±10.1 vs. 50.1±14.2 years, p<0.001), had higher burden of cardiovascular risk factors and prevalence of chronic kidney disease (CKD; n=8, 13.6% vs. n=1, 1.8%, p=0.03) and higher levels of triglycerides (143.2±80.5 vs. 106.8±36.2 mg/dL, p=0.004), but not LDL-cholesterol. Eleven (9.6%) patients presented 3P-MACE during a mean follow-up of 6.55±2.82 years. CAC score independently predicted 3P-MACE (HR 1.06 [1.01-1.11] per 100 Agatston units, p=0.02) along with CKD (HR 6.94 [1.58-30.46], p=0.01) and basal glucose levels (HR 1.03 [1.02-1.04] per mg/dL, p<0.001). Conclusions CAC can be incidentally detected in more than half of our unselected cohort of patients undergoing uTCT for other indications. CAC score quantification by Agatston method is feasible in uTCT and associates with a higher risk of MACE during follow-up.Incidental CAC detection in uTCT

Visual coronary artery calcium assessment on routine chest CT can readily and cheaply identify liver transplant recipients with a low cardiac risk

Abstract Background Peri- and postoperative cardiac complications play an important role in survival of patients after liver transplantation (LT). However, the optimal approach for cardiac risk prediction in these patients has been debated on. LT recipients routinely receive non-gated computed tomography (CT) of the abdomen and chest to follow-up on their liver diseases. This study analyzed whether performance of coronary artery calcium (CAC) assessment on freely available scans can enhance cardiac risk stratification of LT recipients. Methods Consecutive LT recipients from 2008-2023 were included and analyzed retrospectively. CAC was scored visually on non-gated CT on a semi-quantitative ordinal scale and stratified in two groups according to CAC severity. Peri-operative (≤90 days) and post-operative all-cause mortality and cardiac events were analyzed. Furthermore, all-cause mortality and cardiac events were analyzed and compared between the groups with a Kaplan Meier curve and Cox proportional hazard regression. Results A total of 149 patients were included with a median age of 58 years. The median follow-up time was 5 years. The majority (74%) of patients had no or mild CAC. None of these patients died perioperatively due to cardiac causes, or experienced peri-operative cardiac events. The post-operative 10-year survival of LT recipients with no or mild calcifications was significantly better (p<0.01) than patients with severe CAC. The 10-year cardiac event-free survival was significantly better in patients with no or mild CAC (p<0.01) and the incidence increased >4-fold from only 4% in patients with no-mild CAC to 18% in patients with severe CAC. Of interest, severe CAC was a significant predictor of cardiac events (HR 4.6, p=0.02), while classical cardiovascular risk factors e.g. diabetes were not. Conclusion CAC assessment on routinely available non-gated CT can readily enhance peri- and postoperative cardiac risk stratification of LT recipients, as it identifies a selection of low-risk individuals for having peri- and post-operative cardiac events and all-cause mortality. Thereby, reducing test burden for the patients and saving healthcare expenses.

The role of coronary artery calcium score and european score2 in primary prevention of cardiovascular disease

Abstract Background There is limited evidence about the role of using coronary artery calcium (CAC) score in conjunction with the European SCORE2 (systematic coronary risk evaluation) for the identification of individuals at higher risk of cardiovascular disease. Purpose To evaluate the relationship between CAC score and the European SCORE2 for cardiovascular risk stratification in an asymptomatic population in primary prevention. Methods This is a preliminary analysis from a single-centre, prospective, randomised 1:1, controlled trial assessing the use of CAC score vs standard preventive strategies in asymptomatic individuals undergoing primary cardiovascular prevention. The 10-year risk of cardiovascular events was assessed with either SCORE2 (ages 45-69) or SCORE2-OP (ages 70-89). Subjects were classified as 1) low to moderate risk, 2) high risk, and 3) very high risk according to SCORE2/SCORE2-OP. CAC score was assessed by ECG gated low-dose computed tomography with no contrast administration. A CAC score of zero was considered to reclassify risk one level downward in high or very-high risk patients. CAC score ≥ 100 was used to reclassify risk one level upward in patients with low to moderate risk and a CAC score > 1000 to reclassify risk upward in subjects with high risk. Results A total of 1,252 participants: 627 in the CAC group and 625 in the control group, median age 55 yrs, 25th-75th 50-61 yrs, with availability of SCORE2 and SCORE2-OP were included in this analysis. SCORE2/SCORE 2-OP was similar in the CAC and control groups. Among the subjects in the CAC group, there was a highly significant difference in SCORE2/SCORE 2-OP between participants with CAC score = zero and those with CAC > zero (median 5.6, 25th-75th 3.6-8.55 vs 2.9, 1.8-4.6, p< 0.001) as well as between those with CAC ≥ 100 and those with CAC score < 100 [median 6.8, 25th-75th (10.3-4.5) vs 3.3 (2.0-5.3), p<0.001]. The prevalence of CAC score = zero was 75% in the low to moderate risk group, 47% in the high risk, and 20% in the very high risk, p<0.001. By contrast the prevalence of CAC score ≥ 100 was 7% in the low to moderate risk group, 24% in the high risk, and 45% in the very high risk, p< 0.001. The proportion of patients classified as low to moderate risk group was 72% with CAC score vs 59% without CAC score, high risk 23% with CAC score vs 35% without CAC score, very high risk 5% vs 6% respectively, p<0.001. Using CAC score risk reclassification occurred in 48% downward and 2% upward in originally classified high risk subjects, 7% upward in originally classified low to moderate risk, and 20% downward in originally classified very high risk subjects. Conclusions CAC score may lead to risk reclassification in a substantial proportion of individuals, in particular among those originally classified as high risk by the SCORE2/SCORE2-OP. The cardiovascular risk prediction accuracy of adding CAC score to SCORE2/SCORE2-OP awaits validation at long-term follow-up.

Automatic identification of coronary stent in coronary calcium scoring CT using deep learning

Coronary artery calcium (CAC) scoring CT is a useful tool for screening coronary artery disease and for cardiovascular risk stratification. However, its efficacy in patients with coronary stents, who had pre-existing coronary artery disease, remains uncertain. Historically, CAC CT scans of these patients have been manually excluded from the CAC scoring process, even though most of the CAC scoring process is now fully automated. Therefore, we hypothesized that automating the filtering of patients with coronary stents using artificial intelligence could streamline the entire CAC workflow, eliminating the need for manual intervention. Consequently, we aimed to develop and evaluate a deep learning-based coronary stent filtering algorithm (StentFilter) in CAC scoring CT scans using a multicenter CAC dataset. We developed StentFilter comprising two main processes: stent identification and false-positive reduction. Development utilized 108 non-enhanced echocardiography-gated CAC scans (including 74 with manually labeled stents), and for false positive reduction, 2063 CAC scans with significant coronary calcium (average Agatston score: 523.8) but no stents were utilized. StentFilter’s performance was evaluated on two independent internal test sets (Asan cohort- and 2; n = 355 and 396; one without coronary stents) and two external test sets from different institutions (n = 105 and 62), each with manually labeled stents. We calculated the per-patient sensitivity, specificity, and false-positive rate of StentFilter. StentFilter demonstrated a high overall per-patient sensitivity of 98.8% (511/517 cases with stents) and a false-positive rate of 0.022 (20/918). Notably, the false-positive ratio was significantly lower in the dataset containing stents (Asan cohort-1; 0.008 [3/355]) compared with the dataset without stents (Asan cohort-2; 0.043 [17/396], p = 0.008). All false-positive identifications were attributed to dense coronary calcifications, with no false positives identified in extracoronary locations. The automated StentFilter accurately distinguished coronary stents from pre-existing coronary calcifications. This approach holds potential as a filter within a fully automated CAC scoring workflow, streamlining the process efficiently.

Comparison between coronary calcium score and segment involvement score when used in clinical routine

Abstract Data from a nationwide registry on coronary computer tomography angiography. Background The Coronary Artery Calcium Score (CACS) and segment involvement score (SIS) are crucial factors in assessing the severity of coronary artery disease (CAD) and determining the likelihood of future cardiovascular events. However, there are few large multicenter studies comparing the predictive value of CACS and SIS when used in the clinical routine. Objectives To compare CACS and SIS ability to predict cardiovascular outcomes when used in the clinical routine. Methods This retrospective cohort study included patients who underwent Coronary Computed Tomography Angiography (CCTA) for CAD evaluation between 2006-2022 in 29 different centers in Sweden. CACS was calculated using the Agatston method, and SIS was determined based on the number of coronary segments exhibiting plaque. For SIS, patients were categorized into SIS=0, SIS=1, SIS=2, SIS=3, SIS³4, for CACS patients were categorized into CACS=0, CACS=1-9, CACS=10-99, CACS=100-299, CACS³300. Patients were followed regarding the primary outcome of death or myocardial infarction. Multivariable Cox proportional hazards models were used for adjusting for potential confounders. Results A total of 23034 patients underwent CCTA and were followed for up to ten years. Median age (IQR) was 58 (49–67) years, 50% were women, 9% had diabetes mellitus, 42% had hypertension, 25% had hyperlipidemia, 5% had previously known CAD. There were 955 patients (4.1%) with myocardial infarction (MI) and 409 patients (2.2%) died during follow up. There was an increase in cardiovascular events with increasing SIS. When comparing SIS and presence of obstructive stenosis, SIS was a better predictor of outcome (Figure 1). There was also an increase cardiovascular events with increasing CACS (Figure 2). When adjusted for age, sex, risk factors, previous cardiovascular disease and intervention, both SIS and CACS were independently associated with outcome. When SIS and CACS were compared in a receiver operating characteristic curve, CACS had significantly larger AUC than SIS, (0.70[0.67-0.73] vs 0.62[0.60-0.63]). Conclusion Our findings show that both CACS and SIS are important and independent predictors of cardiovascular outcomes thereby offering a valuable tool for personalized risk stratification and management in clinical settings. When used in the clinical routine, CACS appears to be more strongly associated with outcome than SIS.Figure1 Survival SISFigure 2 Survival CACS

AI-enabled cardiac chambers volumetry in coronary calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis

Abstract Introduction Coronary 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 for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score. Methods We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% women, 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 versus NT-proBNP, Agatston score, and 10 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP) for predicting incident HF over 15 years. Results Over 15 years of follow-up, 256 HF events accrued. The AUC for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free Net Reclassification Index (NRI) significantly improved on clinical risk factors (0.32), NT-proBNP (0.46), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001). Conclusion AI-CAC 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.

AI-based algorithm for assessing coronary artery calcium score on contrast-enhanced cardiac computed tomography scans

Abstract Introduction In cardiac computed tomography (CT) examinations, non-contrast scans are often performed in addition to contrast-enhanced scans to quantify the coronary artery calcification (CAC) score. These calcium score scans are associated with additional exposure to ionizing radiation. Purpose We sought to develop a fully automated artificial intelligence (AI)-based algorithm capable of determining CAC score based solely on contrast-enhanced CT scans, eliminating the need for additional non-contrast scans. Methods An automated CAC scoring algorithm on contrast-enhanced CT scans was developed and trained using a dataset of 297 cardiac CT studies. Coronary artery calcifications were manually segmented in contrast-enhanced scans using a threshold of 2 standard deviations above the mean attenuation value of the ascending aorta. On non-contrast scans, CAC was manually assessed using a standard threshold of ≥130 Hounsfield Units. A correction factor for calcium score results assessed on contrast-enhanced scans was determined using linear regression. The algorithm was tested on an independent set of 90 contrast-enhanced CT scans (four manufacturers, eight scanner models). We compared automated CAC scores to manual expert reader reference assessments on non-contrast CT scans. CAC scores were categorized into four risk categories following the Society of Cardiovascular Computed Tomography recommendations: 0, 1-100, 101-300, and >300 Agatston Units. Results In the assessment of 90 CT studies (mean age 61.0±11.4 years, 46.7% males), the AI-model detected CAC in 69 contrast-enhanced scans (76.7%), comparable to the human reader's detection rate of CAC in 71 non-contrast scans (78.9%) (p = 0.63). The CAC score was initially calculated using AI-based segmentation of coronary calcification on contrast-enhanced scans and then the result was multiplied by established linear correction factor of 1.97. There was an excellent correlation between AI-model and manual reference total CAC scores (Pearson’s r = 0.96, 95% CI 0.94–0.97, p < 0.001). The model correctly classified 77 patients (85.6%) into the same CAC risk category as the human reader (Figure 1). Among 19 patients (21.1%) with a CAC score of zero, only 1 patient (5.3%) was reclassified with a non-zero CAC score by the AI-model. Cohen’s kappa value for CAC score risk categorization was 0.80 (p < 0.001), indicating very good agreement (Figure 2). Bland–Altman analysis revealed minimal bias of -9.7 Agatston Unit with 95% limits of agreement ranging from -184.8 to 165.5 Agatston Unit. Conclusions CAC score can be accurately quantified on contrast-enhanced cardiac CT scans using an automated AI-based algorithm. This approach has the potential to eliminate the necessity for an additional non-contrast CT scan, thereby reducing the patient's exposure to ionizing radiation.

Coronary artery calcium scans powered by artificial intelligence (AI-CAC) predicts atrial fibrillation and stroke comparably to cardiac magnetic resonance imaging: MESA

Abstract Background AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as one year. In this study we evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF and stroke, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP. Methods We used 15-year outcomes data from 3552 asymptomatic individuals (52.2% women, age 61.7±10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. CMRI LA volume was previously measured by human experts. Data on BNP, CHARGE-AF risk score and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve (AUC). Results Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The AUC for AI-CAC versus CMRI for AF and stroke were not significantly different (0.802 vs. 0.798 and 0.762 vs. 0.751 respectively, p=0.60). AI-CAC significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF and stroke when added to CHARGE-AF risk score (0.28, 0.21), NT-proBNP (0.43, 0.37), and Agatston score (0.69, 0.41) respectively (p for all<0.0001). Conclusion AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted.

AI-enabled cardiac chambers volumetry in non-contrast coronary artery calcium CT scans vs. contrast-enhanced coronary CT angiography scans in the same patients

Abstract Introduction We have developed an AI-enabled automated volumetry of cardiac chambers (AutoChamber) that works both on non-contrast ECG-gated cardiac CT scans used for coronary artery calcium (CAC) score, and non-contrast non-gated lung cancer screening CT scans. We have recently reported the agreement between AutoChamber measurements vs cardiac MRI measurements. Here we report AutoChamber results on non-contrast CAC scans and contrast enhanced coronary CT angiography (CTA). Methods We have obtained CT images from a previously published study in which 131 cases underwent non-contrast CAC and contrast enhanced CCTA at research institute. The cases consisted of 52.4% women with ages 55.0 ± 10.1 years. The cardiac chambers volume in 131 cases of contrast enhanced CCTA were measured using automated chamber volumetry by Philips Brilliance Volume v 4.5; Philips Healthcare. Results The correlations between Philips’ automated chamber volumetry in contrast enhanced CCTA scans versus AutoChamber automated volumetry in non-contrast CAC scans for left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), and left ventricular wall (LVW) volume were 0.73, 0.65, 0.85, 0.93, 0.90, respectively (P for all <0.01). Conclusion AI-enabled AutoChamber volumetry based on non-contrast cardiac scans is feasible, and correlates reasonably well with contrast-enhanced cardiac CT scans. Further studies are warranted to reproduce these results and explore the add-on value of AutoChamber to CAC scans.

The predictive value of coronary artery calcium deposit for cardiovascular events in patients treated with immune checkpoint inhibitor therapy

Abstract Background Immune checkpoint inhibitors (ICIs) are now a standard of care for the treatment of wide variety of cancers, due to their dramatically improved patient's outcomes. Nevertheless, their use may lead to significant cardiovascular immune-related adverse events (CV irAEs). Objectives We aimed to assess whether pre-treatment coronary artery calcium (CAC) deposit may play as a risk factor for CV irAEs development in patients treated with ICIs therapy. Methods A retrospective single center cohort of patients treated with ICIs therapy, who performed pre-treatment chest computed tomography. Visual CAC assessment was performed, categorized to positive or negative calcium deposit in the coronary arteries. Patients with pre-existing ischemic heart disease were excluded. The primary endpoint was to evaluate the association between positive CAC deposit and the composite CV irAEs, including myocarditis, acute coronary syndrome, heart failure, and arrhythmias. The secondary endpoint was to evaluate its association to all-cause mortality. Results The cohort included 238 patients, with mean age 65±13 years and 53% male representative. The most prevalent type of cancer was lung (36%) and the prominent ICIs regimen was pembrolizumab (54%). High incidence of cardiac risk factors was observed, including hypertension (39%), hyperlipidemia (35%) and diabetes (23%). Overall, 145 (61%) patients were included in the positive CAC deposit group. No significant differences were observed between the positive and negative groups regarding cancer type, ICIs therapy and baseline cardiac risk factors or medical therapy. Over a 12-months follow-up period, positive CAC deposit was not associated with the development of CV irAEs (p=0.139), or increased all-cause mortality (p=0.884). Conclusion The presence of pre-treatment positive CAC deposit did not demonstrate a predictive role in assessing the risk of CV irAEs or all-cause mortality in patients treated with ICIs.

Lipoprotein(a) metabolomic and proteomic features and atherosclerotic cardiovascular disease in young, asymptomatic adults

Abstract Background Lipoprotein (a) [Lp(a)] is associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD) events. Although Lp(a) is a powerful genetic risk factor for ASCVD, the pathways that mediate this risk and the associated metabolomic and proteomic features that capture this risk are unknown. Methods In young, asymptomatic Coronary Artery Risk Development in Young Adults (CARDIA) participants, we defined the relationships between Lp(a) and metabolomic (n=563)/proteomic (n=184) features, derived quantitative Lp(a)-omic scores from these features, and related these quantitative scores to ASCVD phenotypes. Results CARDIA subjects had a mean age of 32 years and a median follow-up of 27 years. In the overall cohort (n=3920), Lp(a) levels at year 7 (Y7) were significantly associated with ASCVD endpoints, including hs-CRP, coronary artery calcification (CAC), death, and incident CHD (Figure 1A). In the subcohort (n=2290) that had measurements of Lp(a), proteomics, and metabolomics, Y7 Lp(a) levels were associated with distinct proteomic and metabolomic features indicative of immune responses, lipoprotein metabolism, atherogenesis, and arginine/steroid biosynthesis. Using machine learning approaches, Lp(a) metabolomic, proteomic, and transomic quantitative scores were derived. After adjustment, the Lp(a) transomic score was more strongly related to incident CAC, degree of CAC, CRP, and incident CHD than Lp(a) itself (Figure 1B). Conclusions To our knowledge, this is the first study to examine the relationship between Lp(a), metabolomic/proteomic features, and ASCVD phenotypes in young, asymptomatic adults. The multi-omics approaches employed here provide insight into the pathobiology of Lp(a)-driven ASCVD and enable more nuanced mechanistic risk assessment compared with Lp(a) alone.

Defining high risk in the cardiovascular-kidney-metabolic syndrome and associated cardiovascular disease outcomes

Abstract Background The American Heart Association (AHA) recently developed the cardiovascular-kidney-metabolic (CKM) syndrome construct with stages 0-4 to comprehensively characterize the spectrum of cardiovascular disease (CVD) risk [1]. CKM Stage 3 is defined by subclinical CVD or high (≥20%) estimated 10-year CVD risk according to the newly-derived PREVENT models [2]. However, within CKM Stage 3, the burden of subclinical atherosclerosis equivalent to a ≥20% estimated CVD risk, and long-term CVD outcomes associated with CKM Stage 3, are not known. Purpose To (1) characterize the distribution of subclinical atherosclerosis by coronary artery calcium (CAC) according to PREVENT estimated CVD risk categories, and (2) quantify long-term CVD risk associated with CKM Stage 3, in two international cohorts of adults without prior CVD. Methods In participants of the Multi-Ethnic Study of Atherosclerosis (MESA, age 45-84 years) and the Rotterdam Study (age ≥55 years), the burden of CAC was calculated across estimated 10-year CVD risk (low, 0 to <5%; borderline 5 to <7.5%; intermediate, 7.5 to <20%; high, ≥20%). Participants were categorized into CKM stages according to CVD risk factors (body mass index, waist circumference, glycemic status, blood pressure, lipids, kidney function), estimated 10-year CVD risk (by AHA PREVENT model), and presence of CAC. The association of CKM syndrome stage with fatal or nonfatal CVD outcomes was evaluated with covariate-adjusted proportional hazards regression. Results Among 6,123 MESA participants (52% female, mean age 61.4 years) and 1,691 Rotterdam Study participants (46% female, mean age 67.1 years), the distribution of CAC across absolute 10-year CVD risk categories is shown in Figure 1. In high (≥20%) CVD risk adults, CAC score ≥100 was present in 50% of MESA participants and 48% of Rotterdam Study participants. Most participants were classified as CKM Stage 3 (53% in MESA, 83% in Rotterdam). Over a median follow-up of 17.5 years in MESA, CVD events occurred in 5% of CKM Stage 0, 6% of Stage 1, 9% of Stage 2, and 26% of Stage 3 participants. Over a median follow-up of 10.2 years in the Rotterdam Study, CVD events occurred in 0% of CKM Stage 0 and 1, 8% of Stage 2, and 17% of Stage 3 participants (Figure 2). The risk of total CVD was significantly higher in CKM Stage 3 participants relative to CKM Stage 0-2 (MESA: adjusted HR 2.74, 95% CI 2.32, 3.23; Rotterdam: adjusted HR 2.18, 95% CI 1.34, 3.54). Conclusions In an international, multi-ethnic sample of middle-aged adults, adults with high (≥20%) absolute 10-year CVD risk by PREVENT had substantial burden of CAC. Adults in CKM Stage 3 had approximately 2.2- to 2.7-times higher risk for CVD, compared with adults in lower CKM stages. Accounting for estimated CVD risk and burden of cardiovascular, kidney, and metabolic risk factors informs prevention of subclinical and clinical CVD.

Validation of quantitative aortic valve calcification assessment by photon-counting computed tomography from contrast-enhanced data sets using virtual non-contrast reconstructions

Abstract Background The spectral information of photon-counting computed tomography (PCCT) permits the reconstruction of energy-selective images. Purpose We evaluated two methods for the reconstruction of virtual non-contrast-enhanced images from contrast-enhanced CT angiographic data sets regarding their ability to quantify aortic valve calcification. Results were validated by comparison to non-enhanced computed tomography. Methods A total of 100 subjects with severe aortic valve stenosis who underwent ECG-synchronized cardiac photon-counting CT for diagnostic workup prior to transcatheter aortic valve implantation (TAVI) were included in the current analysis. A standard true non-contrast acquisition was performed for aortic valve calcium quantification in each subject. In addition, two virtual non-enhanced data sets were reconstructed from contrast-enhanced CT angiographic data: a calcium-specific image ("pure calcium scan", PCS) and a virtual non-contrast (VNC) scan by subtraction of iodine. The PCS algorithm separates calcium and iodine in spectral PCCT images, creating a non-calcium mask for denoised spectral input and preserving calcium contrast in final material decomposition. Iodine extraction is achieved by measurement of the iodine content and its differentiation from native soft tissue attenuation. The Agatston Score was assessed in each of the three image data sets using semi-automated software. Results The mean age of the subjects was 78 ± 7 years, 66% were male. Mean EUROSCORE II was 4 ± 4%, mean STS Score for morbidity and mortality was 16 ± 11%. In the true non-contrast scan used for validation, the mean Agatston Score was 3028 ± 1820. The mean Agatston Score obtained from the PCS data set was 3004 ± 2042 and did not differ significantly from true non—contrast image sets (p=0.716). In the VNC data sets, the mean Agatston score was 1726 ± 1288, significantly lower than in the true non-contrast and the PCS data sets (both <0.001). Both, PCS and VNC data showed a significant correlation with true non-contrast scans (correlation coefficient PCS: 0.978, VNC scan: 0.975, both p<0.001). Conclusion In conclusion, the current results show the possibility to reliably quantify the Agatston Score of larger cardiac calcifications in virtual non-contrast data sets – however, there is substantial influence of the algorithm that is used so that validation of new techniques is of key importance.

AI-enabled automated cardiac chambers volumetry in non-contrast ECG-gated cardiac scans vs. non-contrast non-gated lung scans

Abstract Introduction We have developed an AI-enabled automated volume measurement of cardiac chambers (AutoChamber) that works on ECG-gated coronary artery calcium scans and correlates well with contrast enhanced coronary CT angiography scans. We have recently reported that increased left atrial volume measured by AutoChamber is a strong predictor of new onset atrial fibrillation (AF) in asymptomatic individuals of Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we compare the volumetry results in ECG- gated CAC scans 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 obtained from Harbor UCLA. Mean±SD for age was 62.1±10.0 years with 52% female participants. All cases were asymptomatic and were scanned for preventive health assessment. AutoChamber was run on all cases by an independent operator who was not involved in 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) was 63.8 ± 18.5 vs. 65.5 ± 19.4, left ventricle (LV) 102.8 ± 25.3 vs. 105.5 ± 25.2, right atrium (RA) 81.9 ± 20.5 vs. 85.7 ± 21.9 right ventricle (RV), 140.7 ± 34.2 vs. 134.2 ± 33.7, left ventricle wall (LVW) 113.0 ± 26.6 vs. 109.7 ± 27.0 respectively (P < 0.0001). Strong correlation was shown between cardiac and lung scans for each cardiac chamber: LA (R = 0.92), LV (R = 0.93), RA (R = 0.91), RV (R = 0.92), and left ventricular wall (LVW) R = 0.95. 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 AF and heart failure.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography

Abstract Background There are sex differences in the extent, severity, and outcomes of coronary artery disease. Purpose To assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. Methods In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. Results Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p=0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p=0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p=0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n=80) and 48.3% (n=73); p=0.42), or CMA values >1.56 (21.8% (n=33) and 21.8% (n=33); p=1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n=10) and 7.8% of men (n=66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p=0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p<0.001). Conclusions Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.Sex differences in CAD activityCase examples

A novel marker of cardiovascular risk stratification: the role of total cardiovascular calcium score using cardiac CT

Abstract Introduction It is widely accepted that coronary and valve calcification measured by cardiac CT, individually, are associated with cardiovascular events and mortality. However, the role of an encompassing marker of cardiovascular atherosclerosis could be more representative of the real cardiovascular risk. Objectives To determine the prognostic value of a combined coronary, valvular and aortic calcium score to predict long-term major adverse cardiac and cerebrovascular events (MACCE). Methods We conducted a single center study on 316 consecutive patients who underwent cardiac CT scan between January 2018 and December 2019. We excluded patients with poor imaging quality, constrictive pericarditis, prosthetic valves and/or devices. The calcium score of coronary arteries (CA), mitral valve (MV), aortic valve (AoV), ascending aorta (AAo) and aortic arch (AAc) were calculated from non-contrast ECG-gated CT using the Agatston method and were combined to derive a valvular (VA=MV+AoV), total cardiac (TC=CA+VA) and total cardiovascular (TCV=TC+AAo+AAc) calcium scores (Fig.1A). The primary endpoint was a composite of MACCE, defined as all-cause death, stroke, myocardial infarction and hospital admission for heart failure. Results A total of 275 CT scans were suitable for analysis. Mean age was 59.6±12.3 years, 48.4% were female. A total of 183 (66.7%) patients presented calcification in at least one location. Patients with calcification on any of the prespecified locations had higher prevalence of hypertension, dyslipidemia and type 2 diabetes mellitus (DM) than those without any calcium (p<0.05). After a median follow-up of 3.18 [IQR 2.84-3.69] years, 40 (14.5%) patients had met the primary endpoint. Regression analyses demonstrated that all CA, VA, TC and TCV scores were independent predictors of MACCE (p<0.05 for all). The best prediction models included calcium score (all combinations), age, sex, type 2 DM and smoking status (Fig.1B). The model with TCV score was the most powerful predictor of MACCE (χ2 47.8), followed by TC score (χ2 43.1). Of interest, the model with CA score had the poorest performance (χ2 35.4). Conclusion Coronary calcium score is a quick way to stratify risk in clinical practice, but its performance is too focused on coronary events. Total cardiovascular calcium score, however, is more encompassing, also quick to measure, and a more truthful depiction of the patient’s cardiac and cerebrovascular risk, potentially allowing a more tailored and timely approach to risk factors in clinical practice.