Agatston Coronary Artery Calcium Score Research Articles

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.

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.

Beyond the Agatston calcium score: role of calcium density and other calcified plaque markers for cardiovascular disease prediction.

To review the current evidence and highlight future strategies regarding consideration of coronary artery calcium (CAC) density in cardiovascular disease (CVD) risk stratification. Expressed as the product of plaque area and a peak calcium density weighting factor, the Agatston method is the gold-standard for measuring CAC on noncontrast cardiac computed tomography. Over the last decade, observational data have suggested that calcium density is inversely associated with CVD events and confers additional prognostic information independent of traditional risk factors and Agatston CAC scores. Specific density measures have been assessed including peak calcium density, mean CAC density, and CAC area-density discordance. Beyond calcium density, the number of affected arteries and regional distribution of CAC which may be correlated with CAC density have also improved the predictive utility of the Agatston score. Calcium density is inversely associated with CVD risk after considering plaque area and/or volume. Calcium markers including density, vessel involvement, and regional distribution confer additional prognostic information for the prediction of incident CVD among those with prevalent subclinical atherosclerosis. A future area of study includes calcium radiomics ('calcium-omics') and whether the artificial intelligence-derived automated measurement of calcium markers beyond the Agatston score may be of value in CVD risk stratification among individuals with early to advanced subclinical atherosclerosis.

Coronary artery calcification burden, atherogenic index of plasma, and risk of adverse cardiovascular events in the general population: evidence from a mediation analysis

BackgroundDyslipidemia and abnormal cholesterol metabolism are closely related to coronary artery calcification (CAC) and are also critical factors in cardiovascular disease death. In recent years, the atherogenic index of plasma (AIP) has been widely used to evaluate vascular sclerosis. This study aimed to investigate the potential association of AIP between CAC and major adverse cardiovascular events (MACEs).MethodsThis study included 1,121 participants whose CACs were measured by multislice spiral CT. Participants’ CAC Agatston score, CAC mass, CAC volume, and number of vessels with CACs were assessed. AIP is defined as the base 10 logarithm of the ratio of triglyceride (TG) concentration to high-density lipoprotein-cholesterol (HDL-C) concentration. We investigated the multivariate-adjusted associations between AIP, CAC, and MACEs. The mediating role of the AIP in CAC and MACEs was subsequently discussed.ResultsDuring a median follow-up of 31 months, 74 MACEs were identified. For each additional unit of log-converted CAC, the MACE risk increased by 48% (HR 1.48 [95% CI 1.32–1.65]). For each additional unit of the AIP (multiplied by 10), the MACEs risk increased by 19%. Causal mediation analysis revealed that the AIP played a partial mediating role between CAC (CAC Agatston score, CAC mass) and MACEs, and the mediating proportions were 8.16% and 16.5%, respectively. However, the mediating effect of CAC volume tended to be nonsignificant (P = 0.137).ConclusionsAn increased AIP can be a risk factor for CAC and MACEs. Biomarkers based on lipid ratios are a readily available and low-cost strategy for identifying MACEs and mediating the association between CAC and MACEs. These findings provide a new perspective on CAC treatment, early diagnosis, and prevention of MACEs.

Integrating cardiovascular risk assessment into mobile low-dose CT lung screenings in rural Appalachia: A comprehensive analysis of the relationship between lung cancer risk, coronary artery calcium burden, and cardiovascular risk reduction strategies

ObjectiveMobile low-dose computed tomography (LDCT) lung screenings are part of an outreach program in rural Appalachia to detect early lung cancer. Coronary artery calcium (CAC) scoring on LDCT can identify calcium deposits in coronary arteries and can prompt consideration of risk modification for prevention of cardiovascular disease (CVD) events. It is not known if Lung CT Screening Reporting & Data System (Lung-RADS) scoring correlates with CAC scores. There is no clear guidance for patients undergoing LDCT screenings to receive follow-up regarding CAC or prevention of associated CVD risk. MethodsThis was a retrospective review of mobile LDCT LCS in adults with no known history of CVD. CT images were obtained at 100 kVp with a slice thickness of 3 mm. Agatston CAC scoring was performed retroactively. Lung-RADS scores were categorized as: Negative (1), Benign (2), Probably Benign (3), and Suspicious (4). CAC scoring was grouped as 0, 1–100, 101–399, and ≥400. Descriptive statistics and chi-square analyses were utilized. ResultsA total of 526 LDCT screenings were included. Over 54 % of patients had coronary calcification on LDCT LCS. 161 patients (30.6 %) had a CAC score of ≥100 and 75 patients (14.3 %) had a CAC score ≥400. Of patients with a CAC score ≥100, 7.5 % received referrals for follow-up after the LDCT screen and 9.3 % had additional cardiac testing. Of those with a CAC score ≥100 not already on a statin (45.3 %) and not already on aspirin (63.3 %), few were started within 3 months of LDCT for prevention (8.2 % and 5.9 % respectively). Among patients with a Lung-RADS score of 4, 17 % had a CAC score >400, whereas only 12 % with a Lung-RADS score of 1 fell into the same CAC category. Higher Lung-RADS scores correlated with fewer patients with CAC of 0. A significant correlation was observed between higher Lung-RADS scores and elevated CAC scores (p = 0.02). ConclusionIn patients with no CVD history, coronary artery calcification was frequently identified on mobile LDCT lung screenings in rural communities. Patients with higher probabilities of malignant lung nodules may also be at increased risk for significant coronary artery disease. Calcium scoring from LDCT screenings allowed for simultaneous assessment of lung cancer and CVD risk. Unfortunately, few referrals or CVD prevention medications were initiated. Awareness of CAC score utility, follow-up for identified coronary calcifications, and consideration of primary prevention medications when indicated, would be beneficial in patients undergoing LDCT lung screenings, especially in rural areas with limited healthcare access.

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

CARDIORESPIRATORY FITNESS, HIGH BODY FAT PERCENTAGE AND THE RISK OF CORONARY CALCIFICATION IN MIDDLE–AGED MEN

Objective: Low levels of cardiorespiratory fitness (CRF) have been showed to increase the risk of cardiovascular disease(CVD). We tested the hypothesis that central obesity is associated with coronary artery calcification (CAC), but that fitness attenuates this association. Design and method: We analyzed men who participated in a health screening program. We measured CAC using multidetector computed tomography (MDCT) with a calculation of Agatston coronary artery calcium score. CAC score over 400 were defined as the prevalence of moderate to severe subclinical atherosclerosis. Central obesity was defined as body mass index (BMI) over 25kg/m2 and body fat percentage (BFP) over 30% in men. Fitness was directly measured by peak oxygen uptake (VO2peak) and divided into unfit (low tertile) and fit (middle and upper tertile) categories based on age-specific VO2peak percentiles. Results: Compared with normal weight men, central obese men was associated with a higher prevalence of CAC after adjusting for age, SBP, lipid profile, logCRP, s-glucose, heart rate, diabetes, and hypertension. The fit men was associated with lower odds ratios for having CAC. However, the fit with central obesity had odds ratios for CAC that were similar to those of the fit with normal weight. Conclusions: Our findings demonstrate that BMI and BFP are associated with increased risk of moderate to severe CAC score, but that high fitness appears to attenuate the risk in men.

Effects of overhydration, Kt/Vurea, β2-microglobulin on coronary artery calcification and mortality in haemodialysis patients.

We studied the effects of overhydration (OH), Kt/Vurea and β2-microglobulin (β2-MG) on coronary artery calcification and mortality in patients undergoing haemodialysis (HD). The Agatston coronary artery calcium score (CACS), postdialysis body composition using bioimpedance analysis, single-pool Kt/Vurea and predialysis β2-MG at baseline were assessed and followed up for 3 years in patients undergoing HD. We performed logistic regression analyses for a CACS ≥400 and Cox proportional hazard analyses for all-cause and cardiovascular mortality. The study involved 338 patients with a median age of 67 (56-74) years, dialysis duration of 70 (33-141) months and diabetes prevalence of 39.1% (132/338). Patients with a CACS ≥400 (n = 222) had significantly higher age, dialysis duration, male prevalence, diabetes prevalence, C-reactive protein, predialysis β2-MG, OH, extracellular water/total body water and overhydration/extracellular water (OH/ECW) but significantly lower Kt/Vurea than patients with a CACS <400 (n = 116) (p < .05). OH/ECW, Kt/Vurea and predialysis β2-MG were significant predictors of a CACS ≥400 (p < .05) after adjusting for age, dialysis duration, serum phosphate and magnesium. In all patients, cut-off values of OH/ECW, Kt/Vurea and predialysis β2-MG for a CACS ≥400 were 16%, 1.74 and 28 mg/L, respectively. After adjusting for dialysis duration, OH/ECW ≥16%, Kt/Vurea ≥1.74 and β2-MG ≥28 mg/L were significant predictors of 3-year all-cause mortality but not 3-year cardiovascular mortality. Higher OH/ECW, higher predialysis β2-MG and lower Kt/Vurea values are significant risk factors for a CACS ≥400 and 3-year all-cause mortality in patients undergoing maintenance HD.

Absolute iron deficiency, coronary artery calcification and cardiovascular mortality in maintenance haemodialysis patients.

The effects of iron on vascular calcification in rats and vascular smooth muscle cells were recently reported, but clinical studies on iron and vascular calcification are scant. We studied the associations of absolute iron deficiency, coronary artery calcification and mortality in patients with maintenance haemodialysis (MHD). Transferrin saturation (TSAT), ferritin, mean corpuscular haemoglobin (MCH) and Agatston coronary artery calcium score (CACS) were studied at baseline in MHD patients and followed up for 3 years. Cox proportional hazard analyses for mortality and linear regression analyses for CACS were performed. In 306 patients, the median age was 67 (56-81) years, dialysis duration was 76 (38-142) months, and diabetes prevalence was 42.5%. Fifty-two patients had died by 3 years. Patients with absolute iron deficiency (TSAT <20% and ferritin <100 ng/mL) (n = 102) showed significantly higher CACS (p = .0266) and C-reactive protein (p = .0011), but a lower frequency of iron formulation administration compared with patients without absolute iron deficiency at baseline (n = 204). Absolute iron deficiency was a significant predictor for 3-year cardiovascular (CV) mortality (hazard ratio: 2.08; p = .0466), but not for 3-year all-cause mortality. CACS was significant predictor for both 3-year CV and all-cause mortality (p <.05). Absolute iron deficiency and MCH were significant determinants of CACS (p < .05). MHD patients with absolute iron deficiency showed significantly higher CACS than others, and absolute iron deficiency was a significant risk factor for coronary artery calcification and 3-year CV mortality in MHD patients, but was not a significant predictor for 3-year all-cause mortality.

Comparison of Coronary Artery Calcium and Quantitative Coronary Plaque in Predicting Obstructive Coronary Artery Disease: Subgroup Analysis of the CLARIFY Study.

Background: Agatston coronary artery calcium (CAC) score is a strong predictor of mortality. However, the relationship between CAC and quantitative calcified plaque volume (CPV), which is measured on coronary computed tomography angiography (CCTA), is not well understood. Furthermore, there is limited evidence evaluating the difference between CAC versus CPV and CAC versus total plaque volume (TPV) in predicting obstructive coronary artery disease (CAD). Methods: This study included 147 subjects from the CLARIFY registry, a multicentered study of patients undergoing assessment using CCTA and CAC score as part of acute and stable chest pain evaluation. Automated software service (Cleerly.Inc, Denver, CO, USA) was used to evaluate the degree of vessel stenosis and plaque quantification on CCTA. CAC was measured using the standard Agatston method. Spearman correlation and receiver operating characteristic curve analysis was performed to evaluate the diagnostic ability of CAC, CPV and TPV in detecting obstructive CAD. Results: Results demonstrated a very strong positive correlation between CAC and CPV (r=0.76, p=0.0001) and strong correlation between CAC and TPV (r=0.72, p<0.001) at per-patient level analysis. At per-patient level analysis, the sensitivity of CAC (68%) is lower than CPV (77%) in predicting >50% stenosis, but negative predictive value is comparable. However, the sensitivity of TPV is higher compared with CAC in predicting >50% stenosis, and the negative predictive value of TPV is also higher. Conclusion: CPV and TPV are more sensitive in predicting the severity of obstructive CAD compared with the CAC score. However, the negative predictive value of CAC is comparable to CPV, but is lower than TPV. This study elucidates the relationship between CAC and quantitative plaque types, and especially emphasizes the differences between CAC and CPV which are two distinct plaque measurement techniques that are utilized in predicting obstructive CAD.

Visual Ordinal Coronary Artery Calcium Score from Non-Gated Chest CT Predicts Mortality After Severe Chronic Obstructive Pulmonary Disease Exacerbation.

Chronic obstructive pulmonary disease (COPD) patients often undergo chest CT for various indications. Coronary artery calcium (CAC) can be quantified visually on ungated chest CT using an ordinal score that has been shown to correlate well with traditional Agatston CAC scoring. The prognostic role of CAC was studied mainly in stable COPD patients. We aim to study the association between ordinal CAC and mortality amongst patients admitted for acute exacerbation of COPD (AECOPD). Retrospective study of AECOPD cases with no previous coronary revascularization admitted between 1st January 2016 to 30th June 2017 with a chest CT performed during admission or within 365 days prior. Ordinal CAC scoring (scale of 0-12) was performed by an experienced CT cardiologist blinded to patient data and outcomes. Patient demographics and future clinical events were retrieved from electronic medical records. There were 93 patients included (87.1% male, mean age 75 years) with the majority (59.1%) in GOLD Stage III. There were 21 (22.6%) patients with no CAC as well as 39 (41.9%) and 33 (35.5%) with ordinal CAC of 1-3 and 4-12, respectively. There were no significant differences in Charlson Comorbidity Index (CCI) and the proportion of patients with traditional cardiovascular risk factors (namely hypertension, dyslipidaemia, diabetes and smoking status) between the ordinal CAC score groups. Over a median follow-up period of 2.9 (1.1-3.9) years, there were 51 (54.8%) deaths. An ordinal CAC score of 4-12 was the only significant predictor of mortality after multivariate Cox-regression analysis adjustment for age, gender, body mass index, prior exacerbations, FEV1, cardiovascular risk factors and CCI [HR 3.944, (95% confidence interval 1.647-9.433, p = 0.002)]. Ordinal CAC measured from a current or recent ungated chest CT is an independent predictor of all-cause mortality in admitted AECOPD patients with no previous coronary revascularization.

Abstract 14960: Coronary Artery Calcium Distribution Patterns Are Independently Predictive of Adverse Cardiovascular Events: Observations From the Dallas Heart Study

Introduction: The coronary artery calcium (CAC) diffusivity index (DI) may improve risk stratification for those with Agatston CAC &gt; 0 in older individuals. We aimed to assess whether the distribution of CAC would improve risk prediction of cardiovascular (CV) events beyond the traditional Agatston CAC score in a younger population. Methods: Among Dallas Heart Study 2 (DHS2) participants with Agatston CAC &gt; 0 and no history of CVD (35% of entire cohort), we studied 1013 participants with CAC DI at DHS2. The raw CAC DI was calculated as 1 - (CAC in most affected vessel/total CAC) and categorized into concentrated (&lt;25th%), standard (25-75th%), and diffuse (&gt;75th%) patterns. Multivariable Cox proportional hazards regression and C statistics were calculated for coronary events (CHD) and all CVD, and all-cause mortality. Models were adjusted for total Agatston score, race, gender, BSA, smoking history, diabetes, and statin use. Results: Mean age of the DHS2 study cohort was 55 ± 9.4 years (48% women, 45% Black). The median follow up was 9 ± 2.5 years (55 CHD, 97 CVD, and 120 all-cause mortality events) (Figure, A) . CAC DI was modestly correlated with Agatston CAC (Spearman rho = 0.60, p&lt;0.01). In models adjusted for total Agatston score, risk factors, and demographics, compared with a concentrated pattern, a diffuse CAC pattern was associated with incident CHD (HR [95%CI]: 5.32 [1.68-16.86]); CVD (HR [95%CI]: 3.21 [1.66-6.21]), and all-cause mortality (HR [95%CI]: 2.02 [1.18-3.47] (Figure, B) . Findings were similar for continuous CAC DI. Addition of continuous CAC DI to adjusted models including Agatston CAC improved the C-statistic for CHD (0.79 to 0.81, p&lt;0.01) and CVD (0.71 to 0.73, p&lt;0.01). Conclusions: Beyond the validated Agatston score, higher CAC DI at baseline improves risk prediction of incident CHD and CVD events in a younger population-based cohort. Further studies are warranted to evaluate the clinical utility of assessing distribution patterns of CAC.

The Role of Coronary Artery Calcium Score to Assess Risk of Cardiovascular Disease in Irradiated Esophageal Cancer Patients

Coronary artery calcium (CAC) score is an important predictive imaging marker of cardiovascular disease (CVD). While studies have found positive association between CAC score and cardiac toxicity in irradiated lung and breast cancer patients, there are no studies assessing CAC scores in esophageal cancer (EC). While a cardiac-gated CT is required for standard Agatston CAC score, visual assessment of CAC via ordinal scoring on non-gated CT has shown good concordance with Agatston score. In this study, we sought to examine whether visual assessment of CAC, measured on standard of care, non-contrast chest CT, predicts the development of adverse cardiovascular events (ACVE) in irradiated EC patients. This is a single institution retrospective study of EC patients treated with RT from 2010-2021. We included patients with available PET/CT at diagnosis or chest CT simulation scan without contrast, and excluded those with history of percutaneous coronary intervention, coronary bypass surgery, or prior thoracic RT. Pre-treatment characteristics, clinical factors, and grade ≥ 3 (G3+) adverse cardiovascular events (ACVE) (CTCAEv5.0) were evaluated. Visual assessment of CAC was performed using ordinal method (CAC scored from 0 to 12), by a thoracic radiologist. Fine and Gray regression was used to compute hazard ratios for time to first ACVE. Univariate analyses using Cox proportional hazards were used for overall survival (OS). ACVEs were recorded from start of oncologic treatment and OS calculated after completion of RT. A total of 118 patients were analyzed with a median follow-up of 16 months. Median age was 67 years, 65% male, 43% white, 59% with EC of distal esophagus, and 59% had squamous cell carcinoma. Median mean heart dose was 21.93 Gy (range 0.15-36.94). 24% developed G3+ ACVEs: atrial fibrillation 9%, stroke 6%, heart failure 4%, pulmonary embolism 4%, pericardial effusion 3%, myocardial infarction 2%, heart block 2%, and cardiac death 1%. On univariate analyses, CAC >1 vs. CAC ≤ 1 trended towards increased risk of ACVE (HR = 1.95, 95% CI = 0.89-4.26; p = 0.094), however it is not predictive of OS (HR = 1.31, 95% CI = 0.75-2.30; p = 0.343). Proportion of patients with ACVEs was greater in CAC>1 group (Table). When compared to patients with CAC ≤ 1, those with CAC >1 were older (median age 62 vs 72 years, p = 0.0015), less likely to be never smokers (38% vs 30%, p = 0.0437), and more likely to have hypertension (43% vs 64%, p = 0.0197), and hyperlipidemia (30% vs 47%, p = 0.0557). This is the first study to investigate the relationship between CAC score and ACVEs in EC. While the study was underpowered (likely due to low rates of recorded ACVEs), to detect a significant association between CAC score and ACVEs, there was a trend towards increased risk of ACVEs in patients with a CAC score >1 by visual ordinal scoring. Further prospective evaluation with a larger cohort is warranted.

#5365 T2DM MODIFIES THE RELATIONSHIP BETWEEN CAC AND ADVERSE KIDNEY OUTCOME IN PATIENTS WITH CKD

Abstract Background and Aims Kidney function decline faster in patients with type 2 diabetes mellitus (T2DM) than in those without. We previously showed that coronary artery calcification (CAC) is a strong risk of adverse kidney outcome. Here, we examined whether T2DM could modify the relationship between CAC and the progression of chronic kidney disease (CKD). Method 2067 participants from the KoreaN Cohort Study for Outcome in Patients With CKD were enrolled. The main exposures were T2DM and CAC. The primary outcome was CKD progression defined as a composite of a 50% decline in estimated glomerular filtration rate or kidney failure with replacement therapy. Multivariable cause-specific hazard function was employed to determine the association between the main exposures and the primary outcome. Results During 8633 person-years of follow-up, the primary outcome occurred in 565 (27.3%) participants. In the multivariable cause-specific hazard model, both T2DM and CAC Agatston score (CACS) &amp;gt;0 were associated with a 1.80- and a 1.41-fold increased risk of CKD progression compared with counterparts. There was a significant interaction between T2DM and CAC for the primary outcome. In patients without T2DM, CACS &amp;gt;0 was not associated with the primary outcome compared with CACS of 0. However, in patients with T2DM, CACS &amp;gt;0 was associated with a significantly higher risk of CKD progression (adjusted hazard ratio, 2.12; 95% confidence interval, 1.64-2.75). In addition, a 1-SD increase in CACS was associated with 1.11-fold higher risk of CKD progression. Notably, this association was also significant in patients with T2DM who had CACS of 0. The slope of eGFR decline was steeper in patients with T2DM and the magnitude of decline was greater when CACS &amp;gt;0. Conclusion CAC were more strongly associated with risk of CKD progression in patients with T2DM than in those without. Thus, clinical implication of CAC may differ depending on the presence of T2DM.

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. Coronary artery calcium (CAC) score testing is a standard stratification tool that is rapid and highly reproducible. We analyzed CAC results of 100 studies in order to determine the accuracy and correlation between the AI software (Coreline AVIEW, Seoul, South Korea) and expert level-3 computed tomography (CT) human CAC interpretation and its performance when coronary artery disease data and reporting system (coronary artery calcium data and reporting system) classification is applied. A total of 100 non-contrast calcium score images were selected by blinded randomization and processed with the AI software versus human level-3 CT reading. The results were compared and the Pearson correlation index was calculated. The CAC-DRS classification system was applied, and the cause of category reclassification was determined using an anatomical qualitative description by the readers. The mean age was age 64.5 years, with 48% female. The absolute CAC scores between AI versus human reading demonstrated a highly significant correlation (Pearson coefficient R = 0.996); however, despite these minimal CAC score differences, 14% of the patients had their CAC-DRS category reclassified. The main source of reclassification was observed in CAC-DRS 0-1, where 13 were recategorized, particularly between studies having a CAC Agatston score of 0 versus 1. Qualitative description of the errors showed that the main cause of misclassification was AI underestimation of right coronary calcium, AI overestimation of right ventricle densities and human underestimation of right coronary artery calcium. Correlation between AI and human values is excellent with absolute numbers. When the CAC-DRS classification system was adopted, there was a strong correlation in the respective categories. Misclassified were predominantly in the category of CAC = 0, most often with minimal values of calcium volume. Additional algorithm optimization with enhanced sensitivity and specificity for low values of calcium volume will be required to enhance AI CAC score utilization for minimal disease. Over a broad range of calcium scores, AI software for calcium scoring had an excellent correlation compared to human expert reading and in rare cases determined calcium missed by human interpretation.

Coronary Artery Calcium Density and Cardiovascular Events by Volume Level: The MESA.

The Agatston coronary artery calcium (CAC) score provides robust cardiovascular disease risk prediction but upweights plaque area by a density factor. Density, however, has been shown to be inversely associated with events. Using CAC volume and density separately improves risk prediction, but it is unclear how to apply this method clinically. We aimed to evaluate the association between CAC density and cardiovascular disease across the spectrum of CAC volume to better understand how to incorporate these metrics into a single score. We performed an analysis of MESA (Multi-Ethnic Study of Atherosclerosis) participants with detectable CAC to evaluate the association between CAC density and events by level of CAC volume using multivariable Cox regression models. In a cohort of 3316 participants, there was a significant interaction (P<0.001) between CAC volume and density for coronary heart disease (CHD) risk (myocardial infarction, CHD death, resuscitated cardiac arrest). Models using CAC volume and density resulted in improvement in the C-index (0.703, SE 0.012 versus 0.687, SE 0.013) and a significant net reclassification improvement (0.208 [95% CI, 0.102-0.306]) compared with the Agatston score for CHD risk prediction. Density was significantly associated with lower CHD risk at volumes ≤130 mm3 (hazard ratio, 0.57 per unit of density [95% CI, 0.43-0.75]), but the inverse association at volumes >130 mm3 was not significant (hazard ratio, 0.82 per unit of density [95% CI, 0.55-1.22]). The lower risk for CHD associated with higher CAC density varied by level of volume, and volume ≤130 mm3 is a potentially clinically useful cut point. Further study is needed to integrate these findings into a unified CAC scoring method.

The Predictive Value of Serum Aldosterone Level for Coronary Artery Calcium Score in Patients with Chronic Kidney Disease: A Single-center Study.

Patients with chronic kidney disease (CKD) have high cardiovascular risk (CVR), which is often underestimated by conventional tools. The coronary artery calcium score (CACS) significantly improves CVR stratification by conventional tools, but it is often not available in low-resources settings. Aldosterone may be a cheaper alternative to CACS for CVR assessment in CKD patients. The aim was to assess the ability of serum aldosterone level to predict CACS in patients with CKD in comparison to standard predictors. This single-center study included 57 patients aged 40 to 67 years with CKD (estimated glomerular filtration rate [eGFR] ≥45 ml/min) and arterial hypertension. Serum aldosterone, sex, age, body mass index, blood pressure, total cholesterol, eGFR, and proteinuria were used for prediction of CACS&gt;0 Agatston units (AU) and CACS&gt;100 AU. The area under the curve (AUC) with 95% confidence intervals (CI) and the mean Brier scores were examined for predictors of CACS. Aldosterone predicted a CACS&gt;100 AU (AUC = 0.72, 95% CI: 0.56-0.88), but not a CACS&gt;0 AU. Age predicted a CACS&gt;100 AU (AUC = 0.80, 95% CI: 0.67-0.93) and a CACS&gt;0 AU (AUC = 0.75, 95% CI: 0.62-0.89). The addition of aldosterone to age for prediction of a CACS&gt;100 AU improved the mean Brier score, compared to the model with age alone, from 0.16 to 0.14, but not the AUC (0.83, 95% CI: 0.70-0.95). Aldosterone was a significant predictor of a CACS&gt;100 AU in patients with CKD, but aldosterone was not a better predictor than age alone.

Transient left bundle branch block associated with very high coronary artery calcium: a case report.

Coronary artery calcium (CAC) is the measure of subclinical coronary artery atherosclerosis most strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk. However, CAC is rarely reported in the inpatient setting to guide chest pain management. We present a case of very high CAC in a 64-year-old woman with hypertension, type 2 diabetes, and hyperlipidemia presenting with dyspnea. Initial electrocardiogram (ECG) demonstrated normal conduction with a heart rate of 76 beats/min, but new T-wave inversions in V1-V4 and a high-sensitivity troponin-I (hsTnI) value of 6 ng/L (normal < 6 ng/L). Repeat ECG in the emergency department showed normal sinus rhythm (heart rate of 80 beats/min); however, it subsequently demonstrated a left bundle branch block (LBBB) with a repeat hsTnI of 7 ng/L. Stress testing with pharmacologic single-photon emission computerized tomography did not show scintigraphic evidence of ischemia but noted extensive CAC and a concern for balanced ischemia. Subsequent coronary computed tomography angiography (CCTA) showed nonobstructive disease and a total Agatston CAC score of 1262. Invasive evaluation with left heart catheterization was deferred given the patient's unchanged symptoms and CCTA findings. Statin therapy was intensified and aspirin, metoprolol succinate, and antihypertension therapies were continued. Initiation of glucose-lowering therapy and lipoprotein(a) testing was strongly recommended on follow-up. Our case suggests that CAC ⩾ 1000 may be incidentally associated with transient LBBB during the workup of coronary artery disease. Here, we specifically show that functional testing that incorporates measurement of CAC burden can help to improve ASCVD-preventive pharmacotherapy initiation and intensification beyond the identification of obstructive disease alone.

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification

BackgroundDeep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP.MethodsOne hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk).ResultsIn comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%.ConclusionDLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment.Key Points• In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise.• Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score.• Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.

Abstract 12310: Comparison of Coronary Artery Calcium Quantification Methods in Statin Users and Non-Users

Background: Coronary artery calcium (CAC) can be quantified using various methods. The Agatston score (AS) computes the weighted sum of lesions with the maximum plaque attenuation factor. Calcium Volume measures the total volume of CAC, whereas Calcium Density has been shown to provide superior prognostication compared to the AS. Since statin use is associated with change in coronary plaque composition and thus CAC, we evaluated the difference CAC quantification methods among patients with suspected CAD receiving and not receiving statin therapy. Methods: Patients with suspected CAD and underwent cardiac computed tomography were recruited from a tertiary hospital in Singapore. Patient demographics and risk factors were obtained from electronic medical records. AS, Calcium Volume, Number of Lesions and Calcium Density were individually analysed with patient demographics and risk factors using multivariate models. Results were compared between patients on statins and not on statins to assess differences in CAC quantification methods. Results: Of the 528 patients, 43.0% were female, 40.9% had hypertension, 14.8% had diabetes, 44.3% had family history of CAD and 23.5% were smokers. While 58.5% of patients had hyperlipidemia, only 48.9% were on statin therapy. The mean age was 53.9±10.9 years, and the mean BMI was 26.1±5.69. Diabetes, hypertension, family history of CAD, and older age was more likely present in statin users (P &lt; 0.05 for all). In multivariate analysis, these variables and higher BMI were significantly associated with statin use in all four models (Table 1). For CAC quantification, Calcium Volume, AS, and Number of Lesions did not differ significantly between statin users and non-users, whereas Calcium Density was higher in statin users (OR = 1.19, CI = (1.02, 1.39), P = 0.025). Conclusions: Statin use is associated with higher Calcium Density but is not associated with differences in other CAC quantification methods in patients with suspected CAD.