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Contains fulltext : 290911.pdf (Publisher’s version ) (Open Access) OBJECTIVE: The aim of this study is to evaluate the diagnostic and prognostic value of non-hyperaemic Pd/Pa and to determine its additional value when combined with the gold standard hyperaemic pressure ratio (FFR) to guide revascularization. METHODS: In a large, multi-center, retrospective registry, we included a total of 2141 patients with a clinical indication for coronary angiography providing physiological data in 2726 vessels. A classification was made based on the FFR (cut-off value: 0.80) and non-hyperaemic Pd/Pa (cut-off value: 0.92) values and the primary outcome was target-vessel failure (TVF) at 5-year follow-up. RESULTS: Mean age was 63 ± 10.0 and 75% of the study population were men. Regression analysis showed an overall good correlation between FFR and non-hyperaemic Pd/Pa (r = 0.73, p

Background We present an automatic method for coronary artery calcium (CAC) quantification and cardiovascular risk categorization in CT attenuation correction (CTAC) scans acquired at rest and stress during cardiac PET/CT. The method segments CAC according to visual assessment rather than the commonly used CT-number threshold. Methods The method decomposes an image containing CAC into a synthetic image without CAC and an image showing only CAC. Extensive evaluation was performed in a set of 98 patients, each having rest and stress CTAC scans and a dedicated calcium scoring CT (CSCT). Standard manual calcium scoring in CSCT provided the reference standard. Results The interscan reproducibility of CAC quantification computed as average absolute relative differences between CTAC and CSCT scan pairs was 75% and 85% at rest and stress using the automatic method compared to 121% and 114% using clinical calcium scoring. Agreement between automatic risk assessment in CTAC and clinical risk categorization in CSCT resulted in linearly weighted kappa of 0.65 compared to 0.40 between CTAC and CSCT using clinically used calcium scoring. Conclusion The increased interscan reproducibility achieved by our method may allow routine cardiovascular risk assessment in CTAC, potentially relieving the need for dedicated CSCT.

The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR).Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations.Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination.Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of 70%); however, 41 (66.1%) of these had an FFR of 0.8.A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Outcomes of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) have improved because of advancements in equipment and techniques. With global collaboration and knowledge sharing, we have identified 7 common principles that are widely accepted as best practices for CTO-PCI. 1. Ischemic symptom improvement is the primary indication for CTO-PCI. 2. Dual coronary angiography and in-depth and structured review of the angiogram (and, if available, coronary computed tomography angiography) are key for planning and safely performing CTO-PCI. 3. Use of a microcatheter is essential for optimal guidewire manipulation and exchanges. 4. Antegrade wiring, antegrade dissection and reentry, and the retrograde approach are all complementary and necessary crossing strategies. Antegrade wiring is the most common initial technique, whereas retrograde and antegrade dissection and reentry are often required for more complex CTOs. 5. If the initially selected crossing strategy fails, efficient change to an alternative crossing technique increases the likelihood of eventual PCI success, shortens procedure time, and lowers radiation and contrast use. 6. Specific CTO-PCI expertise and volume and the availability of specialized equipment will increase the likelihood of crossing success and facilitate prevention and management of complications, such as perforation. 7. Meticulous attention to lesion preparation and stenting technique, often requiring intracoronary imaging, is required to ensure optimum stent expansion and minimize the risk of short- and long-term adverse events. These principles have been widely adopted by experienced CTO-PCI operators and centers currently achieving high success and acceptable complication rates. Outcomes are less optimal at less experienced centers, highlighting the need for broader adoption of the aforementioned 7 guiding principles along with the development of additional simple and safe CTO crossing and revascularization strategies through ongoing research, education, and training.

Item does not contain fulltext OBJECTIVES: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping. BACKGROUND: Damping of aortic pressure during coronary angiography must be identified to avoid serious complications and make accurate coronary physiology measurements. There are currently no automated methods to do this, and so identification of damping requires constant monitoring, which is prone to human error. METHODS: The neural network was trained and tested versus core laboratory expert opinions derived from 2 separate datasets. A total of 5,709 aortic pressure waveforms of individual heart beats were extracted and classified. The study developed a recurrent convolutional neural network to classify beats as either normal, showing damping, or artifactual. Accuracies were reported using the opinions of 2 independent core laboratories. RESULTS: The neural network was 99.4% accurate (95% confidence interval: 98.8% to 99.6%) at classifying beats from the testing dataset when judged against the opinions of the internal core laboratory. It was 98.7% accurate (95% confidence interval: 98.0% to 99.2%) when judged against the opinions of an external core laboratory not involved in neural network training. The neural network was 100% sensitive, with no beats classified as damped misclassified, with a specificity of 99.8%. The positive predictive and negative predictive values were 98.1% and 99.5%. The 2 core laboratories agreed more closely with the neural network than with each other. CONCLUSIONS: Arterial waveform analysis using neural networks allows rapid and accurate identification of damping. This demonstrates how machine learning can assist with patient safety and the quality control of procedures.

Left bundle branch block (LBBB) is related to abnormal cardiac conduction and mechanical asynchrony and is associated with hypertension and coronary artery disease. Improved evaluation of left ventricular (LV) mechanical asynchrony is needed, because of the increasing number of patients with LBBB and heart failure. In this paper, we describe tissue Doppler imaging (TDI), strain (rate) imaging and tissue tracking in LBBB patients. A variety of patterns of mechanical activation can be observed in LBBB patients. A recent development, referred to as tissue synchronisation imaging, colour codes TDI time-to-peak systolic velocities of segments and displays mechanical asynchrony. Furthermore, real-time 3D echocardiography provides new regional information about mechanical asynchrony. Contained in an LV model and projected on a bull's eye plot, this modality helps to display the spatial distribution of mechanical asynchrony. Finally, segmental time-to-peak circumferential strain curves, produced by cardiac magnetic resonance imaging, provide additional quantification of LV mechanical asynchrony. Effects of LBBB on regional and global cardiac function are impressive, myocardial involvement seems to play a role and with the help of these novel imaging modalities, new insights continue to develop.

During left ventricular (LV) torsion, the base rotates in an overall clockwise direction and the apex rotates in a counterclockwise direction when viewed from apex to base. LV torsion is followed by rapid untwisting, which contributes to ventricular filling. Because LV torsion is directly related to fiber orientation, it might depict subclinical abnormalities in heart function. Recently, ultrasound speckle tracking was introduced for quantification of LV torsion. This fast, widely available technique may contribute to a more rapid introduction of LV torsion as a clinical tool for detection of myocardial dysfunction. However, knowledge of the exact function and structure of the heart is fundamental for understanding the value of LV torsion. LV torsion has been investigated with different measurement methods during the past 2 decades, using cardiac magnetic resonance as the gold standard. The results obtained over the years are helpful for developing a standardized method to quantify LV torsion and have facilitated the interpretation and value of LV torsion before it can be used as a clinical tool