CT-derived Fractional Flow Reserve Research Articles

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% CI 0.774 - 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using FFR and FAI, could significantly improve the prediction of MACE in CAD patients, thereby potentially aiding clinical decision-making.

Triglyceride glucose index is associated with functional coronary artery stenosis in hypertensive patients.

The triglyceride glucose (TyG) index is an effective method for determining insulin resistance (IR). Limited research has explored the connection between the TyG index and functionally significant stenosis in hypertensive patients. Furthermore, the connections between the TyG index, fat attenuation index (FAI) and atherosclerotic plaque characteristics are also worth exploring. The study screened 1622 hypertensive participants without coronary artery disease history who underwent coronary computed tomography angiography. The TyG index was calculated as ln (fasting glucose [mg/dL] * fasting TG [mg/dL]/2). Adverse plaque characteristics (HRPCs), high-risk plaques (HRPs), FAI, and CT-derived fractional flow reserve (FFRCT) were analyzed and measured for all patients. Functionally significant stenosis causing ischemia is defined as FFRCT ≤ 0.80. Two patient groups were created based on the FFRCT: the FFRCT < 0.80 group and the FFRCT > 0.80 group. In hypertensive patients, the association between the TyG index and FFRCT was examined applying a logistic regression model. The TyG index was higher for people with FFRCT ≤ 0.80 contrast to those with FFRCT > 0.80. After controlling for additional confounding factors, the logistic regression model revealed a clear connection between the TyG index and FFRCT ≤ 0.80 (OR = 1.718, 95% CI 1.097-2.690, p = 0.018). The restricted cubic spline analysis displayed a nonlinear connection between the TyG index and FFRCT ≤ 0.80 (p for nonlinear = 0.001). The TyG index increased the fraction of individuals with HRPs and HRPCs, FAI raised, and FFRCT decreased (p < 0.05). The multivariate linear regression analysis illustrated a powerfulcorrelation between high TyG index levels and FAI, FFRCT, positive remodeling (PR), and low-attenuation plaque (LAPs) (standardized regression coefficients: 0.029 [p = 0.007], -0.051 [p < 0.001], 0.029 [p = 0.027], and 0.026 [p = 0.046], separately). In hypertensive patients, the TyG index showed an excellent association with a risk of FFRCT ≤ 0.80. Additionally, the TyG index was also linked to FAI, FFRCT, PR, and LAPs.

Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus

BackgroundCoronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS). ObjectivesTo explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM. Methods469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the independent predictors. The predictive performance was evaluated with area under receiver operating characteristic curve (AUC). ResultsDL-CACS (adjusted odds ratio (OR): 1.005; 95% CI: 1.003–1.006; P ＜ 0.001) was significantly associated with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002–1.004; P ＜ 0.001) was also an independent predictor for hemodynamically significant CAD. The AUCs, sensitivities, specificities, positive predictive values and negative predictive values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 0.753 (95% CI: 0.712–0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 0.728–0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to perform automated measurement of DL-CACS in total, which was significantly less than manual measurement of 1.73 min (P ＜ 0.001). ConclusionsDL-CACS, with less time-consuming, can accurately and effectively predict obstructive CAD and hemodynamically significant CAD in T2DM.

The Correlation between Region-specific Epicardial Adipose Tissue and Myocardial Ischemia Defined by CT-FFR in Type 2 Diabetes Mellitus Patients.

Epicardial Adipose Tissue (EAT) accumulation is closely associated with the presence and severity of coronary artery disease (CAD), myocardial ischemia, plaque vulnerability, and major adverse cardiovascular events. The aim of this study was to investigate the correlation between myocardial ischemia defined by computed tomography-derived fractional flow reserve (CT-FFR) and region-specific EAT in patients with type 2 diabetes mellitus (T2DM). Between January 2022 and May 2023, 200 T2DM patients were randomly selected from the Department of Endocrinology in The Central Hospital of Wuhan. These patients were divided into two groups based on myocardial ischemia defined by CT-FFR: myocardial ischemia group (152 cases) and control group (48 cases). Both groups of patients used a post-treatment workstation to measure the thickness of region-specific EAT. Receiver operating characteristic (ROC) curve analysis and binary logistic regression were used to evaluate the correlation between various parameters and myocardial ischemia. Patients in the myocardial ischemia group had significantly higher values of age, male gender, systolic blood pressure, total cholesterol, triglycerides, LDL, HDL, fasting blood glucose, fasting insulin, HOMA-IR, EAT thickness in right ventricular wall, left atrioventricular groove, and superior and inferior interventricular groove. ROC curve analysis results showed that EAT thickness in the left atrioventricular groove had the largest area under the ROC curve for diagnosing myocardial ischemia (0.837 [95% CI 0.766-0.865]; P < 0.001). Binary logistic regression analysis showed that EAT thickness in the left atrioventricular groove was an independent risk factor for myocardial ischemia in patients with T2DM (P < 0.05). The EAT thickness in the left atrioventricular groove is an independent risk factor for myocardial ischemia in patients with T2DM. Adipose tissue in the left atrioventricular groove region plays a major role in EAT-mediated CAD.

Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.

To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. The study protocol was completed in 110 participants (mean age, 62years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

CT-derived fractional flow reserve in intracranial arterial stenosis: A pilot study based on computational fluid dynamics

BackgroundCT-derived fractional flow reserve (CT-FFR) has been widely applied in coronary hemodynamic assessment. However, the feasieablity and standardization measurement in intracranial artery stenosis (ICAS) remains to be defined. PurposeTo demonstrate the feasibility of CT-FFR in ICAS functional assessment and explore the optimal CT-FFR measurement position with invasive FFR as reference standard. Materials and methodsNineteen patients (mean age, 58.6 years ± 1.9 [SD]; 13 men) with moderate to severe (≥50 %) ICAS undergoing guidewire-based pressure measurement and preoperative head CT angiography (CTA) were retrospectively enrolled. CT-FFR was measured in the following standard measurement positions, including the end of stenosis (D0), 1 cm distal to the stenosis (D1) and 2 cm distal to the stenosis (D2). Diagnostic performance of CT-FFR was assessed by the area under the curve (AUC) of receiver operating characteristic curves by assuming invasive FFR ≤ 0.80 or 0.75 as hemodynamically significant stenosis. ResultsExcellent intra- and inter-observer agreement (ICC range, 0.930–0.992) was observed for CT-FFR measurement in different positions. Under different FFR thresholds, the diagnostic performance of CT-FFRD1 showed perfect prediction with AUC values of 1.000 (95 % CI: 0.824, 1.000). The sensitivity, specificity and AUC of CT-FFRD1 ≤ 0.80 in detecting FFR ≤ 0.80 was 0.94 (95 % CI: 0.68, 1.00), 1.00 (95 % CI: 0.31, 1.00) and 0.969 (95 % CI: 0.772, 1.000), respectively. Similar performance of CT-FFRD1 ≤ 0.75 was obtained for identifying FFR ≤ 0.75 with the AUC of 0.964. The strongest correlation (r = 0.915, p < 0.001) and agreement (mean difference: 0.02, 95 % limits of agreement: −0.16 to 0.19) were observed between CT-FFRD1 and FFR. ConclusionCerebral CT-derived fractional flow reserve (CT-FFR) measured 1 cm distal to stenosis achieved the most comparable results with invasive FFR, which indicated its potentially promising clinical application for evaluating the functional relevance of intracranial artery stenosis.

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment.

Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis.

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches. The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data. Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86vs. 0.84), specificity (Sp; 0.90vs. 0.78), positive predictive value (PPV; 0.83vs. 0.70), and negative predictive value (NPV; 0.91vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94vs. 0.87). Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches.

Advanced CT measures of coronary artery disease with intermediate stenosis in patients with severe aortic valve stenosis.

Coronary artery disease (CAD) and severe aortic valve stenosis (AS) frequently coexist. While pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) allows to rule out obstructive CAD, interpreting hemodynamic significance of intermediate stenoses is challenging. This study investigates the incremental value of CT-derived fractional flow reserve (CT-FFR), quantitative coronary plaque characteristics (e.g., stenosis degree, plaque volume, and composition), and peri-coronary adipose tissue (PCAT) density to detect hemodynamically significant lesions among those with AS and CAD. We included patients with severe AS and intermediate coronary lesions (20-80% diameter stenosis) who underwent pre-TAVR CTA and invasive coronary angiogram (ICA) with resting full-cycle ratio (RFR) assessment between 08/16 and 04/22. CTA image analysis included assessment of CT-FFR, quantitative coronary plaque analysis, and PCAT density. Coronary lesions with RFR ≤ 0.89 indicated hemodynamic significance as reference standard. Overall, 87 patients (age 77.9 ± 7.4 years, 38% female) with 95 intermediate coronary artery lesions were included. CT-FFR showed good discriminatory capacity (area under receiver operator curve (AUC) = 0.89, 95% confidence interval (CI) 0.81-0.96, p < 0.001) to identify hemodynamically significant lesions, superior to anatomical assessment, plaque morphology, and PCAT density. Plaque composition and PCAT density did not differ between lesions with and without hemodynamic significance. Univariable and multivariable analyses revealed CT-FFR as the only predictor for functionally significant lesions (odds ratio 1.28 (95% CI 1.17-1.43), p < 0.001). Overall, CT-FFR ≤ 0.80 showed diagnostic accuracy, sensitivity, and specificity of 88.4% (95%CI 80.2-94.1), 78.5% (95%CI 63.2-89.7), and 96.2% (95%CI 87.0-99.5), respectively. CT-FFR was superior to CT anatomical, plaque morphology, and PCAT assessment to detect functionally significant stenoses in patients with severe AS. CT-derived fractional flow reserve in patients with severe aortic valve stenosis may be a useful tool for non-invasive hemodynamic assessment of intermediate coronary lesions, while CT anatomical, plaque morphology, and peri-coronary adipose tissue assessment have no incremental or additional benefit. These findings might help to reduce pre-transcatheter aortic valve replacement invasive coronary angiogram. • Interpreting the hemodynamic significance of intermediate coronary stenoses is challenging in pre-transcatheter aortic valve replacement CT. • CT-derived fractional flow reserve (CT-FFR) has a good discriminatory capacity in the identification of hemodynamically significant coronary lesions. • CT-derived anatomical, plaque morphology, and peri-coronary adipose tissue assessment did not improve the diagnostic capability of CT-FFR in the hemodynamic assessment of intermediate coronary stenoses.

Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications.

To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors. Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR. Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined. CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was -0.05(-0.12-0.02) (p < 0.001). Differences showed an inverse correlation to the absolute CT-FFR values. Categorization into CAD was different in 37/214 examinations, resulting in net recategorization of Δ13 (13/214) examinations and a difference in accuracy of Δ6.1%. On patient level, correlation of absolute and categorized values was substantial (0.567 and 0.570, p < 0.001). Categorization into CAD showed no correlation to image quality or CAC (p > 0.13). Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.

Prognostic value of CT-derived fractional flow reserve and fat attenuation index in patients with suspected coronary artery disease: a sex-disaggregated analyses

BackgroundThere are sex differences in many risk factors associated with coronary artery disease (CAD). CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI) have been shown to independently predict cardiovascular events. We aimed to examine the impact of sex on the prognostic value of CT-FFR and FAI in suspected CAD patients, and to examine the incremental prognostic value of FAI over CT-FFR in both sex.MethodsA total of 1334 consecutive suspected CAD subjects who underwent coronary computed tomographic angiography (CCTA) were retrospectively collected. We divided the patients into males and females and calculated CT-FFR and FAI data from CCTA images. Kaplan-Meier analysis was used to assess the risk of major adverse cardiovascular events (MACE) stratified by CT-FFR and FAI in both sex. Cox regression models were used to assess the incremental prognostic value of FAI by adding the variable to a model that included CT-FFR and clinical variables.ResultsDuring a median follow-up of 2.08 years, 212 patients had MACE. CT-FFR ≤ 0.80 was significantly associated with MACE in both sex. FAI value of left anterior descending artery (FAI[LAD]) and FAI value of left circumflex (FAI[LCX]) ≥ 70.1 were significantly associated with MACE in females. FAI[LCX] added incremental prognostic value over clinical and CT-FFR variables in females, with hazard ratio (HR) 3.230 (1.982–5.265, P = 0.000), Harrel’s C 0.669 (P < 0.001), net reclassification improvement (NRI) 0.161 (0.073–0.260, P < 0.001), and integrated discrimination index (IDI) 0.036 (0.008–0.090, P = 0.010). FAI[LAD] did not enhance risk prediction in females (Harrel’s C 0.643, P = 0.054; NRI 0.041, P = 0.189; IDI 0.005, P = 0.259). The decision curve analysis demonstrated that the model including FAI[LCX] resulted in the highest net benefit.ConclusionsIn suspected CAD patients, the prognostic value of CT-FFR is not significantly biased by sex. The prognostic value of FAI[LAD] and FAI[LCX] were significantly associated with MACE in females, but not males. FAI[LCX], not FAI[LAD], added incremental prognostic value over CT-FFR and might enhance CT-FFR risk stratification in females.

Effects of stenotic lesion morphology on trans-lesional computed tomography derived fractional flow reserve (FFRCT) changes

Abstract Background To assess the indication for revascularization CT-derived fractional flow reserve (FFRCT) has been measured at only a portion distal to the lesion. This conventional method is controversial in terms of its ability to accurately evaluate the coronary flow disturbance. Changes in FFRCT between the portions proximal and distal to the lesion (ΔFFRCT) are implemented as a novel method for the assessment of the coronary flow disturbance. The effect of different morphologies of stenotic lesions on ΔFFRCT remains unclear. Purpose To investigate the effect of lesion morphology on ΔFFRCT and the ability of ΔFFRCT to detect lesions with the coronary flow disturbance. Methods A total of 1502 outpatients with suspected stable coronary artery disease who underwent FFRCT between January 2017 and May 2022 were evaluated. Among them, 76 patients who underwent both FFRCT and invasive coronary angiography with ≥ 50% stenosis were evaluated and divided into four groups according to the degree of stenosis and lesion length (moderate, 50–69%; severe: 70–99%) and lesion length (focal, &amp;lt; 40 mm; diffuse, ≥ 40 mm) as follows: moderate/focal (n = 23), moderate/diffuse (n = 9), severe/focal (n = 34), and severe/diffuse (n = 10) (Figure 1). Vessel and lesion morphology (length, luminal volume, and plaque characteristics) and left ventricular mass were assessed. Indication for revascularization (percutaneous coronary intervention and coronary artery bypass graft) was assessed by invasive coronary angiography and additional invasive FFR measurement was performed when necessary. Results ΔFFRCT was greater in diffuse than in focal stenosis in both moderate and severe stenosis: moderate/focal (0.11 ± 0.12), moderate/diffuse (0.18 ± 0.10), severe/focal (0.26 ± 0.15), and severe/diffuse (0.38 ± 0.12) (Figure 2A). Lesion length correlated with ΔFFRCT (r = 0.31, p = 0.006) (Figure 2B). As for focal lesion, lesion length did not correlate with ΔFFRCT (r = 0.13, p = 0.34), whereas, as for diffuse lesion it did (r = 0.72, p = 0.0007) (Figure 2C). Multivariable analysis showed that lesion length was the strongest predictor of ΔFFRCT (β-coefficient = 0.41, p = 0.003), followed by lesion positive remodelling (β-coefficient = 0.27, p = 0.01), lesion calcified plaque volume (β-coefficient = －0.39, p = 0.02), and lesion spotty calcification (β-coefficient = 0.23, p = 0.04). ΔFFRCT predicted for lesion requiring revascularization (cut-off 0.21, AUC 0.80, sensitivity 64.4%, specificity 85.7%, 95% CI 0.69–0.91) (Figure 2D). Conclusions ΔFFRCT was affected by lesion length, followed by lesion calcium components. ΔFFRCT predicted lesions requiring revascularization.Figure 1Figure 2

Real world impact of added FFR-CT to coronary CT angiography on clinical decision-making and patient prognosis

Abstract Objectives The addition of CT-derived fractional flow reserve (FFR-CT) increases the diagnostic accuracy of Coronary CT Angiography (CCTA). We assessed the impact of FFR-CT in routine clinical practice on clinical decision-making and patient prognosis in patients suspected of stable coronary artery disease (CAD). Methods This retrospective, single-center study compared a cohort that received CCTA with FFR-CT to a historical cohort that received CCTA before FFR-CT was available. We assessed the clinical management decisions after FFR-CT and CCTA and the rate of major adverse cardiac events (MACE) during the one-year follow-up using chi-square tests for independence. Kaplan-Meier curves were used to visualize the occurrence of safety outcomes over time. Results 360 patients at low to intermediate risk of CAD were included, 224 in the CCTA only group and 136 in the FFR-CT group. During follow-up, 13 MACE occurred in 12 patients, 9 (4.0%) in the CCTA-group and three (2.2%) in the FFR-CT group. Clinical management decisions differed significantly between both groups. After CCTA, 60 patients (26.5%) received optimal medical therapy (OMT) only, 115 (51.3%) invasive coronary angiography (ICA) and 49 (21.9%) single positron emission CT (SPECT). After FFR-CT, 106 patients (77.9%) received OMT only, 27 (19.9%) ICA and three (2.2%) SPECT (p&amp;lt;0.001 for all three options). The revascularization rate after ICA was similar between groups (p=0.15). However, patients in the CCTA-group more often underwent revascularization (p=0.007). Conclusion Addition of FFR-CT to CCTA led to a reduction in (invasive) diagnostic testing and less revascularizations without observed difference in outcomes after one year.Clinical decisions and follow upSignificant LAD lesion, negative FFRCT

Clinical impacts of CT-derived fractional flow reserve under insurance reimbursement: Results from multicenter, prospective registry

BackgroundAlthough computed tomography-derived fractional flow reserve (FFRCT) has been reimbursed in a few countries, its impacts on daily practice of coronary artery diseases are not fully elucidated. We evaluated the clinical impacts of FFRCT under the real Japanese insurance reimbursement. MethodsIn the multicenter prospective registry: DYNAMIC-FFRCT study, a total of 410 patients who underwent FFRCT analysis under reimbursement were prospectively enrolled at 6 Japanese sites from October 2019 to November 2021. Coronary CT angiography and FFRCT findings, treatment plans, and 90-day outcomes were recorded. The primary endpoint was the redirection rate from the tests that might be expected without FFRCT [invasive coronary angiography (ICA)-selected group, myocardial perfusion single photon emission CT (MPS)-selected group, optimal medical therapy (OMT)-selected group, and others-selected group] to those that were actually performed based on FFRCT. ResultsICA could be avoided in 39.5 % in the ICA-selected group (N = 233). In particular, in 94.3 % of patients with an FFRCT value of >0.80, additional examinations, such as ICA, were avoided. In addition, in the MPS-selected group (N = 133), 92.6 % had no additional tests with FFRCT > 0.80, while only 2 cases with FFRCT ≤ 0.80 underwent additional MPS examination. On the contrary, 33.3 % of the OMT-selected group (N = 33) had FFRCT ≤ 0.80. Approximately, 35 % medical cost reduction was also finally expected. ConclusionIntroduction of FFRCT could not only reduce unnecessary ICA and be a test that replaces the conventional non-invasive functional assessment modality but also result in medical cost reduction even when used under real Japanese insurance reimbursement.

Low-attenuation coronary plaque burden and troponin release in chronic coronary syndrome: A mediation analysis

BackgroundCoronary low-attenuation plaque (LAP) burden is a strong predictor of myocardial infarction in patients with stable chest pain. We aimed to assess the relationship between LAP burden and circulating levels of high-sensitivity cardiac troponin T (hs-cTnT), and to explore the potential underlying etiology in patients undergoing clinically indicated coronary CT angiography (CCTA). MethodsA comprehensive metabolic and lipid panel, as well as C-reactive protein (CRP) and hs-cTnT tests were obtained from consecutive patients with stable chest pain at the time of CCTA. Qualitative and quantitative coronary plaque analysis, CT-derived fractional flow reserve (FFR) calculation, and pericoronary adipose tissue (PCAT) attenuation measurement around the right coronary artery were performed on CCTA images. Linear regression analyses were performed to identify independent associations with hs-cTnT concentration and mediation analysis was used to assess whether ischemia or markers of inflammation mediate hs-cTnT elevation. ResultsIn total, 114 patients (56.3 ​± ​10.6 years, 44.7 ​% female) were enrolled. In multivariable analysis, age (β ​= ​0.04 [95%CI: 0.02; 0.06], p ​< ​0.001), female sex (β ​= ​−0.77 [95%CI: −1.20; 0.33], p ​< ​0.001), and LAP burden (β ​= ​0.03 [95%CI: 0.001; 0.06], p ​= ​0.04) were independently associated with hs-cTnT levels. Mediation analysis, on the other hand, did not identify a significant mediating effect of lesion-specific ischemia based on CT-FFR, circulating CRP levels, or PCAT values between LAP burden and hs-cTnT levels (all p ​> ​0.05). ConclusionAlthough ischemia and inflammation have previously been proposed to mediate the association between LAP burden and hs-cTnT levels, our results did not confirm the role of these pathophysiological pathways in patients with stable chest pain.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

ObjectivesTo evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms. MethodsThe retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model. ResultsLow-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90–0.96], 0.86 [0.79–0.94], 0.88 [0.83–0.94], and 0.90 [0.84–0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94–0.99], 0.92 [0.86–0.99], 0.92 [0.86–0.99] and 0.94 [0.91–0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone. ConclusionsComprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

TCT-24 Assessing the Feasibility and Accuracy of an On-Site Prototype Workstation in Assessing CT-Derived Fractional Flow Reserve in Severe Aortic Stenosis

TCT-24 Assessing the Feasibility and Accuracy of an On-Site Prototype Workstation in Assessing CT-Derived Fractional Flow Reserve in Severe Aortic Stenosis

Cardiac Computed Tomography Angiography in CAD Risk Stratification and Revascularization Planning.

Functional stress testing is frequently used to assess for coronary artery disease (CAD) in symptomatic, stable patients with low to intermediate pretest probability. However, patients with highly vulnerable plaque may have preserved luminal patency and, consequently, a falsely negative stress test. Cardiac computed tomography angiography (CCTA) has emerged at the forefront of primary prevention screening and has excellent agency in ruling out obstructive CAD with high negative predictive value while simultaneously characterizing nonobstructive plaque for high-risk features, which invariably alters risk-stratification and pre-procedural decision making. We review the literature detailing the utility of CCTA in its ability to risk-stratify patients with CAD based on calcium scoring as well as high-risk phenotypic features and to qualify the functional significance of stenotic lesions. Calcium scores ≥ 100 should prompt consideration of statin and aspirin therapy. Spotty calcifications < 3 mm, increased non-calcified plaque > 4 mm3 per mm of the vessel wall, low attenuation < 30 HU soft plaque and necrotic core with a rim of higher attenuation < 130 HU, and a positive remodeling index ratio > 1.1 all confer additive risk for acute plaque rupture when present. Elevations in the perivascular fat attenuation index > -70.1 HU are a strong predictor of all-cause mortality and can further the risk stratification of patients in the setting of a non-to-minimal plaque burden. Lastly, a CT-derived fractional flow reserve (FFRCT) < 0.75 or values from 0.76 to 0.80 in conjunction with additional risk factors is suggestive of flow-limiting disease that would benefit from invasive testing. The wealth of information available through CCTA can allow clinicians to risk-stratify patients at elevated risk for an acute ischemic event and engage in advanced revascularization planning.

Precisión diagnóstica de la TC-RFF con un nuevo algoritmo de subpíxel grueso a fino en la detección de isquemia específica de lesión: un estudio multicéntrico prospectivo

Introduction and objectivesA new computed tomography-derived fractional flow reserve (CT-FFR) technique with a “coarse-to-fine subpixel” algorithm has been developed to generate precise lumen contours. The aim of this study was to assess the diagnostic performance of this new CT-FFR algorithm for discriminating lesion-specific ischemia using wire-based FFR ≤ 0.80 as the reference standard in patients with coronary artery disease. MethodsThis prospective, multicenter study screened 330 patients undergoing coronary CT angiography (CCTA) and invasive FFR (median interval 2 days) from 6 tertiary hospitals. CT-FFR was evaluated in a blinded fashion with a “coarse-to-fine subpixel” algorithm for lumen contour. ResultsBetween March 2019 and May 2020, we included 316 patients with 324 vessels. There was a good correlation between CT-FFR and invasive FFR (r=0.76, P<.001). The diagnostic sensitivity, specificity, and accuracy on a per-vessel level were 95.3%, 89.8%, and 92.0% for CT-FFR, and 96.4%, 26.4%, and 53.1% for CCTA>50% stenosis, respectively. CT-FFR showed improved discrimination of ischemia compared with CCTA alone overall (AUC, 0.95 vs 0.74, P<.001) and in intermediate (AUC, 0.96 vs 0.62, P<.001) and “gray zone” lesions (AUC, 0.88 vs 0.61, P<.001). The diagnostic specificity, accuracy, and AUC for CT-FFR (71.9%, 82.8%, and 0.84) outperformed CCTA (9.4%, 48.3%, and 0.66) in patients or in vessels with severe calcification (all P<.05). ConclusionsCT-FFR with a new “coarse-to-fine subpixel” algorithm showed high performance in identifying hemodynamically significant stenosis. The diagnostic performance of CT-FFR was superior to that of CCTA in intermediate lesions, “gray zone” lesions, and severely calcified lesions.Clinical Trial Register: NCT04731285Full English text available from:www.revespcardiol.org/en

Science mapping analysis of computed tomography-derived fractional flow reverse: a bibliometric review from 2012 to 2022.

Computed tomography-derived fractional flow reserve (CT-FFR) is a non-invasive imagological examination used for diagnosing suspected coronary atherosclerotic heart disease, providing the morphological and functional value on a three-dimensional (3D) coronary artery model. This article aimed to collate the existing knowledge and predict this novel technology's future research hotspots. To collect data, 1,712 articles were retrieved from the Web of Science Core Collection (WoSCC) database from 2012-2022. CiteSpace5.8.R3 was used to visually analyze the research status and predict future research hotspots. Firstly, the United States, China, and the Netherlands were identified as the countries having published the most articles about CT-FFR. Jonathan Leipsic's group ranked first for the highest number of published articles. Secondly, the visualized analysis indicated that the exploration of CT-FFR is multi-disciplinary and involves cardiology, radiology, engineering, and computer science. Thirdly, the hotspots in this field, which were inferred from the keyword distribution and clustering, included the following: "diagnostic performance", "accuracy", and the "prognostic value" of CT-FFR, and comparison of CT-FFR and other imaging methods sharing similarities. The research frontiers included technologies utilized to obtain more accurate CT-FFR values, such as artificial intelligence (AI) and deep learning. As the first visualized bibliometric analysis on CT-FFR, this study captured the current accumulated information in this field and offer more insight and guidance for future research.