Nogami, Kai

Background: Previous studies have linked pericoronary artery adipose tissue attenuation to major adverse cardiac event (MACE). However, the association between pericarotid artery fat and prognosis remains underexplored. This study aimed to investigate the association between preoperative pericarotid fat characteristics and the subsequent prognosis in patients undergoing carotid endarterectomy (CEA). Methods: This study included patients with preoperative computed tomography angiography (CTA) followed by CEA. Preoperative neck CTA images were obtained and analyzed with novel methods using 3D Slicer software. We segmented the common and internal carotid arteries. The segment was horizontally trimmed, isolating the region around the bifurcation, extending 15 mm above and 10 mm below. Pericarotid fat was defined as adipose tissue within a 5 mm radial distance from the outer vessel wall, with Hounsfield units (HU) between -190 and -30 (Figure 1, carotid segmentation [green] and pericarotid fat [yellow]) . For each patient, we calculated the HU average and the volume (mm3) of the pericarotid fat. MACE was defined as a composite of all-cause mortality, myocardial infarction, and cerebrovascular events. The relationship between both pericarotid fat characteristics and MACE was investigated. Results: Of the 43 patients initially included, one was excluded due to an inability to perform segmentation caused by chronic total occlusion, leaving 42 patients (age 73 [67-78] years, 33 [76.7%] male). The comparison of mean pericarotid fat HU between the ipsilateral and contralateral sides showed no significant difference (-60.3±11.0 HU vs. -60.9 ±11.3 HU, p=0.437). However, the median ipsilateral fat volume was significantly higher than the contralateral volume (528 [248, 1100] mm3 vs. 328 [184, 697] mm3, p=0.020). When comparing patients with and without MACE, those with MACE had a significantly lower ipsilateral fat density (-59.4±10.2 HU vs. -66.8 ±11.1 HU, p=0.022) and a higher fat volume (468 [233, 746] mm3 vs. 845 [458, 1451] mm3, p=0.044). Kaplan-Meier analysis revealed that higher volume and lower fat density than the median were significant predictors of MACE (log-rank p=0.015 and p=0.042, respectively) (Figure 2). Conclusions: Assessment of pericarotid fat using a novel CTA analytic method in patients undergoing CEA identified ipsilateral lower fat density and higher fat volume as predictors of adverse outcomes.

Background: Atherosclerotic carotid artery disease is associated with a high risk of major adverse cardiac events (MACE) even with optimal risk factor management and surgical interventions such as carotid endarterectomy (CEA). Aortic stenosis (AS) is an age-related valve disease that is associated with a high rate of MACE. An artificial intelligence-based AS estimation algorithm (AI-AS) can identify AS with high accuracy using electrocardiogram. This study evaluates the prognostic value of AI-AS in patients with carotid artery disease undergoing CEA. Methods: We included patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 if they had >30 days of follow-up and an electrocardiogram within one year before CEA. The electrocardiogram closest to the CEA date was used to calculate AI-AS (AS probability ranging from 0-1). MACE was defined as the composite of cardiac events, ischemic cerebrovascular accidents, and all-cause mortality during follow-up after CEA. A receiver operating characteristic curve and the Youden index were used to identify the optimal AI-AS cut-off value for predicting MACE. Kaplan Meier plot was used to depict MACE-free survival. Regression models were used to evaluate the predictive value of AI-AS for future MACE adjusting for clinical covariates. Results: A total of 665 patients with a median age of 72 [IQR:66, 78] years and 438 (65.9%) males were included. Over a median follow-up time of 7.1 [IQR:2.7, 11.5] years, MACE occurred in 419 (63%) of the patients, including 187 (44.6%) mortality, 161 (38.4%) cardiac events, and 71 (16.9%) cerebrovascular accidents. Median AI-AS was 0.32 [IQR:0.09, 0.55] in the total cohort, 0.34 [IQR:0.12, 0.56] in patients with MACE, and 0.30 [IQR:0.07, 0.53] in patients without MACE (P=0.038). AI-AS was a significant predictor of MACE (HR:2.38, 95%CI:1.64-3.46, P<0.001) in univariate Cox regression analysis. The optimal cut-off value of AI-AS for MACE was 0.419 (area under the curve: 0.55). Patients with high AI-AS (≥0.419) had a significantly lower MACE-free survival in the Log-rank test ( Figure 1 ) and in the multivariable Cox regression model adjusted for covariates compared to low AI-AS (<0.419) patients ( Table 1 ). Conclusion: AI-AS is an independent predictor of MACE in patients with carotid artery disease after CEA and can be used as a risk-stratifying tool in this patient population. Additional studies are needed to elucidate the mechanistic link between AS and MACE in these patients.

Background: Previous research has suggested pericarotid fat density to be significantly linked to atherosclerotic plaque components and cerebrovascular events. Inflammatory markers within the plaques are critical in the pathogenesis and progression of carotid artery disease. However, the association between pericarotid fat and inflammatory processes within atherosclerotic plaques remains underexplored. This study hypothesizes that pericarotid fat density is directly associated with the levels of inflammatory markers within atherosclerotic plaques. Methods: Carotid plaques were prospectively collected from patients with carotid artery disease undergoing carotid endarterectomy (CEA). The levels of inflammatory plaque cytokines (MCP-1, IL-8, IFN-γ, IL-10, IL-1β, IL-6, PDGF-AA, PDGF-AB/BB, TNF-α, VEGF) were measured in tissue homogenates using a customized 10-plex Luminex assay. Baseline demographics and clinical data of our population were extracted from electronic health records. We analyzed preoperative neck computed tomography angiography (CTA) images placing regions-of-interest (ROI) in the pericarotid fat on the slice showing maximal stenosis. For each patient, we calculated the average of Hounsfield Units (HU) within the ROI. Spearman correlation test was used to assess the correlation between variables. Results: Among the 43 patients included, the median age was 73 (67, 78) years, and 33 (76.7%) patients were male. Plaque levels of PDGF-AA and VEGF showed significant positive correlations with HU (r=0.330, p=0.030; r=0.380, p=0.011, respectively), as illustrated in Figure 1 . Although plaque levels of IFN-γ demonstrated a significant negative correlation with HU (r=-0.390, p=0.010), the most cases exhibited values close to zero. Conclusions: The assessment of pericarotid fat density on CTA may allow for the estimation of plaque inflammation, suggesting that more fat density is associated with increased inflammatory activity within atherosclerotic plaques.

Introduction: Patients with atherosclerotic carotid artery disease are at high risk of mortality in the long-term follow-up after carotid endarterectomy (CEA), partly due to dysrhythmia. Atrial fibrillation (AF) is a common cardiac dysrhythmia linked to stroke and cardiovascular events. An artificial intelligence-based electrocardiogram (ECG) AF algorithm (AI-AF) can effectively identify silent AF. We aimed to assess the effectiveness of AI-AF in predicting mortality in patients with carotid artery disease undergoing CEA. Methods: Patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 were included if they had >30 days follow-up and an ECG within one year before CEA. The ECG closest to the CEA date was used to calculate AI-AF (probability of AF ranging from 0 to 1). All-cause mortality rates after CEA were obtained from clinical records. The optimal AI-AF cut-off value for predicting mortality was calculated using a receiver operating characteristic curve and the Youden index. Long-term survival after CEA was depicted with a Kaplan Meier plot and compared with the Log-rank test. Univariate and multivariate Cox Regression models were used to assess the predictive value of AI-AF for mortality, adjusting for age, sex, body mass index, diabetes mellitus, hypertension, and hyperlipidemia. Results: A total of 636 patients with a median age of 72 [IQR: 66, 78] years and 414 (65.1%) males were included. The median AI-AF was 0.056 [IQR: <0, 0.171]. Over a median follow-up time of 7.1 [IQR: 2.7, 11.5] years, mortality occurred in 326 (51.3%) patients. AI-AF significantly predicted mortality in univariate and multivariate Cox regression analysis (HR: 3.47, 95%CI: 2.34-5.16, P<0.001; HR: 2.30, 95%CI: 1.44-3.68, P<0.001, respectively). The best cut-off value for AI-AF for mortality prediction was 0.025 (area under the curve: 0.608). High and low AI-AF were defined as AI-AF≥ 0.025 and <0.025, respectively. Mortality was significantly higher in high AI-AF compared to low AI-AF patients (P<0.001, Figure 1A,1B), and high AI-AF was a significant predictor of mortality (univariate: Figure 2; multivariate Cox regression: HR: 2.11, 95%CI: 1.56-2.84, P<0.001). Conclusion: AI-AF can identify patients with a higher risk of mortality after CEA. This finding supports a role for AI algorithms in risk stratification of patients with carotid artery disease following CEA. Future studies are needed to elucidate the contribution of AF to all-cause mortality.

Background: The bovine aortic arch is a vascular variant in which the left common carotid artery originates from the brachiocephalic trunk rather than directly from the aortic arch. The bovine anatomical variation may alter the blood flow dynamics, potentially contributing to the formation and progression of carotid plaques. In this study, we evaluated the association of the bovine aortic arch and the severity of carotid stenosis. Methods: In this retrospective study, we included patients with carotid artery disease undergoing carotid endarterectomy (CEA) and pre-surgical computed tomography angiography (CTA). CTAs were reviewed to assess the carotid stenosis and the anatomy of the aortic arch. The left carotid artery was classified as normal if it originated from the aortic arch and as bovine if it originated from the brachiocephalic trunk (Figure 1) . Unlimited propensity score matching was performed to balance baseline characteristics between patients with bovine and normal anatomies. The Mann-Whitney U test was applied to compare carotid stenosis between bovine and normal arteries. Results: A total of 203 patients (bovine:23, normal:180) were initially enrolled. Using propensity score matching, 20 patients with bovine anatomy were matched to 45 patients with normal anatomy (Figure 2) . Among the 65 patients included, 47 (74.6%) were male, with a median age of 70 [IQR:65.0-76.0] years. Left-sided carotid artery disease was more prevalent in the bovine compared to normal group (N=12, 66.7% vs. N=16, 36.4%; p=0.030). Additionally, the severity of carotid stenosis in CEA-treated side was higher in patients with bovine arch compared to normal controls (82.5% [76.2-88.7] vs. 70.0% [50.0-95.0]; p=0.030) (Figure 3) . Conclusions: The association of a bovine aortic arch with more severe carotid stenosis suggests that this anatomical variant may be a marker for increased cerebrovascular risk.

Background: Cardiac allograft vasculopathy (CAV) is one of the leading causes of graft failure and death after heart transplantation (HTx). Conventionally, CAV is characterized by concentric intimal thickening, however the long-term progression patterns of plaque morphology are not yet well understood. Research Question: This study aimed to assess the plaque morphology, including histology and eccentricity, in a temporal manner post-HTx using virtual histology intravascular ultrasonography (VH-IVUS). Methods: We included 50 patients who underwent serial VH-IVUS assessment at three assessment points (A1:0-1 year, A2:3-5 years, A3:7-10 years) after HTx. Differences in VH-IVUS findings between timepoints A1 to A2 (early period) and A2 to A3 (late period) were investigated. Plaque volume index was defined as (plaque volume/vessel volume) × 100%. Eccentricity index (EI) was defined as (maximum intimal thickness – minimum intimal thickness)/maximum intimal thickness and was evaluated in 39 patients with significant plaque (maximal intimal thickness >0.50 mm at the A3) at each timepoint. The serial changes of EI and relationship between EI and other VH-IVUS findings were investigated. Results: Of 50 patients, mean age was 51±13 years, 36 (72%) were males. In both early and late periods, the plaque volume (PV) index showed a significant increase, however, the rate of progression was significantly slower in late period. (5.8% [0.9, 13.2] vs 2.4% [-0.2, 7.4], p=0.015). The EI showed a trend of decrease in the early period, compared to increases in the late period (-0.040 [-0.103, 0.004] vs 0.003 [-0.030, 0.052], p=0.036). Patients with EI change in late period higher than median value had significantly lower proportion of fibrous plaque (70.8% [67.2, 75.1] vs 62.7 [55.5, 66.9], p=0.002) and higher dense calcium (1.1% [0.9, 1.9], 4.5% [1.8, 6.9], p=0.022) at A3. Conclusion: In heart transplant patients, coronary artery plaque progression in the late period showed more eccentric progression with lower fibrous plaque composition, a pattern commonly observed in typical and conventional coronary atherosclerosis. These findings may provide a novel mechanism and therapeutic insight into the temporal patterns in CAV progression during long term follow up.