Dobutamine stress echocardiography after positive CCTA: diagnostic performance using fractional flow reserve and instantaneous wave-free ratio as reference standards.

To assess the diagnostic accuracy of dobutamine stress echocardiography (DSE) in symptomatic patients with a low to intermediate pretest probability of obstructive coronary artery disease (CAD) and a positive coronary CT angiography (CCTA). We prospectively enrolled 104 consecutive patients undergoing coronary angiography for symptoms of stable CAD and a CCTA indicative of obstructive CAD. The diagnostic performance of DSE was evaluated against two intracoronary pressure indices: (a) fractional flow reserve (FFR) with a cut-off of ≤0.80 and (b) instantaneous wave-free ratio (iFR) with a cut-off of ≤0.89, indicating haemodynamically significant stenoses. Of 102 patients, 46 (45%) had at least one significant lesion as defined by FFR, as did 37 (36%) as defined by iFR. DSE showed positive results in 33% (34/102) of cases. The discriminative power of DSE for detecting significant CAD was moderate, with areas under the curve of 0.63 (p=0.024) compared with FFR and 0.64 (p=0.025) compared with iFR. The accuracy, sensitivity and specificity of DSE were, respectively, 61%, 43%, and 75% against FFR, and 64%, 46% and 74% against iFR. The diagnostic accuracy of DSE did not differ significantly between FFR and iFR as a reference (p=0.549). In patients with positive CCTA, DSE has a moderate ability to identify haemodynamically significant CAD, with low sensitivity and moderate specificity. When assessed against FFR and iFR criteria, its additive diagnostic value is limited in patients with low to intermediate pretest probability of obstructive CAD. NCT03045601.

Tjellaug Bråten A ，Holte E ，Wiseth R ，Aakhus S ... -  《Open Heart》

Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.

Peters B ，Paul JF ，Symons R ，Franssen WMA ，Nchimi A ，Ghekiere O ... -  《-》

Accuracy of subtraction fractional flow reserve with computed tomography in identifying early revascularization in patients with coronary artery disease.

Zhu T ，Li D ，Qiao J ，Li Q ，Xu Y ，Ge B ，Xia L ... -  《-》

Fractional Flow Reserve/Instantaneous Wave-Free Ratio Discordance in Angiographically Intermediate Coronary Stenoses: An Analysis Using Doppler-Derived Coronary Flow Measurements.

The study sought to determine the coronary flow characteristics of angiographically intermediate stenoses classified as discordant by fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR). Discordance between FFR and iFR occurs in up to 20% of cases. No comparisons have been reported between the coronary flow characteristics of FFR/iFR discordant and angiographically unobstructed vessels. Baseline and hyperemic coronary flow velocity and coronary flow reserve (CFR) were compared across 5 vessel groups: FFR+/iFR+ (108 vessels, n = 91), FFR-/iFR+ (28 vessels, n = 24), FFR+/iFR- (22 vessels, n = 22), FFR-/iFR- (208 vessels, n = 154), and an unobstructed vessel group (201 vessels, n = 153), in a post hoc analysis of the largest combined pressure and Doppler flow velocity registry (IDEAL [Iberian-Dutch-English] collaborators study). FFR disagreed with iFR in 14% (50 of 366). Baseline flow velocity was similar across all 5 vessel groups, including the unobstructed vessel group (p = 0.34 for variance). In FFR+/iFR- discordants, hyperemic flow velocity and CFR were similar to both FFR-/iFR- and unobstructed groups; 37.6 (interquartile range [IQR]: 26.1 to 50.4) cm/s vs. 40.0 [IQR: 29.7 to 52.3] cm/s and 42.2 [IQR: 33.8 to 53.2] cm/s and CFR 2.36 [IQR: 1.93 to 2.81] vs. 2.41 [IQR: 1.84 to 2.94] and 2.50 [IQR: 2.11 to 3.17], respectively (p > 0.05 for all). In FFR-/iFR+ discordants, hyperemic flow velocity, and CFR were similar to the FFR+/iFR+ group; 28.2 (IQR: 20.5 to 39.7) cm/s versus 23.5 (IQR: 16.4 to 34.9) cm/s and CFR 1.44 (IQR: 1.29 to 1.85) versus 1.39 (IQR: 1.06 to 1.88), respectively (p > 0.05 for all). FFR/iFR disagreement was explained by differences in hyperemic coronary flow velocity. Furthermore, coronary stenoses classified as FFR+/iFR- demonstrated similar coronary flow characteristics to angiographically unobstructed vessels.

Cook CM ，Jeremias A ，Petraco R ，Sen S ，Nijjer S ，Shun-Shin MJ ，Ahmad Y ，de Waard G ，van de Hoef T ，Echavarria-Pinto M ，van Lavieren M ，Al Lamee R ，Kikuta Y ，Shiono Y ，Buch A ，Meuwissen M ，Danad I ，Knaapen P ，Maehara A ，Koo BK ，Mintz GS ，Escaned J ，Stone GW ，Francis DP ，Mayet J ，Piek JJ ，van Royen N ，Davies JE ... -  《-》

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners. We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives. AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.

Kübler J ，Brendel JM ，Küstner T ，Walterspiel J ，Hagen F ，Paul JF ，Nikolaou K ，Gassenmaier S ，Tsiflikas I ，Burgstahler C ，Greulich S ，Winkelmann MT ，Krumm P ... -  《-》