Global trans-lesional computed tomography-derived fractional flow reserve gradient is associated with clinical outcomes in diabetic patients with non-obstructive coronary artery disease.

BACKGROUND: Coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) enables physiological assessment and risk stratification, which is of significance in diabetic patients with nonobstructive coronary artery disease (CAD). We aim to evaluate prognostic value of the global trans-lesional CT-FFR gradient (GΔCT-FFR), a novel metric, in patients with diabetes without flow-limiting stenosis. METHODS: Patients with diabetes suspected of having CAD were prospectively enrolled. GΔCT-FFR was calculated as the sum of trans-lesional CT-FFR gradient in all epicardial vessels greater than 2 mm. Patients were stratified into low-gradient without flow-limiting group (CT-FFR > 0.75 and GΔCT-FFR < 0.20), high-gradient without flow-limiting group (CT-FFR > 0.75 and GΔCT-FFR ≥ 0.20), and flow-limiting group (CT-FFR ≤ 0.75). Discriminant ability for major adverse cardiovascular events (MACE) prediction was compared among 4 models [model 1: Framingham risk score; model 2: model 1 + Leiden score; model 3: model 2 + high-risk plaques (HRP); model 4: model 3 + GΔCT-FFR] to determine incremental prognostic value of GΔCT-FFR. RESULTS: Of 1215 patients (60.1 ± 10.3 years, 53.7% male), 11.3% suffered from MACE after a median follow-up of 57.3 months. GΔCT-FFR (HR: 2.88, 95% CI 1.76-4.70, P < 0.001) remained independent risk factors of MACE in multivariable analysis. Compared with the low-gradient without flow-limiting group, the high-gradient without flow-limiting group (HR: 2.86, 95% CI 1.75-4.68, P < 0.001) was associated with higher risk of MACE. Among the 4 risk models, model 4, which included GΔCT-FFR, showed the highest C-statistics (C-statistics: 0.75, P = 0.002) as well as a significant net reclassification improvement (NRI) beyond model 3 (NRI: 0.605, P < 0.001). CONCLUSIONS: In diabetic patients with non-obstructive CAD, GΔCT-FFR was associated with clinical outcomes at 5 year follow-up, which illuminates a novel and feasible approach to improved risk stratification for a global hemodynamic assessment of coronary artery in diabetic patients.

Vascular-specific epicardial adipose tissue in predicting functional myocardial ischemia for patients with stable chest pain.

The relationship between vascular-specific epicardial adipose tissue (vEAT) volume and myocardial ischemia measured by fractional flow reserve (FFR) was not well investigated. Patients with typical and atypical chest pain undergoing coronary computed tomographic angiography scan followed by invasive coronary angiography in combination with FFR examination within one month were retrospectively included. EAT volume and CT attenuation was calculated. The patient with FFR ≤ 0.8 in at least one vessel was referred to as functional ischemia. The mean age of all patients was 61.7 ± 8.9 years and 66.7% of patients were male. There was a significant difference for left anterior descending branch (LAD) vEAT volume between patients with and without functional myocardial ischemia (28.7 ± 10.6 cm3 vs. 23.9 ± 8.7 cm3, p = 0.005). After adjusted by cardiac risk factors and CAD-RADS categories in multivariable logistic regression analysis, LAD-vEAT volume ≥ 24.6 cm3 (OR 3.355, 95% CI 1.546-7.281, p = 0.002) remained an independent predictor of functional ischemia. After adding LAD-vEAT volume ≥ 24.6 cm3 to a prediction model composed with cardiac risk factors and CAD-RADS categories, receiver operating characteristic curve analysis showed significantly improved areas under curve (AUC) for the new model (AUC: 0.795, p = 0.0319) compared with the previous ones. Moreover, the new model revealed significance in net reclassification improvement (NRI: 0.186, p = 0.037). In conclusion, LAD-vEAT volume measurements have incremental predictive performance beyond cardiac risk factors and CAD-RADS categories in identifying significant flow-limit ischemia detected by FFR.

Progression of coronary atherosclerotic plaque burden and relationship with adverse cardiovascular event in asymptomatic diabetic patients.

BACKGROUND: The 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. METHODS: Data 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. RESULTS: Patients 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. CONCLUSION: Asymptomatic 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.

Incremental Value of Noncontrast Chest Computed Tomography-derived Parameters in Predicting Subclinical Carotid Atherosclerosis: From the PERSUADE Study.

PURPOSE: To investigate the incremental value of noncontrast chest computed tomography (CT)-derived parameters, such as coronary artery calcium score (CACS) and epicardial adipose tissue volume (EATv), in predicting subclinical carotid atherosclerosis above traditional risk factors in community-based asymptomatic populations of northern China. MATERIALS AND METHODS: A total of 2195 community-based asymptomatic individuals were enrolled from Jidong Oilfield in accordance with the PERSUADE study. CACS and EATv were measured on noncontrast chest CT. Demographics and ideal cardiovascular health score (ICHS) were collected through questionnaires. We recalculated the ideal cardiovascular health risk score (ICHRS) (ICHRS=14-ICHS) and standardized the parameters as log-CACS and body mass index adjusted EATv (i-EATv). Subclinical carotid atherosclerosis was assessed by Doppler sonography and defined as any prevalence of average carotid intima-media thickness ≥1.00 mm, appearance of carotid plaque, and carotid arterial stenosis in the areas of extracranial carotid arteries on both sides. RESULTS: A total of 451 (20.55%) individuals presented subclinical carotid atherosclerosis. CACS and EATv were significantly greater in the subclinical group, while ICHS was lower. In multivariate logistic regression, ICHRS (odds ratio [OR]=1.143, 95% confidence interval [CI]: 1.080-1.210, P <0.001), log-CACS (OR=1.701, 95% CI: 1.480-1.955, P <0.001), and i-EATv (OR=1.254, 95% CI: 1.173-1.341, P <0.001) were found to be independent risk predictors for subclinical carotid atherosclerosis. In receiver-operating characteristic curve analysis, when combined with male sex and age level, the area under the curve of the ICHRS basic model increased from 0.627 (95% CI: 0.599-0.654) to 0.757 (95% CI: 0.732-0.781) ( P <0.0001). Further adding log-CACS and i-EATv, the area under the curve demonstrated a statistically significant improvement (0.788 [95% CI: 0.765-0.812] vs. 0.757 [95% CI: 0.732-0.781], P <0.0001). CONCLUSION: Noncontrast chest CT-derived parameters, including CACS and EATv, could provide significant incremental improvement for predicting subclinical carotid atherosclerosis beyond the conventional risk assessment model based on ICHRS.

Incremental diagnostic value of perivascular fat attenuation index for identifying hemodynamically significant ischemia with severe calcification.

PURPOSE: To explore the incremental value of perivascular fat attenuation index (FAI) to identify hemodynamically significant ischemia in severe calcified vessels. METHODS: Patients who underwent coronary computed tomographic angiography (CCTA) examination at Chinese PLA General Hospital from 2017 to 2020 and subsequently underwent fractional flow reserve (FFR) examination within 1 month were consecutively included. Several CCTA-derived indices were measured, including the coronary artery calcification score (CACS), lesion length, ≥CAD-RADS 4 proportion, perivascular FAI and CT-FFR. The included vessels were divided into a nonsevere calcification group and a severe calcification group according to the quartile of CACS. FFR ≤ 0.80 represents the presence of hemodynamically significant ischemia. RESULTS: A total of 124 patients with 152 vessels were included (age: 61.1 ± 9.2 years; male 64.5%). Significant differences in lesion length (28.4 ± 14.2 vs. 23.1 ± 12.3 mm, P = 0.021), perivascular FAI (-73.0 ± 7.5 vs. -79.0 ± 7.4 HU, P < 0.001) and CT-FFR (0.78 ± 0.06 vs. 0.86 ± 0.04, P < 0.001) were noted between the FFR ≤ 0.80 group (47 vessels) and the FFR > 0.80 group (105 vessels). Furthermore, the perivascular FAI in the FFR ≤ 0.80 group was significantly greater than that in the FFR > 0.80 group (nonsevere calcification: -73.2 ± 7.5 vs. -78.2 ± 7.4 HU, P = 0.002; severe calcification: -72.8 ± 7.7 vs. -82.7 ± 6.3 HU, P < 0.001). In discriminating hemodynamically significant ischemia, the specificity and accuracy of CT-FFR were significantly affected by severe calcification, which demonstrated a significantly declining trend (P = 0.033 and P = 0.010, respectively). The diagnostic performance of CT-FFR in the severe calcification group was lower than that in the nonsevere calcified group. However, perivascular FAI showed good discriminative performance in the severe calcification group. In combination with perivascular FAI, the predictive value of CT-FFR in identifying hemodynamically significant ischemia with severe calcification increased from an AUC of 0.740 to 0.919. CONCLUSION: For coronary artery with severe calcification, the diagnostic performance of CT-FFR in discriminating flow-limiting lesions could be greatly impaired. Perivascular FAI represents a potential reliable imaging marker to provide incremental diagnostic value over CT-FFR for identifying hemodynamically significant ischemia with severe calcification.

Machine Learning Model-Based Simple Clinical Information to Predict Decreased Left Atrial Appendage Flow Velocity.

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.

CT-Based Leiden Score Outperforms Confirm Score in Predicting Major Adverse Cardiovascular Events for Diabetic Patients with Suspected Coronary Artery Disease.

OBJECTIVE: Evidence supports the efficacy of coronary computed tomography angiography (CCTA)-based risk scores in cardiovascular risk stratification of patients with suspected coronary artery disease (CAD). We aimed to compare two CCTA-based risk score algorithms, Leiden and Confirm scores, in patients with diabetes mellitus (DM) and suspected CAD. MATERIALS AND METHODS: This single-center prospective cohort study consecutively included 1241 DM patients (54.1% male, 60.2 ± 10.4 years) referred for CCTA for suspected CAD in 2015-2017. Leiden and Confirm scores were calculated and stratified as < 5 (reference), 5-20, and > 20 for Leiden and < 14.3 (reference), 14.3-19.5, and > 19.5 for Confirm. Major adverse cardiovascular events (MACE) were defined as the composite outcomes of cardiovascular death, nonfatal myocardial infarction (MI), stroke, and unstable angina requiring hospitalization. The Cox model and Kaplan-Meier method were used to evaluate the effect size of the risk scores on MACE. The area under the curve (AUC) at the median follow-up time was also compared between score algorithms. RESULTS: During a median follow-up of 31 months (interquartile range, 27.6-37.3 months), 131 of MACE were recorded, including 17 cardiovascular deaths, 28 nonfatal MIs, 64 unstable anginas requiring hospitalization, and 22 strokes. An incremental incidence of MACE was observed in both Leiden and Confirm scores, with an increase in the scores (log-rank p < 0.001). In the multivariable analysis, compared with Leiden score < 5, the hazard ratios for Leiden scores of 5-20 and > 20 were 2.37 (95% confidence interval [CI]: 1.53-3.69; p < 0.001) and 4.39 (95% CI: 2.40-8.01; p < 0.001), respectively, while the Confirm score did not demonstrate a statistically significant association with the risk of MACE. The Leiden score showed a greater AUC of 0.840 compared to 0.777 for the Confirm score (p < 0.001). CONCLUSION: CCTA-based risk score algorithms could be used as reliable cardiovascular risk predictors in patients with DM and suspected CAD, among which the Leiden score outperformed the Confirm score in predicting MACE.

Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD.

Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30-month follow-up for MACEs were included. We collected demographic characteristics, cardiovascular risk factors, and information on coronary plaques by analyzing CCTA information (plaque length, plaque composition and coronary artery stenosis of 18 coronary artery segments, coronary dominance, myocardial bridge (MB), and patients with vulnerable plaque) and follow-up information (cardiac death, nonfatal myocardial infarction and unstable angina requiring hospitalization). An ML algorithm was used for survival analysis (CoxBoost). This analysis showed that chest symptoms, the stenosis severity of the proximal anterior descending branch, and the stenosis severity of the middle right coronary artery were among the top three variables in the ML model. After the 22nd month of follow-up, in the testing dataset, ML showed the largest C-index and AUC compared with Cox regression, SIS, SIS score + clinical factors, and clinical factors. The DCA of all the models showed that the net benefit of the ML model was the highest when the treatment threshold probability was between 1% and 9%. Integrating coronary plaque information from CCTA based on ML technology provides a feasible and superior method to assess prognosis in patients with suspected coronary artery disease over an approximately three-year period.

Impact of atherosclerotic extent on clinical outcome for diabetic patients with non-obstructive coronary artery disease.

Background and aims: The prognostic impact of non-obstructive coronary artery disease (CAD) has long been underestimated due to its mild stenosis (<50% stenosis). We aim to investigate the prognostic value of atherosclerotic extent in DM patients with non-obstructive CAD. Methods: The analysis was based on a single center cohort of DM patients referred for coronary computed tomography angiography (CCTA) due to suspect CAD in 2015-2017. Based on coronary stenosis combined with segment involvement score (SIS), the study population were divided into four groups: normal (0% stenosis), non-obstructive SIS<3, non-obstructive SIS≥3 and obstructive (≥50% stenosis). The intra-class correlation (ICC) was used to test the inter-and intra-reviewer agreement. Multivariate Cox model and Kaplan-Meier method were used to evaluate the effect size of atherosclerotic extent on the prognosis. Results: In total, 1241 patients (age 60.2 ± 10.4 years, 54.1% male) were included, of which 50.2% were non-obstructive. During a median follow-up of 2.6 years, 131 MACEs (10.6%) were adjudicated, including 17 cardiovascular deaths, 28 non-fatal myocardial infarctions, 64 unstable anginas requiring hospitalization and 22 strokes. Incremental event rates could be observed across the four groups. After adjustment for age, gender, hyperlipidemia and presence of high-risk plaque, Hazard Ratio (HR) for non-obstructive SIS<3, non-obstructive SIS≥3 and the obstructive group was 1.84 (95%CI: 0.70-4.79), 3.71 (95%CI: 1.37-10.00) and 5.46 (95%CI: 2.18-13.69), respectively. Compared with non-obstructive SIS<3, non-obstructive SIS≥3 showed a significantly higher risk (HR:2.02 95%CI:1.11-3.68, p = 0.021). Similar results were demonstrated when Leiden risk score was used for sensitivity analysis. Conclusion: In DM patients with non-obstructive CAD, atherosclerotic extent was associated with higher risk of major adverse cardiac events at long-term follow-up. Efforts should be made to determine risk stratification for the management of DM patients with non-obstructive CAD.

Lipoprotein(a) is associated with coronary atheroma progression: analysis from a serial coronary computed tomography angiography study.

BACKGROUND: Lipoprotein(a) [Lp(a)] has been closely related to coronary atherosclerosis and might affect perivascular inflammation due to its proinflammatory properties. However, there are limited data about Lp(a) and related perivascular inflammation on coronary atheroma progression. Therefore, this study aimed to investigate the associations between Lp(a) and the perivascular fat attenuation index (FAI) with coronary atheroma progression detected by coronary computed tomography angiography (CCTA). METHODS: Patients who underwent serial CCTA examinations without a history of revascularization and with available data for Lp(a) within one month before or after baseline and follow-up CCTA imaging scans were considered to be included. CCTA quantitative analyses were performed to obtain the total plaque volume (TPV) and the perivascular FAI. Coronary plaque progression (PP) was defined as a ≥ 10% increase in the change of the TPV at the patient level or the presence of new-onset coronary atheroma lesions. The associations between Lp(a) or the perivascular FAI with PP were examined by multivariate logistic regression. RESULTS: A total of 116 patients were ultimately enrolled in the present study with a mean CCTA interscan interval of 30.80 ± 13.50 months. Among the 116 patients (mean age: 53.49 ± 10.21 years, males: 83.6%), 32 patients presented PP during the follow-up interval. Lp(a) levels were significantly higher among PP patients than those among non-PP patients at both baseline [15.80 (9.09-33.60) mg/dLvs. 10.50 (4.75-19.71) mg/dL,P = 0.029] and follow-up [20.60 (10.45-34.55) mg/dLvs. 8.77 (5.00-18.78) mg/dL,P = 0.004]. However, there were no differences in the perivascular FAI between PP group and non-PP group at either baseline or follow-up. Multivariate logistic regression analysis showed that elevated baseline Lp(a) level (OR = 1.031, 95% CI: 1.005-1.058,P = 0.019) was an independent risk factor for PP after adjustment for other conventional variables. CONCLUSIONS: Lp(a) was independently associated with coronary atheroma progression beyond low-density lipoprotein cholesterol and other conventional risk factors. Further studies are warranted to identify the inflammation effect exhibited as the perivascular FAI on coronary atheroma progression.

Prognostic Value of Atherosclerotic Extent in Diabetic Patients with Nonobstructive Coronary Artery Disease.

METHODS AND RESULTS: 813 DM patients (mean age 58.9 ± 9.9 years, 48.1% male) referred for CCTA due to suspected CAD in 2015-2017 were consecutively included. During a median follow-up of 31.77 months, 50 major adverse cardiovascular events (MACEs) (6.15%) were experienced, including 2 cardiovascular deaths, 14 nonfatal myocardial infarctions, 27 unstable anginas requiring hospitalization, and 7 strokes. Three groups were defined based on coronary stenosis combined with Leiden score as normal, nonobstructive Leiden < 5, and nonobstructive Leiden ≥ 5. Cox models were used to assess the prognosis of plaque burden within these groups. An incremental incidence of MACE rates was observed. After adjustment for age, gender, and presence of high-risk plaque, the group of Leiden ≥ 5 showed a higher risk than Leiden < 5 (HR: 1.88, 95% CI: 1.03-3.42, p = 0.039). Similar results were observed when segment involvement score (SIS) was used for sensitivity analysis. CONCLUSION: Atherosclerotic extent was associated with the prognosis of DM patients with nonobstructive coronary artery disease, highlighting the importance of better risk stratification and management.

Epicardial Adipose Tissue Volume Is Associated with High Risk Plaque Profiles in Suspect CAD 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.

Noninvasive Quantitative Plaque Analysis Identifies Hemodynamically Significant Coronary Arteries Disease.

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.

Stress Myocardial Blood Flow Ratio by Dynamic CT Perfusion Identifies Hemodynamically Significant CAD.

OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of stress myocardial blood flow ratio (SFR), a novel parameter derived from stress dynamic computed tomographic perfusion (CTP), for the detection of hemodynamically significant coronary stenosis. BACKGROUND: A comprehensive cardiac computed tomographic protocol combining coronary computed tomographic angiography (CTA) and CTP can provide a simultaneous assessment of both coronary artery anatomy and ischemia. METHODS: Patients with chest pain scheduled for invasive angiography were prospectively enrolled in this study. Stress dynamic CTP was performed followed by coronary CTA using a second-generation dual-source computed tomographic system. At subsequent invasive angiography, fractional flow reserve was performed to identify hemodynamically significant stenosis. For each coronary territory, SFR was defined as the ratio of hyperemic myocardial blood flow (MBF) in an artery with stenosis to hyperemic MBF in a nondiseased artery. The diagnostic accuracy of SFR to identify hemodynamically significant stenosis was determined against the reference standard of invasive fractional flow reserve ≤0.80. RESULTS: A total of 82 patients (mean age 58.5 ± 10 years) with 101 vessels with either 1- or 2-vessel disease were included. By FFR, 48 (47.5%) vessels were deemed hemodynamically significant. Hyperemic MBF and SFR were lower for vessels with hemodynamically significant lesions (95.1 ± 32.4 ml/100 ml/min vs. 142.5 ± 31.2 ml/100 ml/min and 0.66 ± 0.14 vs. 0.90 ± 0.07, respectively; p < 0.01 for both). When compared with ≥50% stenosis by CTA, the specificity for detecting ischemia by SFR increased from 43% to 91%, while the sensitivity decreased from 95% to 62%. Accordingly, the positive and negative predictive values were 85% and 73%, respectively. The combination of stenosis ≥50% by CTA and SFR resulted in an area under the curve of 0.91, which was significantly higher compared with hyperemic MBF (area under the curve = 0.79; p = 0.013). CONCLUSIONS: Calculation of SFR by dynamic CTP provides a novel and accurate method to identify flow-limiting coronary stenosis.