Abstract Funding Acknowledgements Type of funding sources: None. Background The coronary artery calcium score (CACS) independently predicts the risk of cardiovascular disease and major adverse cardiovascular events. While previous studies have demonstrated regional and ethnic differences in coronary calcification, the distribution of CACS in Southeast Asian (SEA) adults has not been investigated. Purpose The aim of this study was to determine CACS distribution in a SEA cohort living in Singapore. Methods This study involved 4945 asymptomatic patients who underwent CT coronary angiography and calcium scoring as part of screening for cardiovascular disease. Similar to the MESA study, patients with diabetes were analyzed separately due an increased prevalence of coronary calcification. A nonparametric analytical approach was used to determine CACS distribution stratified by age, gender and ethnicity. Results A positive CACS was seen in 43.7% of the overall SEA cohort with a higher prevalence in males (45.2%) than females (36.7%). The onset and burden of coronary calcification was also earlier and more severe in male subjects. There were no significant differences in CACS distribution amongst the three major ethnic groups in our study (p = 0.177). The presence of coronary calcification (CACS >0) was associated with increasing age, male gender and hypertension. Ethnicity, dyslipidemia, smoking and a family history of coronary artery disease did not significantly affect the presence of CACS. Conclusions This study provides a reference CACS distribution in an asymptomatic SEA population. There were no significant differences in CACS distribution amongst the three major ethnic groups living in Singapore.
Introduction: The 2013 ACC/AHA cholesterol management guidelines advocate the use of statin treatment for prevention of CVD. We aimed to determine the usefulness of coronary computed tomographic an...
Abstract Aims To identify differences in CT-derived perivascular (PVAT) and epicardial adipose tissue (EAT) characteristics that may indicate inflammatory status differences between post-treatment acute myocardial infarction (AMI) and stable coronary artery disease (CAD) patients. Methods and Results A cohort of 205 post-AMI patients (age 59.8±9.2, 92.2% male) was propensity-matched with 205 stable CAD patients (age 60.5±10.0, 90.2% male). Coronary CT angiography and non-contrast CT scans were performed to assess PVAT mean attenuation across major coronary segments and EAT mean attenuation and volumes, respectively. For post-AMI patients, CT scans were conducted 28.6 ± 13.8 days after the AMI incidence. Post-AMI patients showed higher non-culprit PVAT and EAT mean attenuation than stable CAD patients (8.01HU, 95% CI 5.90 to 10.11 HU, p<0.001, 2.48 HU, 95% CI 0.83 to 4.13 HU, p=0.003, respectively). The EAT volume percentage at higher attenuation levels was higher in post-AMI patients compared to stable CAD (33.93cm3, 95% CI 16.86 to 51.00 cm3, p<0.001), with the difference maximized at the -70 HU threshold (4.75%, 95% CI 3.64% to 5.87%, p<0.001). PVAT mean attenuation positively correlated with EAT mean attenuations and the percentage of EAT volume >-70 HU (p<0.001 for both). Conclusions Post-AMI patients showed higher PVAT and EAT attenuation than stable CAD patients, potentially indicating AMI-associated inflammatory cardiac adipose tissue changes. -70 HU can act as a potential cut-off for inflamed EAT. These findings highlight the potential of using CT-derived adipose tissue characteristics to assess inflammation and guide post-AMI management strategies.
Introduction: Necrotic core (NC) is a surrogate of high-risk plaque identified by histopathologic analysis in sudden coronary death (SCD) victims. Non-invasive discrimination of NC from fibrous plaque (FP) is challenging at present due to overlapping Hounsfield Units (HU) between NC and FP. Dual-energy CT (DECT) as a novel imaging technique, enables differentiation of tissue materials based upon atomic density and evaluation of images based on monochromatic energies (MCE) rather than polychromatic spectra of energies by single-energy CT (SECT). Hypothesis: To determine whether DECT could improve discrimination of high-risk plaque features, and compare DECT-MCE to SECT for discrimination of NC from FP. Methods: Coronary specimens were obtained from 12 post-mortem hearts from autopsy-validated SCD victims. NC and FP were identified on histologic sections. Histology images with a ≥90% uniform region of interest (ROIs) of NC and FP, and corresponding to a 0.4 mm 2 area, were created. HU values for SECT and DECT-MCE (ranging from 40 to 140 kilo-electron voltages [keV]) were calculated for histologic ROIs based on NC and FP. Standardized HU were defined as the mean HU measure divided by the corresponding lumen mean HU measure for NC and FP. The maximum difference in standardized median HU values between NC and FP were then calculated at each DECT-MCE to determine the optimal viewing mode for diagnosis of NC. Results: Of 136 sections, a total 56 ROIs (23 NCs and 33 FPs) were measured. Across a range of DECT-MCE, varying minimum differences in standardized median HU values between NC and FP was displayed, with the highest difference at 140 keV (0.25, NC 0.12, FP 0.37) and lowest difference at 60 keV (0.10, NC: 0.37; FP: 0.47). Compared to DECT-MCE, SECT was poorer at discrimination of NC and FP, with a minimum difference of 0.07 between NC and FP. Conclusions: DECT is superior to SECT for discrimination of NC and FP in coronary lesions from SCD victims, with a 140 keV MCE performing best for DECT.
Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.
Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death and disability worldwide. As such, new treatments are needed to prevent the onset and progression of atherosclerosis to improve outcomes in patients with coronary, cerebrovascular, and peripheral arterial disease. In this regard, inflammation is known to be a critical driver of atherosclerosis formation and progression, thus it is a viable target for vascular protection in patients at risk of developing ASCVD. Leukotrienes, key pro-inflammatory lipid mediators derived from arachidonic acid, are associated with atheroma inflammation and progression. Genetic mutations in key components of the leukotriene synthesis pathway, such as 5-lipoxygenase (5-LO) and 5-lipoxygenase-activating protein (FLAP), are associated with an increased risk of cardiovascular disease, and pharmacological inhibition of 5-LO and FLAP has been reported to prevent atheroma formation in pre-clinical and early clinical studies. In this article, we provide an overview of these studies and highlight the therapeutic potential of targeting leukotriene synthesis to prevent atheroma inflammation and progression and improve outcomes in patients at risk of ASCVD.
Coronary computed tomography angiography (CCTA) and coronary artery calcium score (CACS) have prognostic value for coronary artery disease (CAD) events beyond traditional risk assessment. Age is a risk factor with very high weight and little is known regarding the incremental value of CCTA over CAC for predicting cardiac events in older adults. Of 27 125 individuals undergoing CCTA, a total of 3145 asymptomatic adults were identified. This study sample was categorized according to tertiles of age (cut-off points: 52 and 62 years). CAD severity was classified as 0, 1–49, and ≥50% maximal stenosis in CCTA, and further categorized according to number of vessels ≥50% stenosis. The Framingham 10-year risk score (FRS) and CACS were employed as major covariates. Major adverse cardiovascular events (MACE) were defined as a composite of all-cause death or non-fatal MI. During a median follow-up of 26 months (interquartile range: 18–41 months), 59 (1.9%) MACE occurred. For patients in the top age tertile, CCTA improved discrimination beyond a model included FRS and CACS (C-statistic: 0.75 vs. 0.70, P-value = 0.015). Likewise, the addition of CCTA improved category-free net reclassification (cNRI) of MACE in patients within the highest age tertile (e.g. cNRI = 0.75; proportion of events/non-events reclassified were 50 and 25%, respectively; P-value <0.05, all). CCTA displayed no incremental benefit beyond FRS and CACS for prediction of MACE in the lower age tertiles. CCTA provides added prognostic value beyond cardiac risk factors and CACS for the prediction of MACE in asymptomatic older adults.
This paper presents the design, fabrication, and test results for a novel basket catheter that utilizes soft robotic technology, which can conform to complex patient anatomy. Two designs of basket-shaped balloons in three sizes are fabricated based on a CO2 laser cutting method from thin (<50 µm) thermoplastic polyurethane. The balloons are deployed in four soft-material 3D printed left atria, whose geometries are based on volume rendered segmentation of cardiac computed tomography (CT) scans. The coverage and conformability to the realistic patient anatomies is tracked with the small patches of pH paper that indicate, via a color change, contact with a basic solution that lined the 3D printed atriums. The conformability of these inflatable basket catheters is demonstrated as high as (85%) for the optimized design. To visualize the balloon's performance, microCT images of balloons deployed in 3D printed models are shown. These images show the ability of the balloons to adapt to complex patient anatomy and do not exhibit any spline bunching or other deleterious mechanical behavior. This platform has the potential to be coupled with electrical sensors for simultaneous multisensor mapping of atrial fibrillation and other cardiac arrhythmias.
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