Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

OBJECTIVES: To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). METHODS: Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. RESULTS: Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). CONCLUSION: Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. KEY POINTS: • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study.

OBJECTIVES: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. METHODS: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. RESULTS: In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). CONCLUSION: In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. KEY POINTS: • In stable patients with intermediate stenosis, ML-based FFRCT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRCTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFRCT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia.

OBJECTIVES: Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia. METHODS: Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard. RESULTS: Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05). CONCLUSIONS: EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis. KEY POINTS: • CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Impact of age on the outcome of cryoballoon ablation as the primary approach in the interventional treatment of atrial fibrillation: Insights from a large all-comer study.

INTRODUCTION: The objective was to analyze the impact of patient age on clinical characteristics, procedural results, safety, and outcome of cryoballoon ablation (CBA) as the primary approach in the interventional treatment of symptomatic atrial fibrillation (AF). METHODS AND RESULTS: The single-center prospective observational study investigated consecutive patients who underwent initial left atrial ablation for symptomatic paroxysmal (PAF) or persistent AF (persAF). Age groups (A-F) of less than 40, 40-49, 50-59, 60-69, 70-79 and more than or equal to 80 years were evaluated. Follow-up (FU) included ECG, Holter monitoring and assessment of AF-symptoms. From 2012 to 2016, a total of 786 patients (64 ± 11 years, range 21-85) underwent CBA. With advancing age, more cardiovascular comorbidities and larger LA diameter were observed, more females were included (each p < .001). PAF (57%) and persAF (43%, p = .320) were equally distributed over all age groups. Age was neither related to procedural parameters, nor to the complication rate (3.9%, p = .233). Median FU was 38 months. Two non-procedure related noncardiac deaths occurred late during FU. Freedom from arrhythmia was independent of age at 18 months (p = .210) but decreased for patients more than or equal to 70 years at 24 months (p = .02). At 36 months, freedom from arrhythmia was 66%-74% (groups A-D), 54% (E) and 49% (F), respectively (p = .002). LA diameter and persAF were independent predictors, whereas age was a dependent predictor of recurrence. CONCLUSION: CBA as the primary approach in the initial ablation procedure is safe and highly effective in the young, middle aged, and elderly population. LA diameter and persAF, but not ageing, were independent predictors for arrhythmia recurrence.

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning.

OBJECTIVES: To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). METHODS: Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC). RESULTS: MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93-0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80-0.87]), segment involvement score (AUC 0.88 [95%CI 0.84-0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86-0.92], all p < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72-0.82, all p < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71-0.76, all p < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024). CONCLUSION: Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient's information to enhance risk stratification. KEY POINTS: • A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.

Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease.

OBJECTIVE: To investigate the utility of coronary CT angiography-derived fractional flow reserve (FFRCT) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE). METHODS: This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death. FFRCT and plaque parameters were analyzed on a per-vessel and per-patient basis. Univariable and multivariable COX regression analysis determined predictors of MACE. The prognostic value of FFRCT and plaque progression were assessed in nested models. RESULTS: Two hundred eighty-four patients (median age, 61 years (interquartile range, 54-70); 202 males) were evaluated. MACE was observed in 45 patients (15.8%, 45/284). By Cox multivariable regression modeling, vessel-specific FFRCT ≤ 0.80 was associated with a 2.4-fold increased risk of MACE (HR (95% CI): 2.4 (1.3-4.4); p = 0.005) and plaque progression was associated with a 9-fold increased risk of MACE (HR (95% CI): 9 (3.5-23); p < 0.001) after adjusting for clinical and imaging risk factors. FFRCT and plaque progression improved the prediction of events over coronary artery calcium (CAC) score and high-risk plaques (HRP) in the receiver operating characteristics analysis (area under the curve: 0.70 to 0.86; p = 0.002). CONCLUSIONS: Fractional flow reserve and plaque progression assessed by serial coronary CT angiography predicted the risk of future MACE. KEY POINTS: • Vessel-specific CT angiography-derived fractional flow reserve (FFRCT) ≤ 0.80 and plaque progression improved the prediction of events over current risk factors. • Major adverse cardiovascular events (MACE) significantly increased with the presence of plaque progression at follow-up stratified by the FFRCT change group.

Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.

OBJECTIVES: This study investigated the impact of machine learning (ML)-based fractional flow reserve derived from computed tomography (FFRCT) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD). METHODS: One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFRCT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFRCT and severe stenosis on qualitative CCTA and ICA were also evaluated. RESULTS: After FFRCT results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4-48 months), FFRCT ≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11); p < 0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73), p = 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14, p = 0.045). Reserving ICA and revascularization for vessels with positive FFRCT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions. CONCLUSIONS: This study indicated ML-based FFRCT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFRCT may direct therapeutic decision-making with the potential to improve efficiency of ICA. KEY POINTS: • ML-based FFRCT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA. • FFRCT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD. • Reserving ICA and revascularization for vessels with positive FFRCT may reduce the normalcy rate of ICA and improve its efficiency.

Review of Clinical Applications for Virtual Monoenergetic Dual-Energy CT.

In this article, the authors discuss the technical background and summarize the current body of literature regarding virtual monoenergetic (VM) images derived from dual-energy CT data, which can be reconstructed between 40 and 200 keV. Substantially improved iodine attenuation at lower kiloelectron volt levels and reduced beam-hardening artifacts at higher kiloelectron volt levels have been demonstrated from all major manufacturers of dual-energy CT units. Improved contrast attenuation with VM imaging at lower kiloelectron volt levels enables better delineation and diagnostic accuracy in the detection of various vascular or oncologic abnormalities. Low-kiloelectron-volt VM imaging may be useful for salvaging CT studies with suboptimal contrast material delivery or providing additional information on the arterial vasculature obtained from venous phase acquisitions. For patients with renal impairment, substantial reductions in the use of iodinated contrast material can be achieved by using lower-energy VM imaging. The authors recommend routine reconstruction of VM images at 50 keV when using dual-energy CT to exploit the increased contrast properties. For reduction of beam-hardening artifacts, VM imaging at 120 keV is useful for the initial assessment.

Hybrid-stenting with metallic and bioresorbable drug-eluting stents 2-year clinical outcomes in KUM ABSORB registry.

AIM AND OBJECTIVE: We sought to investigate and compare outcomes 2 years after Hybrid-stenting with bioresorbable vascular scaffolds (BVS) and contemporary metallic drug-eluting stents (DES) within the same coronary lesion versus BVS alone. METHODS: Between 11/2012 and 7/2015 at our institution, 134 (33.2%) were treated with Hybrid-stenting for complex or long coronary lesions, 270 patients were treated by BVS alone. The primary outcome of interest was target lesion failure (TLF) at 2-years of follow-up. RESULTS: Patients treated by Hybrid-stenting were more frequently men (80% vs. 70%, p = 0.04) had extensive multivessel disease (84% vs. 71%, p < 0.01) including more complex (89% vs. 52%, p < 0.01) and longer lesions (28.9 mm vs 16.4 ± mm, p < 0.01) resulting in longer treated segments (47.3 mm vs 21.5 mm, p < 0.01) and more residual in-segment stenosis (12.3% vs 8.5%, p < 0.01) compared to BVS alone patients. At 2 years, cumulative incidence of TLF was 9.7% of Hybrid-stenting patients and 11.5% of BVS alone patients (p = 0.62), myocardial infarction (3.0% vs 4.1%, p = 0.59) and mortality (1.5% vs 4.1%, p = 0.17), respectively. Target lesion revascularization occurred in 9 Hybrid-stenting patients (2 located in DES) and in 20 BVS alone patients, cumulative incidence 6.7% vs. 7.4% (p = 0.80). Chronic kidney disease and residual in-segment stenosis >30% were identified as independent predictors of TLF at 2-years. CONCLUSION: Despite differences in clinical and angiographic profile, Hybrid-stenting performed similar to BVS alone at 2 years after percutaneous coronary intervention.

Fractional flow reserve derived from CCTA may have a prognostic role in myocardial bridging.

PURPOSE: To evaluate the feasibility of fractional flow reserve (cFFR) derivation from coronary CT angiography (CCTA) in patients with myocardial bridging (MB), its relationship with MB anatomical features, and clinical relevance. METHODS: This retrospective study included 120 patients with MB of the left anterior descending artery (LAD) and 41 controls. MB location, length, depth, muscle index, instance, and stenosis rate were measured. cFFR values were compared between superficial MB (≤ 2 mm), deep MB (> 2 mm), and control groups. Factors associated with abnormal cFFR values (≤ 0.80) were analyzed. RESULTS: MB patients demonstrated lower cFFR values in MB and distal segments than controls (all p < 0.05). A significant cFFR difference was only found in the MB segment during systole between superficial (0.94, 0.90-0.96) and deep MB (0.91, 0.83-0.95) (p = 0.018). Abnormal cFFR values were found in 69 (57.5%) MB patients (29 [49.2%] superficial vs. 40 [65.6%] deep; p = 0.069). MB length (OR = 1.06, 95% CI 1.03-1.10; p = 0.001) and systolic stenosis (OR = 1.04, 95% CI 1.01-1.07; p = 0.021) were the main predictors for abnormal cFFR, with an area under the curve of 0.774 (95% CI 0.689-0.858; p < 0.001). MB patients with abnormal cFFR reported more typical angina (18.8% vs 3.9%, p = 0.023) than patients with normal values. CONCLUSION: MB patients showed lower cFFR values than controls. Abnormal cFFR values have a positive association with symptoms of typical angina. MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value. KEY POINTS: • MB patients showed lower cFFR values than controls. • Abnormal cFFR values have a positive association with typical angina symptoms. • MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value .

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. METHODS: Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard. RESULTS: One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93). CONCLUSION: Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power. KEY POINTS: • Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.

OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFRML). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFRML. Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFRML. MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFRML. CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFRML in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFRML in relation to the invasively measured FFR value was ρ = 0.684, ρ = 0.622, and ρ = 0.669, respectively (p < 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p < 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p < 0.001 for all). CONCLUSION. CCTA-derived cFFRML is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

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.

Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling.

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

Coronary artery assessment using self-navigated free-breathing radial whole-heart magnetic resonance angiography in patients with congenital heart disease.

OBJECTIVES: To evaluate a self-navigated free-breathing three-dimensional (SNFB3D) radial whole-heart MRA technique for assessment of main coronary arteries (CAs) and side branches in patients with congenital heart disease (CHD). METHODS: SNFB3D-MRA datasets of 109 patients (20.1±11.8 years) were included. Three readers assessed the depiction of CA segments, diagnostic confidence in determining CA dominance, overall image quality and the ability to freeze cardiac and respiratory motion. Vessel sharpness was quantitatively measured. RESULTS: The percentages of cases with excellent CA depiction were as follows (mean score): left main, 92.6 % (1.92); left anterior descending (LAD), 88.3 % (1.88); right (RCA), 87.8 % (1.85); left circumflex, 82.8 % (1.82); posterior descending, 50.2 % (1.50) and first diagonal, 39.8 % (1.39). High diagnostic confidence for the assessment of CA dominance was achieved in 56.2 % of MRA examinations (mean score, 1.56). Cardiac motion freezing (mean score, 2.18; Pearson's r=0.73, P<0.029) affected image quality more than respiratory motion freezing (mean score, 2.20; r=0.58, P<0.029). Mean quantitative vessel sharpness of the internal thoracic artery, RCA and LAD were 53.1, 52.5 and 48.7 %, respectively. CONCLUSIONS: Most SNFB3D-MRA examinations allow for excellent depiction of the main CAs in young CHD patients; visualisation of side branches remains limited. KEY POINTS: • Self-navigated free-breathing three-dimensional magnetic resonance angiography (SNFB3D-MRA) sufficiently visualises coronary arteries (CAs). • Depiction of main CAs in patients with congenital heart disease is excellent. • Visualisation of CA side branches using SNFB3D-MRA is limited. • SNFB3D-MRA image quality is especially correlated to cardiac motion freezing ability.

High-pitch low-voltage CT coronary artery calcium scoring with tin filtration: accuracy and radiation dose reduction.

OBJECTIVES: To investigate diagnostic accuracy and radiation dose of high-pitch CT coronary artery calcium scoring (CACS) with tin filtration (Sn100kVp) versus standard 120kVp high-pitch acquisition. METHODS: 78 patients (58% male, 61.5±9.1 years) were prospectively enrolled. Subjects underwent clinical 120kVp high-pitch CACS using third-generation dual-source CT followed by additional high-pitch Sn100kVp acquisition. Agatston scores, calcium volume scores, Agatston score categories, percentile-based risk categorization and radiation metrics were compared. RESULTS: 61/78 patients showed coronary calcifications. Median Agatston scores were 34.9 [0.7-197.1] and 41.7 [0.7-207.2] and calcium volume scores were 34.1 [0.7-218.0] for Sn100kVp and 35.7 [1.1-221.0] for 120kVp acquisitions, respectively (both p<0.0001). Bland-Altman analysis revealed underestimated Agatston scores and calcium volume scores with Sn100kVp versus 120kVp acquisitions (mean difference: 16.4 and 11.5). However, Agatston score categories and percentile-based risk categories showed excellent agreement (ĸ=0.98 and ĸ=0.99). Image noise was 25.8±4.4HU and 16.6±2.9HU in Sn100kVp and 120kVp scans, respectively (p<0.0001). Dose-length-product was 9.9±4.8mGy*cm and 40.9±14.4mGy*cm with Sn100kVp and 120kVp scans, respectively (p<0.0001). This resulted in significant effective radiation dose reduction (0.13±0.07mSv vs. 0.57±0.2mSv, p<0.0001) for Sn100kVp acquisitions. CONCLUSION: CACS using high-pitch low-voltage tin-filtered acquisitions demonstrates excellent agreement in Agatston score and percentile-based cardiac risk categorization with standard 120kVp high-pitch acquisitions. Furthermore, radiation dose was significantly reduced by 78% while maintaining accurate risk prediction. KEY POINTS: • Coronary artery calcium scoring with tin filtration reduces radiation dose by 78%. • There is excellent correlation between high-pitch Sn100kVp and standard 120kVp acquisitions. • Excellent agreement regarding Agatston score categories and percentile-based risk categorization was achieved. • No cardiac risk reclassifications were observed using Sn100kVp coronary artery calcium scoring.

Diagnostic accuracy of low and high tube voltage coronary CT angiography using an X-ray tube potential-tailored contrast medium injection protocol.

OBJECTIVES: To compare the diagnostic accuracy between low-kilovolt peak (kVp) (≤ 100) and high-kVp (> 100) third-generation dual-source coronary CT angiography (CCTA) using a kVp-tailored contrast media injection protocol. METHODS: One hundred twenty patients (mean age = 62.6 years, BMI = 29.0 kg/m2) who underwent catheter angiography and CCTA with automated kVp selection were separated into two cohorts (each n = 60, mean kVp = 84 and 117). Contrast media dose was tailored to the kVp level: 70 = 40 ml, 80 = 50 ml, 90 = 60 ml, 100 = 70 ml, 110 = 80 ml, and 120 = 90 ml. Contrast-to-noise ratio (CNR) was measured. Two observers evaluated image quality and the presence of significant coronary stenosis (> 50% luminal narrowing). RESULTS: Diagnostic accuracy (sensitivity/specificity) with ≤ 100 vs. > 100 kVp CCTA was comparable: per patient = 93.9/92.6% vs. 90.9/92.6%, per vessel = 91.5/97.8% vs. 94.0/96.8%, and per segment = 90.0/96.7% vs. 90.7/95.2% (all P > 0.64). CNR was similar (P > 0.18) in the low-kVp vs. high-kVp group (12.0 vs. 11.1), as ws subjective image quality (P = 0.38). Contrast media requirements were reduced by 38.1% in the low- vs. high-kVp cohort (53.6 vs. 86.6 ml, P < 0.001) and radiation dose by 59.6% (4.3 vs. 10.6 mSv, P < 0.001). CONCLUSIONS: Automated tube voltage selection with a tailored contrast media injection protocol allows CCTA to be performed at ≤ 100 kVp with substantial dose reductions and equivalent diagnostic accuracy for coronary stenosis detection compared to acquisitions at > 100 kVp. KEY POINTS: • Low-kVp coronary CT angiography (CCTA) enables reduced contrast and radiation dose. • Diagnostic accuracy is comparable between ≤ 100 and > 100 kVp CCTA. • Image quality is similar for low- and high-kVp CCTA. • Low-kVp image acquisition is facilitated by automated tube voltage selection. • Tailoring contrast injection protocols to the automatically selected kVp-level is feasible.

Coronary CT Angiography-derived Fractional Flow Reserve.

Invasive coronary angiography (ICA) with measurement of fractional flow reserve (FFR) by means of a pressure wire technique is the established reference standard for the functional assessment of coronary artery disease (CAD) ( 1 , 2 ). Coronary computed tomographic (CT) angiography has emerged as a noninvasive method for direct assessment of CAD and plaque characterization with high diagnostic accuracy compared with ICA ( 3 , 4 ). However, the solely anatomic assessment provided with both coronary CT angiography and ICA has poor discriminatory power for ischemia-inducing lesions. FFR derived from standard coronary CT angiography (FFRCT) data sets by using any of several advanced computational analytic approaches enables combined anatomic and hemodynamic assessment of a coronary lesion by a single noninvasive test. Current technical approaches to the calculation of FFRCT include algorithms based on full- and reduced-order computational fluid dynamic modeling, as well as artificial intelligence deep machine learning ( 5 , 6 ). A growing body of evidence has validated the diagnostic accuracy of FFRCT techniques compared with invasive FFR. Improved therapeutic guidance has been demonstrated, showing the potential of FFRCT to streamline and rationalize the care of patients suspected of having CAD and improve outcomes while reducing overall health care costs ( 7 , 8 ). The purpose of this review is to describe the scientific principles, clinical validation, and implementation of various FFRCT approaches, their precursors, and related imaging tests. © RSNA, 2017.

Diagnostic accuracy of coronary CT angiography using 3rd-generation dual-source CT and automated tube voltage selection: Clinical application in a non-obese and obese patient population.

PURPOSE: To investigate diagnostic accuracy of 3rd-generation dual-source CT (DSCT) coronary angiography in obese and non-obese patients. METHODS: We retrospectively analyzed 76 patients who underwent coronary CT angiography (CCTA) and invasive coronary angiography. Prospectively ECG-triggered acquisition was performed with automated tube voltage selection (ATVS). Patients were dichotomized based on body mass index in groups A (<30 kg/m2, n = 37) and B (≥30 kg/m2, n = 39) and based on tube voltage in groups C (<120 kV, n = 46) and D (120 kV, n = 30). Coronary arteries were assessed for significant stenoses (≥50 % luminal narrowing) and diagnostic accuracy was calculated. RESULTS: Per-patient overall sensitivity, specificity, positive predictive value, negative predictive value (NPV) and accuracy were 96.9 %, 95.5 %, 93.9 %, 97.7 % and 96.1 %, respectively. Sensitivity and NPV were lower in groups B and D compared to groups A and C, but no statistically significant differences were observed (group A vs. B: sensitivity, 100.0 % vs. 93.3 %, p = 0.9493; NPV, 100 % vs. 95.5 %, p = 0.9812; group C vs. D: sensitivity, 100.0 % vs. 92.3 %, p = 0.8462; NPV, 100.0 % vs. 94.1 %, p = 0.8285). CONCLUSION: CCTA using 3rd-generation DSCT and (ATVS) provides high diagnostic accuracy in both non-obese and obese patients. KEY POINTS: • Coronary CTA provides high diagnostic accuracy in non-obese and obese patients. • Diagnostic accuracy between obese and non-obese patients showed no significant difference. • <120 kV studies were performed in 44 % of obese patients. • Current radiation dose-saving approaches can be applied independent of body habitus.