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BackgroundThe heterogeneity of risk in patients with diabetes mellitus (DM) is acknowledged in new guidelines promulgating different treatment recommendations for diabetics at low cardiac risk. We performed a retrospective longitudinal follow-up study to evaluate coronary plaque progression and its impact on cardiac events in asymptomatic diabetic patients.MethodsData of 197 asymptomatic patients (63.1 ± 17 years, 60% males) with DM and suspected coronary artery disease (CAD) who underwent clinically indicated dual-source cardiac computed tomography (CT) were retrospectively analyzed. Patients with DM received standard of care treatment. Patients were classified into two groups based on CT coronary artery calcium scores (CACS): A, CACS> 10; B, CACS≤10. Progression of coronary plaque burden in both groups was evaluated and compared by baseline and follow-up coronary CT angiography (CCTA) using semi-automated plaque analysis and quantification software. Follow-up data were retrospectively gathered from medical records and endpoints of cardiac events were recorded via prospective phone-calls. The impacts of plaque composition and progression on cardiac events were specifically assessed.ResultsPatients with CACS> 10 showed an increase in dense coronary calcium volume, while patients with CACS≤10 had a more pronounced increase in the volume of low-attenuation “lipid-rich” plaque components between CCTA acquisitions. The composite endpoint occurred in 20 patients (10.2%) after a median follow-up period of 41.8 months. Furthermore, at follow-up CCTA, the presence of CACS> 10 (adjusted odds ratio, 0.701; 95% CI, 0.612–0.836), increase of dense calcium volume (OR, 0.860 95% CI, 0.771–0.960), and lipid volume (OR, 1.013; 95% CI, 1.007–1.020) were all independent predictors of cardiac events.ConclusionAsymptomatic patients with DM experienced plaque progression as well as progression to “overt or silent CAD”. The relative increase in plaque volume was associated with subsequent cardiac events, and the coronary calcification seemed to be inversely related to the outcome in asymptomatic diabetic patients.

Objective. To explore the association between EAT volume and plaque precise composition and high risk plaque detected by coronary computed tomography angiography (CCTA). Methods. 101 patients with suspected coronary artery disease (CAD) underwent CCTA examination from March to July 2019 were enrolled, including 70 cases acute coronary syndrome (ACS) and 31 cases stable angina pectoris (SAP). Based on CCTA image, atherosclerotic plaque precise compositions were analyzed using dedicated quantitative software. High risk plaque was defined as plaque with more than 2 high risk features (spotty calcium, positive remolding, low attenuation plaque, napkin-ring sign) on CCTA image. The association between EAT volume and plaque composition was assessed as well as the different of correlation between ACS and SAP was analyzed. Multivariable logistic regression analysis was used to explore whether EAT volume was independent risk factors of high risk plaque (HRP). Results. EAT volume in the ACS group was significantly higher than that of the SAP group ( 143.7 ± 49.8  cm3 vs. 123.3 ± 39.2  cm3, P = 0.046 ). EAT volume demonstrated a significant positive correlation with total plaque burden ( r = 0.298 , P = 0.003 ), noncalcified plaque burden ( r = 0.245 , P = 0.013 ), lipid plaque burden ( r = 0.250 , P = 0.012 ), and homocysteine ( r = 0.413 , P ≤ 0.001 ). In ACS, EAT volume was positively correlated with total plaque burden ( r = 0.309 , P = 0.009 ), noncalcified plaque burden ( r = 0.242 , P = 0.044 ), and lipid plaque burden ( r = 0.240 , P = 0.045 ); however, no correlation was observed in SAP. Patients with HRP have larger EAT volume than those without HRP ( 169 ± 6.2  cm3 vs. 130.6 ± 5.3  cm3, P = 0.002 ). After adjustment by traditional risk factors and coronary artery calcium score (CACS), EAT volume was an independent risk predictor of presence of HRP (OR: 1.018 (95% CI: 1.006-1.030), P = 0.004 ). Conclusions. With the increasing EAT volume, more dangerous plaque composition burdens increase significantly. EAT volume is a risk predictor of HRP independent of convention cardiovascular risk factors and CACS, which supports the potential impact of EAT on progression of coronary atherosclerotic plaque.

Objective: To evaluate the diagnostic performance of automated quantitative analysis by coronary computed tomography angiography (CCTA) in identifying lesion-specific hemodynamic abnormality. Methods: A total of 132 patients (mean age, 61 y; 86 men) with 169 vessels (with 30% to 90% diameter stenosis), who successively underwent invasive coronary angiography with evaluation of fractional flow reserve (values ≤0.8 were defined as lesion-specific hemodynamic abnormalities), were analyzed by CCTA. CCTA images were quantitatively analyzed using automated software to obtain the following index: maximum diameter stenosis (MDS%); maximum area stenosis (MAS%); lesion length (LL); volume and burden (plaque volume×100 per vessel volume) of total plaque (total plaque volume [TPV], total plaque burden [TPB]), calcified plaque (calcified plaque volume [CPV], calcified plaque volume burden [CPB]), noncalcified plaque (noncalcified plaque volume [NCPV], noncalcified plaque volume burden [NCPB]), lipid plaque (lipid plaque volume [LPV], lipid plaque burden [LPB]), and fibrous plaque (fibrotic plaque volume [FPV], fibrotic plaque burden [FPB]); napkin-ring sign (NRS); remodeling index (RI); and eccentric index (EI). Logistic regression and area under the receiver operating characteristics (AUC) were used for statistical analysis. Results: Fractional flow reserve ≤0.80 was found in 57 (33.73%) of the 169 vessels. Vessels with hemodynamic significance had greater MDS% (64.43%±8.69% vs. 57.33%±9.95%, P<0.001), MAS% (73.18%±8.56% vs. 64.66%±8.95%, P<0.001), and lipid plaque burden (12.75% [9.73%, 19.56%] vs. 9.41% [4.10%, 15.70%], P=0.01) compared with vessels with normal hemodynamics. In multivariable logistic regression analysis, MAS% >68% (odds ratio: 7.20, 95% confidence interval [CI]=2.89-17.91, P<0.001) and LPB >10.03% (odds ratio=4.32, 95% CI=1.36-13.66, P=0.01) were significant predictors of hemodynamic abnormalities. In predicting lesion-specific hemodynamic abnormalities, the AUC was 0.77 (95% CI=0.70-0.85) for MAS% versus 0.71 (95% CI=0.63-0.79) for MDS% (P<0.05), 0.66 (95% CI=0.58-0.74) for LPV (P<0.05), 0.66 (95% CI=0.58-0.74) for LPB (P<0.05), and 0.63 (95% CI=0.54-0.71) for TPB (P<0.05). The AUC of MAS%+LPB (0.83, 95% CI=0.76-0.89) was significantly improved compared with that of MAS% (0.77, 95% CI=0.70-0.85, P<0.05). Conclusions: Compared with MDS% and the volume burdens of plaque compositions, MAS% has a higher diagnostic accuracy for coronary hemodynamic abnormalities in the precise quantitative analysis of coronary plaques on the basis of CT. Furthermore, MAS%+LPB might improve the diagnostic accuracy beyond MAS% alone.

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Background: Transesophageal echocardiography (TEE) is the first technique of choice for evaluating the left atrial appendage flow velocity (LAAV) in clinical practice, which may cause some complications. Therefore, clinicians require a simple applicable method to screen patients with decreased LAAV. Therefore, we investigated the feasibility and accuracy of a machine learning (ML) model to predict LAAV. Method: The analysis included patients with atrial fibrillation who visited the general hospital of PLA and underwent transesophageal echocardiography (TEE) between January 2017 and December 2020. Three machine learning algorithms were used to predict LAAV. The area under the receiver operating characteristic curve (AUC) was measured to evaluate diagnostic accuracy. Results: Of the 1039 subjects, 125 patients (12%) were determined as having decreased LAAV (LAAV < 25 cm/s). Patients with decreased LAAV were fatter and showed a higher prevalence of persistent AF, heart failure, hypertension, diabetes and stroke, and the decreased LAAV group had a larger left atrium diameter and a higher serum level of NT-pro BNP than the control group (p < 0.05). Three machine-learning models (SVM model, RF model, and KNN model) were developed to predict LAAV. In the test data, the RF model performs best (R = 0.608, AUC = 0.89) among the three models. A fivefold cross-validation scheme further verified the predictive ability of the RF model. In the RF model, NT-proBNP was the factor with the strongest impact. Conclusions: A machine learning model (Random Forest model)-based simple clinical information showed good performance in predicting LAAV. The tool for the screening of decreased LAAV patients may be very helpful in the risk classification of patients with a high risk of LAA thrombosis.