Total Agatston Score Research Articles

Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis.

This study aimed to test whether the coronary artery calcium (CAC) burden on attenuation correction computed tomography (CTac), measured using artificial intelligence (AI-CACac), correlates with coronary flow capacity (CFC) and prognosis. We retrospectively enrolled patients who underwent [13N]ammonia positron emission tomography (PET) between September 2021 and May 2023. CTac data were obtained from all the patients. Patients with (history of) acute coronary syndrome, previous coronary stent insertion or bypass surgery, or left ventricular ejection fraction < 40% were excluded. The total Agatston score measured using a dedicated AI-CAC quantification software on CTac was defined as AI-CACac. The correlations between AI-CACac and PET-measured myocardial blood flow (MBF) and CFC and significant ischaemia (summed difference score ≥ 7) were analysed. Their prognostic values for major cardiovascular events (MACE), including death, nonfatal myocardial infarction, hospitalisation due to angina pectoris or heart failure, and late (> 90 days) revascularisation, were also evaluated. In total, 289 patients were included in this study. Significant negative correlations were found between AI-CACac and stress MBF (ρ = -0.363, p < 0.001) and MFR (ρ = -0.305, p < 0.001). AI-CACac > 10 was associated with a significantly higher prevalence of impaired CFC (31% vs. 7%, p < 0.001) and significant ischaemia (20% vs. 7%), which remained significant after adjusting for other risk factors. MACE occurred in 49 (17%) patients (median follow-up, 284 days), and those who experienced MACE had significantly higher AI-CACac (median, 166 vs. 56; p = 0.039). However, multivariable analysis revealed an independent prognostic association among impaired CFC, diabetes, smoking, but not for AI-CACac. AI-measured CACac correlates well with PET-measured MBF and CFC, but its prognostic significance requires further validation.

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.

Automated total and vessel-specific coronary artery calcium (CAC) quantification on chest CT: direct comparison with CAC scoring on non-contrast cardiac CT

BackgroundThis study aimed to evaluate the artificial intelligence (AI)-based coronary artery calcium (CAC) quantification and regional distribution of CAC on non-gated chest CT, using standard electrocardiograph (ECG)-gated CAC scoring as the reference.MethodsIn this retrospective study, a total of 405 patients underwent non-gated chest CT and standard ECG-gated cardiac CT. An AI-based algorithm was used for automated CAC scoring on chest CT, and Agatston score on cardiac CT was manually quantified. Bland-Altman plots were used to evaluate the agreement of absolute Agatston score between the two scans at the patient and vessel levels. Linearly weighted kappa (κ) was calculated to assess the reliability of AI-based CAC risk categorization and the number of involved vessels on chest CT.ResultsThe AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (κ = 0.75, 95% CI 0.70–0.79, P < 0.001) and an assignment agreement of 76%. Considerable coronary arteries with CAC were not identified with a per-vessel false-negative rate of 59.3%, 17.8%, 34.9%, and 34.7% for LM, LAD, CX, and RCA on chest CT. The leading causes for false negatives of LM were motion artifact (56.3%, 18/32) and segmentation error (43.8%, 14/32). The motion artifact was almost the only cause for false negatives of LAD (96.6%, 28/29), CX (96.7%, 29/30), and RCA (100%, 34/34). Absolute Agatston scores on chest CT were underestimated either for the patient and individual vessels except for LAD (median difference: − 12.5, − 11.3, − 5.6, − 18.6 for total, LM, CX, and RCA, all P < 0.01; − 2.5 for LAD, P = 0.18). AI-based total Agatston score yielded good reliability for risk categorization (weighted κ 0.86, P < 0.001) and an assignment agreement of 86.7% on chest CT, with a per-patient false-negative rate of 15.2% (28/184) and false-positive rate of 0.5% (1/221) respectively.ConclusionsAI-based per-patient CAC quantification on non-gated chest CT achieved a good agreement with dedicated ECG-gated CAC scoring overall and highly reliable CVD risk categorization, despite a slight but significant underestimation. However, it is challenging to evaluate the regional distribution of CAC without ECG-synchronization.

Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness.

To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.

Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer.

This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD.

The ratio of the max-to-mean coronary artery calcium score in the most calcified vessel is associated with the presence of coronary artery disease

PurposeThis study aimed to clarify the relationship between the severity of the calcium burden in the most calcified coronary vessel and coronary artery disease (CAD). MethodOf 2150 patients, 376 examined by both coronary computed tomographic angiography and invasive coronary angiography (ICA) within 30 days at Sun Yat-sen Memorial Hospital between March 2011 and July 2020 were included. Three coronary artery calcium scores (CACSs), including the Agatston score, volume score, and mass score, and other clinical variables were recorded. The ratio of max-to-mean CACS in the most calcified vessel (CACSmax:mean) was defined as the CACS in the most calcified vessel/average CACS of the four major epicardial coronary arteries. Logistic regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to assess the relationship between CACSmax:mean and CAD. ResultsCACSmax:mean was higher in 81.1% of subjects diagnosed with CAD than in subjects without CAD. In multivariate logistic regression analysis, CACSmax:mean determined by the Agatston score, volumetric score, and mass score was associated with CAD. In LASSO analysis, Agatston scoremax:mean (not the total Agatston score or other CACSmax:mean) had the strongest correlation with CAD (β = 0.125). AUCs in the training set and the validation set were 0.811 and 0.789, respectively. Increased age, diabetes and hypertension correlated with higher Agatston scoremax:mean. ConclusionsIn addition to total CACS, CACSmax:mean may be a novel diagnostic parameter for CAD, showing the calcium burden severity.

Deep learning for vessel-specific coronary artery calcium scoring: validation on a multi-centre dataset.

To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen's Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.

Assessment of visual, manual, and automatic coronary calcium scoring methods - analysis of dedicated CT scans and low dose CT scans acquired during cardiac 13N-ammonia-PET/CT

Abstract Funding Acknowledgements Type of funding sources: None. Background Coronary artery calcium (CAC) is a well-known predictor of major adverse cardiac events and is scored manually from dedicated, ECG-triggered CT scans. In the present study, we investigated the accuracy of risk categorisation based on visual and automatic AI calcium scoring from low dose CT (LDCT) scans and dedicated Calcium Score CT (CSCT) scans. Purpose To assess the agreement of risk prediction based on visual and automatic AI CAC scoring from CSCT scans and LDCT scans as compared to a gold standard, manual CSCT scoring. Methods We retrospectively enrolled 222 patients. Each patient received a 13N-ammonia PET with LDCT and CSCT scan. The time interval between LDCT and CSCT was less than 6 months. Each LDCT and CSCT scan was scored visually, manually, and automatically with AI. For visual scoring we used a previously described 6–points scale (0; 1-10; 11-100; 101-400; 401-100; &amp;gt;1000 Agatston score). For manual scoring we used a generally available software package (Syngo.via,Siemens). The automatic AI scoring was performed with commercially available software based on a deep learning algorithm (included in Syngo.via,Siemens). Each manually and automatically measured Agatston score was converted into the 6-points scale. We performed a per patient analysis; the risk group categorization was based on the total Agatston score. Spearman correlation coefficient was used to analyse the association between manual and automatic AI scoring methods. Agreement between visual, manual, and automatic AI scoring methods was determined using weighted kappa test with 95% confidence intervals (95%CI). Results The correlation between manual scoring from LDCT and CSCT scans was 0.96 (p &amp;lt; 0.001).The agreement between manual scoring from two scans, however, was low with weighted kappa equal 0.57 (95% CI 0.51 – 0.63). 91,9% of calcium scores measured by AI software on CSCT were in the same risk group as manual CSCT scores.The agreement between AI scoring and manual scoring using CSCT was excellent, the weighted kappa was equal 0.95 (95% CI 0.92 - 0.97).Based on visual scoring on LDCT scans, 74,3% of the scores were in the same category as manual scoring on CSCT scans. The agreement between the visual scoring on LDCT scans and a gold standard was strong, weighted kappa equal was 0.82 (95% CI 0.77 – 0.86). The agreement between manual and automatic scoring on LDCT using manual CSCT as the gold standard was low (0.57, 95 % CI 0.51 – 0.63; 0.49, 95 % CI 0.43 – 0.56, respectively). Based on visual LDCT scoring, 7 patients were incorrectly classified as calcium score 0, which underestimated the overall patients’ risk.The AI method scoring CSCT scans, classified 2 patients incorrectly as non-calcium risk group. Conclusions CAC can be automatically assessed from CSCT scans with commercially available AI software.Of manual, automatic, and visual CAC scoring on LDCT scans the visual scoring showed the highest agreement with the gold standard manual CSCT CAC scoring.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans

Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical. To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT. The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations. On CTC scans, automated Agatston scoring correlated highly with manual assessment (R2 = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R2 = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments. Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.

Subtracted Computed Tomography Angiography in the Evaluation of Coronary Arteries With Severe Calcification or Stents Using a 320-Row Computed Tomography Scanner.

Coronary computed tomography angiography (CCTA) has its limitations in evaluating arteries with stents or heavy calcification. This study compares the diagnostic performance of subtracted coronary computed tomography angiography (SCCTA) and nonsubtracted coronary computed tomography angiography (NSCCTA) in evaluating coronary artery disease (CAD) and in-stent restenosis (ISR). Twelve patients with stents and 20 patients with heavy coronary calcifications (total Agatston's score >400) underwent both SCCTA and invasive coronary angiography (ICA) with an interval of <3 months. Four subjects in the stented group also had heavy calcifications. Overall, 30 stented segments and 202 calcified segments were assessed to compare the diagnostic performance of SCCTA and NSCCTA in detecting ISR and CAD. For the 30 stented segments, SCCTA/NSCCTA had a sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) (shown in %) of 66.7/100, 100/55.6, 96.7/60, 100/20, and 96.4/100 in diagnosing ISR, respectively. For the 202 calcified segments, SCCTA/NSCCTA had a sensitivity, specificity, accuracy, PPV, and NPV of 68.8/84.4, 97.6/76.5, 93.1/77.7, 84.6/40.3, and 94.3/96.3 in diagnosing CAD, respectively. For both stented and calcified segments, SCCTA was significantly superior to NSCCTA in specificity and accuracy. For the calcified segments, SCCTA was significantly superior to NSCCTA in PPV. There was no significant difference in the diagnostic performance of SCCTA between the stented and calcified segments. The diagnostic accuracy and specificity of SCCTA are significantly superior to those of NSCCTA in evaluating CAD and ISR. SCCTA shows no statistical difference in its diagnostic performance between the stented and calcified segments.

172Coronary 18F-sodium fluoride uptake predicts progression of coronary arterial calcification

Abstract Background Combined positron emission tomography and computed tomography (PET-CT) using 18F-sodium fluoride (18F-NaF) to detect microcalcification provides the opportunity to gain important insights into disease activity in coronary atherosclerosis. However, the relationship between 18F-NaF uptake and progression of coronary calcification has not been determined. Purpose To determine the relationship between 18F-NaF uptake and progression of coronary calcification in patients with clinically stable coronary artery disease (CAD). Methods Patients with established, multivessel CAD underwent 18F-NaF PET-CT and CT coronary calcium scoring at baseline, with repeat CT calcium scoring at one year. Coronary arterial PET uptake was analysed qualitatively and semi-quantitatively in diseased vessels by measuring maximum tissue-to-background ratio (TBRmax) – defined as the maximum standardised uptake value in a plaque divided by mean blood pool activity measured in the right atrium. Coronary calcification was quantified by measuring calcium mass, volume, average calcium density and total Agatston score (AU). Results In total, 185 patients underwent baseline and repeat imaging (median age 66 years, 80% men), and 118 (64%) had increased 18F-NaF uptake in at least one vessel. Median total calcium score, volume, mass and average density were higher in patients with compared to those without increased 18F-NaF uptake (Table 1). At one year, patients with evidence of increased 18F-NaF uptake demonstrated more rapid progression of coronary calcification (97 [39–166] AU) versus those without uptake (35 [7–93] AU; p&lt;0.0001). Amongst 18F-NaF-positive patients, the calcium score increased only in coronary segments with 18F-NaF uptake (baseline 90.5 [27.5–202] AU versus one year 135.5 [59.3–281.8] AU; p&lt;0.0001) and not in 18F-NaF-negative segments (baseline 44.5 [16–110.5] AU versus one year 46.5 [18.25–114] AU; p=0.446). There was a moderate correlation between TBRmax and change in total calcium score, volume and mass at 1 year (Spearman's Rho = 0.37, 0.38, 0.46 respectively; p&lt;0.0001 for all). Coronary calcification at baseline in PET-negative and PET-positive patients All patients (n=185) 18F-NaF Positive (n=118) 18F-NaF Negative (n=67) P value Agatston Score (AU) 381 [107–892] 541 [245–1130] 136 [55–361] p&lt;0.0001 Calcium Volume (mm3) 358 [131–787] 506 [251–1014] 131 [64–343] p&lt;0.0001 Calcium Mass (mg) 71 [23–155] 100 [48–222] 24 [11–69] p&lt;0.0001 Average Density (mg/mm3) 0.19 [0.17–0.22] 0.20 [0.18–0.23] 0.18 [0.16–0.20] p&lt;0.0001 Conclusions Coronary 18F-NaF uptake identifies both patients and individual coronary segments with greater disease and more rapid progression of coronary calcification over one year. Acknowledgement/Funding AstraZeneca (unrestricted educational grant). British Heart Foundation (CH/09/002, RE/13/3/30183, FS/17/79/33226) Wellcome Trust (WT103782AIA).

Vascular calcification in patients with large-vessel vasculitis compared to patients with hyperlipidemia

ObjectiveCalcification of the coronary arteries, aorta, and branch vessels can occur in both large-vessel vasculitis (LVV) and atherosclerosis. The study objective was to determine the location and amount of vascular calcification in patients with LVV versus hyperlipidemia (HLD) and to identify risk factors associated with vascular calcification in LVV. MethodsPatients with giant cell arteritis (GCA), Takayasu's arteritis (TAK), and HLD underwent non-contrast computed tomography of the aorta and branch vessels. Vascular calcification in 14 specific arterial territories (4 segments of the aorta, 9 branch arteries, and the coronary arteries) was quantified throughout the large arteries by a cumulative Agatston score. Multivariate linear regression analyses were used to identify associations between traditional and disease-specific risk factors and total Agatston score. ResultsA total of 88 subjects, including GCA (n = 29); TAK (n = 22); and HLD (n = 37), participated. Prevalence of vascular calcification in the aorta and branch vessels significantly differed in the coronary arteries (HLD = 67%, GCA = 35%, TAK = 9%, p < 0.01). Total Agatston scores were higher in GCA (median 3260, range 25–18,138) versus HLD (460, 19–17,215) (p < 0.01) but did not significantly differ between GCA and TAK (1944, 52–47,520) (p = 0.53). In multivariable regression analysis, age, type of vasculitis, and prednisone use was associated with vascular calcification in LVV. ConclusionThe prevalence of coronary artery calcification is lower in LVV compared to HLD, but the amount of total vascular calcification throughout the large arteries is greater in LVV. Both traditional and disease-specific risk factors are associated with vascular calcification in LVV.

The role of multidetector computed tomography coronary angiography with calcium score and Framingham Risk Score in evaluation of rheumatoid arthritis patients with chest pain

ObjectivesTo evaluate diagnostic yield of coronary artery calcium (CAC) scoring using multidetector CT imaging in rheumatoid arthritis (RA) patients presented with chest pain. Patients and methodsSeventy RA patients evaluated using the 28 joint disease activity (DAS-28) score, Disability Index (DI) and radiologically for Larsen–Dale index. Patients underwent assessment for coronary artery disease (CAD) risk factors and coronary risk stratification using the Framingham Risk Score (FRS). Patients were clinically categorized according to criteria for typical anginal pain (TAP) and were scanned using a 64-row spiral CT scanner for CAC scoring and were stratified according Agatston CAC scores for calculation of total Agatston score (TAS). ResultsClinically, 32 patients had full criteria of TAP, 27 patients showed a picture of atypical anginal pain, while 11 patients had non-anginal chest pain. FRS predicted low, intermediate and high risk of CAD in 34, 18 and 18 patients, respectively. TAS defined no CAC in 4 patients, while mild, moderate and severe CAC was detected in 24, 36 and 6 patients, respectively. Regression analysis defined low HDL blood level, current smoking and high TAS, DAS-28 and FRS as significant predictor for TAP in decreasing order of significance. Receiver operating characteristic (ROC) curve analysis defined low HDL blood level and high TAS as the significant sensitive and specific tests, respectively. There was positive significant correlation between FRS and both of TAS scores and extent of coronary stenosis. However, FRS was the least significant predictor for TAP. ConclusionScreening of RA patients with combination of clinical scoring using FRS and CAC using non-invasive multidetector CT could allow early detection of patients at risk for acute cardiovascular events. However, TAS acts better for the prediction of TAP.

Comparison of Coronary Artery Calcium Scores Between Patients With Psoriasis and Type 2 Diabetes

Psoriasis is associated with an increased risk of cardiovascular diseases. Subclinical atherosclerosis in patients with psoriasis has not been compared with other conditions associated with increased cardiovascular risk and more rigorous cardiovascular disease screening, such as type 2 diabetes. To assess the burden of asymptomatic coronary atherosclerosis measured by coronary artery calcium score in patients with moderate to severe psoriasis compared with patients with type 2 diabetes and healthy controls. Three single-center, cross-sectional studies were performed in patients recruited from specialty outpatient clinics with moderate to severe psoriasis without type 2 diabetes (recruited from November 1, 2013, through April 31, 2015), patients with type 2 diabetes without psoriasis or other inflammatory diseases (recruited from July 1, 2009, through June 20, 2011), and age- and sex-matched healthy controls without psoriasis, type 2 diabetes, or other inflammatory diseases (recruited from July 1, 2009, through June 20, 2011). Psoriasis, type 2 diabetes, and healthy control effect on coronary artery calcium score. Coronary artery calcium measured by Agatston score. A total of 387 individuals participated in the study. Mean (SD) age was 51 (7.7), 52 (8.0), and 52 (8.0) years in the psoriasis, type 2 diabetes, and healthy control cohorts, respectively. There were 64 men (49.6%) in each group, and most patients were white (119 [92.2%], 123 [95.3%], and 128 [99.2%] in the psoriasis, type 2 diabetes, and healthy control cohorts, respectively). Patients with psoriasis had low cardiovascular risk measured by the Framingham Risk Score but had a high prevalence of cardiovascular and cardiometabolic risk factors, similar to patients with type 2 diabetes. In a fully adjusted model, psoriasis was associated with coronary artery calcium (Tobit regression ratio, 0.89; P < .001) similar to the association in type 2 diabetes (Tobit regression ratio, 0.79; P = .04). Likelihood ratio testing revealed incremental value for psoriasis in a fully adjusted model (χ2 = 4.48, P = .03) in predicting coronary artery calcium. Psoriasis was independently associated with the presence of any coronary artery calcium (odds ratio, 2.35; 95% CI, 1.12-4.94) in fully adjusted models, whereas the association of coronary artery calcium with type 2 diabetes was no longer significant after adding body mass index to the model (odds ratio, 2.18; 95% CI, 0.75-6.35). Patients with psoriasis have increased coronary artery calcium by mean total Agatston scores, similar to that of patients with type 2 diabetes, suggesting that patients with psoriasis harbor high rates of subclinical atherosclerosis beyond adjustment for body mass index. Major educational efforts for patients and physicians should be undertaken to reduce the burden of cardiovascular disease in patients with psoriasis.

Áhrif kalks í kransæðum á greiningargildi TS-kransæðarannsókna

Coronary artery calcium is known to complicate the evaluation of stenoses using computer tomography (CT). The aim of this study was to analyze the effect of coronary calcification on the diagnostic accuracy of CT coronary angiography in an Icelandic population. The study was a retrospective analysis of 417 consecutive subjects that underwent CT coronary angiography and subsequent conventional coronary angiography within 6 months. Subjects were divided based on total Agatston score: 0, 0.1-10, 10.1-100, 100.1-400, 400.1-750 and >750. Sensitivity, specificity, positive and negative predictive values were calculated for ≥50% stenoses diagnosed with the CT, using the conventional coronary angiography as a reference. Correlation between Agatston score and ≥50% stenoses was calculated. A total of 1668 coronary artery segments in 417 individuals were evaluated (68.6% men, mean age 60.2 ± 8.9). The total mean Agatston score was 420 (range from 0-4275). CT detected >50% stenoses with a sensitivity of 70.1%, specificity of 79.9% and positive and negative predictive values of 55.4% and 88.2%, respectively. The negative predictive value was 93.0% for Agatston score zero but 78.3% for Agatston score ˃750. An Agatston score threshold of 363 predicted ≥50% coronary stenoses with 49.6% sensitivity. Diagnostic accuracy is moderate with good negtive predictive value and specificity. Although coronary calcification reduces diagnostic accuracy, negative predictive value is only mildly affected for Agatston score as high as 400. Agatston score is not a good predictor of ≥50% coronary artery stenoses. No particular Agatston score cut-off level was identified to indicate whether CT angiography was useless or not.

Controversies about effects of low-kilovoltage MDCT acquisition on Agatston calcium scoring

Recent articles have advocated the possibility of obtaining Agatston coronary calcium scoring at 100 kVp by using a single adapted elevated calcium threshold. To evaluate the influence of kilovoltage potential protocols on the Agatston score, we acquired successive scans of a calcium scoring phantom at 4 levels of kilovoltage potential (80, 100, 120, and 140 kVp, 55 mAs) and measured semiautomatically the individual and the total Agatston score of 6 inserts (of 5-mm and 3-mm diameter) containing hydroxyapatite at different concentrations (800, 400, 200 mg/cm3). Our results showed that Agatston scores obtained at various low-kilovoltage potential protocols can be highly overestimated in some particular cases. At 80 kVp, for example, mean measured Agatston score was multiplied by a factor from 1.06 (5-mm highest density insert) to 2.67 (3-mm lowest density insert) compared with the Agatston scores performed at 120 kVp. Indeed in the one hand, reducing kilovoltage potential in multidetector CT acquisitions increase the CT density of coronary calcifications that can be measured on the reconstructed images. On the other hand, Agatston score is a multi-threshold measurement (with a step weighting function). Consequently low kilovoltage potential can lead to overweight some calcifications scores. For these reasons, Agatston score with low kilovoltage potential acquisition cannot be reliably adapted by a unique recalibration of the standard calcium attenuation threshold of 130 HU and requires a standardized CT acquisition protocol at 120 kVp. Alternatives to performing low-dose coronary artery calcium scans are either using coronary calcium scans with reduced tube current (low mAs) at 120 kVp with the iterative reconstructions or using mass/volume scoring (not influenced by kilovoltage potential variations). Finally, we emphasized that incorrect Agatston score evaluation may have important clinical, financial, and health care implications.

Risk Factors for Coronary Artery Calcium Among Patients With Chronic Kidney Disease (from the Chronic Renal Insufficiency Cohort Study)

Cardiovascular disease is the leading cause of death in patients with chronic kidney disease (CKD). We examined the cross-sectional association between novel risk factors and coronary artery calcium (CAC) measured using electron beam computed tomography or multidetector computed tomography among 2,018 patients with CKD. Using the total Agatston scores, the participants were classified as having no (0), moderate (>0-100), or high (>100) CAC. After adjustment for age, gender, race, study sites, cigarette smoking, previous cardiovascular disease, hypertension, and diabetes, the use of lipid-lowering drugs, body mass index, waist circumference, and cystatin C, several novel risk factors were significantly associated with high CAC. For example, the odds ratios of high CAC associated with 1 SD greater level of risk factors were 1.20 (95% confidence interval 1.04 to 1.38) for serum calcium, 1.21 (95% confidence interval 1.04 to 1.41) for serum phosphate, 0.83 (95% confidence interval 0.71 to 0.97) for log (total parathyroid hormone), 1.21 (95% confidence interval 1.03 to 1.43) for log (homeostasis model assessment-insulin resistance), and 1.23 (95% confidence interval 1.04 to 1.45) for hemoglobin A1c. Additionally, the multivariate-adjusted odds ratio for 1 SD greater level of cystatin C was 1.31 (95% confidence interval 1.14 to 1.50). Serum high-sensitive C-reactive protein, interleukin-6, tumor necrosis factor-α, and homocysteine were not statistically significantly associated with high CAC. In conclusion, these data indicate that abnormal calcium and phosphate metabolism, insulin resistance, and declining kidney function are associated with the prevalence of high CAC, independent of the traditional risk factors in patients with CKD. Additional studies are warranted to examine the causal effect of these risk factors on CAC in patients with CKD.

Coronary Artery Calcium Score: Influence of the Reconstruction Interval on Cardiac Risk Stratification in Asymptomatic Patients using Dual-Source Computed Tomography

To evaluate the impact of the reconstruction interval on coronary calcium score and cardiac risk stratification using dual-source computed tomography (DSCT). DSCT coronary calcium scoring was performed in 61 consecutive patients, and five data sets per patient were reconstructed within diastole (50 - 70 % of the R-R interval). The Agatston score, volumetric score and the relative variability were assessed for all reconstructions. To assess the individual cardiovascular risk, patients were assigned to risk groups based on age and gender-matched percentile ranks. The mean Agatston score was 184.8 ± 377.9 (relative variability 47 % ± 52 %). The mean volumetric score was 164.4 ± 310.1 (relative variability 49 % ± 58 %). There was a negative correlation between the total Agatston score and the relative variability (r = -0.37; p < 0.01). Depending on the reconstruction interval used, 18 predominantly young patients were assigned to more than one risk group. Despite the increased temporal resolution of DSCT examinations, the Agatston and volumetric scores depend on the reconstruction time within the cardiac cycle. The fact that the greatest relative variability for both the Agatston score and the volumetric score was found in young patients with small amounts of coronary calcium may result in different treatment strategies for young patients depending on the reconstruction used. Therefore, more accurate risk stratification may require the analysis of multiple reconstruction intervals.

Relationship Between Baseline Coronary Calcium Score and Demonstration of Coronary Artery Stenoses During Follow-Up: MESA (Multi-Ethnic Study of Atherosclerosis)

The MESA (Multi-Ethnic Study of Atherosclerosis) is a population-based study of 6,814 men and women. We sought to analyze the relationship between the extent of coronary artery calcium (CAC) at baseline and the severity of coronary stenoses in clinically indicated coronary angiography studies during follow-up. CAC is an established predictor of major cardiovascular events. Yet, the relationship between CAC and the distribution and severity of coronary artery stenoses has not been widely explored. All MESA participants underwent noncontrast enhanced cardiac computed tomography during enrollment to determine baseline CAC. We analyzed 175 consecutive angiography reports from participants who underwent coronary catheterization for clinical indications during a median follow-up period of 18 months. The relationship between baseline CAC and the severity of coronary stenosis detected in coronary angiographies was determined. Baseline Agatston score was 0 in only 7 of 175 (4%) MESA participants who underwent invasive angiography during follow-up. When coronary arteries were studied separately, 13% to 18% of coronary arteries with >or=75% stenosis had 0 calcium mass scores at baseline. There was close association between baseline calcium mass score and the severity of stenosis in each of the coronary arteries (test for trend, p < 0.001). For example, mean calcium mass scores for <50%, 50% to 74%, and >or=75% stenosis in the left anterior descending coronary artery were 105.1 mg, 157.2 mg, and 302.2 mg, respectively (p < 0.001). Finally, there was a direct relationship between the total Agatston Score at baseline and the number of diseased vessels (test for trend, p < 0.001). The majority of patients with clinically indicated coronary angiography during follow-up had detectable coronary calcification at baseline. Although there is a significant relationship between the extent of calcification and mean degree of stenosis in individual coronary vessels, 16% of the coronary arteries with significant stenoses had no calcification at baseline.

Coronary artery disease among asymptomatic diabetic and nondiabetic patients undergoing coronary computed tomography angiography

The aim of this study was to determine the prevalence and severity of coronary artery disease (CAD) and the plaque composition in asymptomatic diabetic and nondiabetic patients undergoing coronary computed tomography angiography (CCTA). CAD is the major cause of death among patients with diabetes. The true prevalence of CAD in asymptomatic diabetic patients, however, remains unknown. A total of 328 consecutive patients (each with at least one risk factor or abnormal stress-test results) were referred for cardiac evaluation, 42 with diabetes and 286 without diabetes, all asymptomatic for cardiac-related symptoms. Groups were matched for age, sex, and CAD risk factors. CAD was defined as coronary atherosclerosis, with obstructive or nonobstructive lesions. CCTA was performed and findings compared between patients with diabetes and those without. CAD was present in 39 (93%) diabetic patients and in 211 (73%) nondiabetic patients (P=0.006). Obstructive CAD was more common in diabetic patients than in nondiabetic patients (29 vs. 6.6%, respectively; P<0.0001). In diabetic patients, more coronary segments with atherosclerosis per patient were detected (5.5 segments/patient vs. 2.8 segments/patient in nondiabetics; P<0.0001). The total Agatston score was significantly higher in diabetic patients vs. nondiabetic patients (370+/-96 and 79.9+/-16, respectively; P<0.0001). Our results indicate a high prevalence (93%) of CAD in asymptomatic diabetic patients with either nonobstructive or obstructive lesions. CCTA may be a useful imaging modality for selecting patients at high risk who would benefit most from further evaluation for subclinical ischemia.