Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography

BACKGROUND: High pericoronary adipose tissue (PCAT) attenuation and non-calcified plaque burden (NCP) measured from coronary CT angiography (CTA) have been implicated in future cardiac events. We aimed to evaluate the interobserver and intraobserver repeatability of PCAT attenuation and NCP burden measurement from CTA, in a sub-study of the prospective SCOT-HEART trial. METHODS: Fifty consecutive CTAs from participants of the CT arm of the prospective SCOT-HEART trial were included. Two experienced observers independently measured PCAT attenuation and plaque characteristics throughout the whole coronary tree from CTA using semi-automatic quantitative software. RESULTS: We analyzed proximal segments in 157 vessels. Intraobserver mean differences in PCAT attenuation and NCP plaque burden were −0.05HU and 0.92% with limits of agreement (LOA) of ±1.54 and ±5.97%. Intraobserver intraclass correlation coefficients (ICC) for PCAT attenuation and NCP burden were excellent (0.999 and 0.978). Interobserver mean differences in PCAT attenuation and NCP plaque burden were 0.13HU [LOA ±1.67HU] and −0.23% (LOA ±9.61%). Interobserver ICC values for PCAT attenuation and NCP burden were excellent (0.998 and 0.944). CONCLUSION: PCAT attenuation and NCP burden on CTA has high intraobserver and interobserver repeatability, suggesting they represent a repeatable and robust method of quantifying cardiovascular risk

Computed tomography attenuation of periaortic adipose tissue in abdominal aortic aneurysms

Purpose: To assess periaortic adipose tissue attenuation on CT angiography indifferent abdominal aortic aneurysm disease states.Materials and Methods: In a retrospective observational study from January 2018 to December 2022, periaortic adipose tissue attenuation was assessed on CT angiography in patients with asymptomatic or symptomatic (including rupture) abdominal aortic aneurysms, and control individuals without aneurysms. Adipose tissue attenuation was measured using semi-automated software in periaortic aneurysmal and non-aneurysmal segments of the abdominal aorta, and in subcutaneous and visceral adipose tissue. Periaortic adipose tissue attenuation values between the three groups was assessed using Students t-test and Wilcoxon rank sum test followed by a multi-regression model.Results: Eighty-eight individuals (median age, 70 [IQR, 65-78] years; 78 male and 10 female) were included: 70 patients with abdominal aortic aneurysms (40 asymptomatic and 30 symptomatic including 24 with rupture), and 18 controls. There was no evidence of differences in the periaortic adipose tissue attenuation in the aneurysmal segment in asymptomatic patients versus controls ((-81.44±7 versus -83.27±9 HU, Hounsfield units, P=0.43) and attenuation in non-aneurysmal segments between asymptomatic patients versus controls (-75.43±8 versus -78.81±6 HU, P=0.08). However, symptomatic patients demonstrated higher periaortic adipose tissue attenuation in both aneurysmal (-57.85±7 HU, P<0.0001) and non-aneurysmal segments (-58.16±8 HU, P<0.0001) when compared with the other two groups.Conclusions: Periaortic adipose tissue CT attenuation was not increased in stableabdominal aortic aneurysm disease. There was a generalised increase in attenuation in patients with symptomatic disease, likely reflecting the systemic consequences of acute rupture

Artificial Intelligence in Ventricular Arrhythmias and Sudden Death

Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field