Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making

Quantitative computed tomography angiography evaluation of the coronary fractional flow reserve in patients with left anterior descending artery myocardial bridging

PURPOSE

To quantitatively investigate the effect of myocardial bridge (MB) in the left anterior descending artery (LAD) on the fractional flow reserve (FFR).

MATERIALS AND METHODS

Three-hundred patients with LAD MB who had undergone coronary artery CT angiography (CCTA) were retrospectively enroled, and 104 normal patients were enroled as the control. The CCTA-derived fractional flow reserve (FFRCT) was measured at the LAD 10 mm proximal (FFR1) and 20-40 mm distal (FFR3) to the MB and at the MB location (FFR2).

RESULTS

FFR2 and FFR3 of the MB (with BM only) and MBLA (with both MB and atherosclerosis) groups were significantly (p < 0.01) lower than those of the control. The FFR3 distal to the MB was significantly lower (p < 0.01) than that of the control. The FFRCT of the whole LAD in the MBLA group was significantly (p < 0.05) lower than that of the MB and control group (p < 0.05). MB length (OR 1.061) and MB muscle index (odds ratio or OR 1.007) were two risk factors for abnormal FFRCT, and MB length was a significant independent risk factor for abnormal FFRCT (OR = 1.077). LAD stenosis degree was a risk factor for abnormal FFRCT values (OR 3.301, 95% confidence interval [CI] 1.441-7.562, p = 0.005) and was also a significant independent risk factor (OR = 3.369, 95% CI: 1.392-8.152; p = 0.007) for abnormal FFRCT.

CONCLUSION

MB significantly affects the FFRCT of distal coronary artery. For patients with MB without atherosclerosis, the MB length is a risk factor significantly affecting FFRCT, and for patients with MB accompanied by atherosclerosis, LAD stenotic severity is an independent risk factor for FFRCT.

Digital twins for cardiac electrophysiology: state of the art and future challenges

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.

Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates

BACKGROUND

Limited studies have investigated the accuracy of therapeutic decision-making using machine learning-based coronary computed tomography angiography (ML-CCTA) compared with CCTA.

PURPOSE

To investigate the performance of ML-CCTA for therapeutic decision compared with CCTA.

MATERIAL AND METHODS

The study population consisted of 322 consecutive patients with stable coronary artery disease. The SYNTAX score was calculated with an online calculator based on ML-CCTA results. Therapeutic decision-making was determined by ML-CCTA results and the ML-CCTA-based SYNTAX score. The therapeutic strategy and the appropriate revascularization procedure were selected using ML-CCTA, CCTA, and invasive coronary angiography (ICA) independently.

RESULTS

The sensitivity, specificity, positive predictive value, negative predictive value, accuracy of ML-CCTA and CCTA for selecting revascularization candidates were 87.01%, 96.43%, 95.71%, 89.01%, 91.93%, and 85.71%, 87.50%, 86.27%, 86.98%, 86.65%, respectively, using ICA as the standard reference. The area under the receiver operating characteristic curve (AUC) of ML-CCTA for selecting revascularization candidates was significantly higher than CCTA (0.917 vs. 0.866, P = 0.016). Subgroup analysis showed the AUC of ML-CCTA for selecting percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) candidates was significantly higher than CCTA (0.883 vs. 0.777, P < 0.001, 0.912 vs. 0.826, P = 0.003, respectively).

CONCLUSION

ML-CCTA could distinguish between patients who need revascularization and those who do not. In addition, ML-CCTA showed a slightly superior to CCTA in making an appropriate decision for patients and selecting a suitable revascularization strategy.

The prognostic value of CT-derived fractional flow reserve in coronary artery bypass graft: a retrospective multicenter study

OBJECTIVES

To investigate the predictive value of CT-derived fractional flow reserve (FFR_CT) in anastomosis occlusion after coronary artery bypass graft (CABG) surgery.

METHODS

Patients undergoing CABG with both pre- and post-operative coronary computed tomographic angiography (CCTA) were retrospectively included. Preoperative CCTA studies were used to evaluate anatomical and FFR_CT information of target vessels. A diameter stenosis (DS) ≥ 70% or left main > 50% was considered to be anatomically severe, while FFR_CT value ≤ 0.80 be functionally significant. The primary endpoint was anastomosis occlusion evaluated on post-operative CCTA during follow-up. Predictors of anastomosis occlusion were assessed by the multivariate binary logistic regression with generalized estimating equations.

RESULTS

A total of 270 anastomoses were identified in 88 enrolled patients. Forty-one anastomoses from 30 patients exhibited occlusion during a follow-up of 15.3 months after CABG. The occluded group had significantly increased prevalence of non-severe DS (58.5% vs. 40.2%; p = 0.023) and non-significant FFR_CT (48.8% vs. 10.0%; p < 0.001). Multivariable analysis indicated FFR_CT ≤ 0.80 (odds ratio [OR]: 0.10, 95% CI: 0.03-0.33; p < 0.001) and older age (OR: 0.92, 95% CI: 0.87-0.97; p = 0.001) were predictors for bypass patency during follow-up, while myocardial infarction history and anastomosis to a local lesion or bifurcation (all p value < 0.05) were predictors of occlusion. Adding FFR_CT into the model based on the clinical and anatomical predictors had an improved AUC of 0.848 (p = 0.005).

CONCLUSIONS

FFR_CT ≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. Preoperative judgment of the hemodynamic significance may improve the CABG surgery strategy and reduce graft failure.

KEY POINTS

• FFR_CT ≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. • The addition of FFR_CT into the integrated model including clinical (age and history of myocardial infarction) and anatomical CCTA indicators (local lesion and bifurcation) significantly improved the model performance with an AUC of 0.848 (p = 0.005). • Preoperative judgment of the hemodynamic significance may help improve the decision-making and surgery planning in patients indicated for CABG and significantly reduce graft failure, without an extra radiation exposure and risk of invasive procedure.

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.

Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks

RATIONALE AND OBJECTIVES

The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients.

MATERIALS AND METHODS

Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort.

RESULTS

Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001).

CONCLUSION

The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

Diagnostic efficacy of CCTA and CT-FFR based on risk factors for myocardial ischemia

Background

Coronary artery coronary computed tomography angiography (CCTA) can observe the degree of coronary artery stenosis and fractional flow reserve (FFR) can diagnose hemodynamic abnormalities caused by coronary artery stenosis. However, noninvasive imaging examination that can both observe the above two methods at the same time has not yet been elucidated.

Objective

To investigate the diagnostic efficacy of CCTA and computed tomography-derived fractional flow reserve (CT-FFR) based on different risk factors for myocardial ischemia.

Methods

Patients undergoing CCTA in our hospital from August 18, 2020 to April 28, 2021 were randomly selected, and the data were subjected to CT-FFR analysis. Vascular characteristics were measured, including total plaque volume, calcified plaque volume, non-calcified plaque volume, plaque length, and lumen stenosis, and the patients were categorized into a non-ischemia group (FFR > 0.8) and an ischemia group (FFR ≤ 0.8). Plaque characteristics were compared between the two groups, and logistic regression analysis was employed to explore the correlations between plaque characteristics and ischemic lesions.

Results

From a total of 122 patients enrolled in the study, there were 218 vascular branches with FFR > 0.8 and 174 vascular branches with FFR ≤ 0.8. There were significant group differences in total plaque volume, calcified plaque volume, plaque length, and lumen stenosis > 50% (n). The obtained data were as follows: non-ischemic group 10.57 (4.80, 259.65), ischemic group 14.87 (3.39, 424.45), Z = 9.772, p = 0.002, non-ischemic group 10.57 (0, 168.77), ischemic group 14.87 (0, 191.00), Z = 2.503, p ≤ 0.001), non-ischemic group 8.17 (37.05, 40.53), ischemic group 8.38 (56.66, 86.47), Z = 5.923, p = 0.016, and lumen stenosis > 50%, non-ischemic group 46, ischemic group 90, x² = 14.77, p ≤ 0.001. The regression analysis results indicated that total plaque volume, calcified plaque volume, plaque length and lumen stenosis > 50% were risk factors for myocardial ischemia, with ORs and p values of (2.311, p = 0.002), (1.021, p = 0.004), (2.159, p < 0.001), and (0.181, p < 0.001), respectively.

Conclusion

Total plaque volume, calcified plaque volume, plaque length and lumen stenosis > 50% are predictors for myocardial ischemia. Coronary artery CCTA combined with CT-FFR could simultaneously observe the anatomical stenosis and evaluate myocardial blood supply at the functional level. Thus, myocardial ischemia could be better diagnosed.

Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease

OBJECTIVES

To propose a novel functional Coronary Artery Disease-Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)-derived fractional flow reserve (FFR_CT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD).

METHODS

Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFR_CT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFR_CT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model.

RESULTS

Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS.

CONCLUSION

The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario.

KEY POINTS

• The novel functional CAD-RADS category system with FFR_CT integrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes.

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 (FFR_CT) 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 FFR_CT 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 FFR_CT 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 FFR_CT 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 FFR_CT 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 FFR_CT 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 FFR_CT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04).

CONCLUSION

In patients with intermediate stenosis, the FFR_CT 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 FFR_CT 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 FFR_CTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFR_CT 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.

Functional assessment of coronary plaques using CT based hemodynamic simulations: Current status, technical principles and clinical value

In recent years, coronary computed tomography angiography (CCTA) has emerged as an accurate and safe non-invasive imaging modality in terms of detecting and excluding coronary artery disease (CAD). In the latest European Society of Cardiology Guidelines CCTA received Class I recommendation for the evaluation of patients with stable chest pain with low to intermediate clinical likelihood of CAD. Despite its high negative predictive value, the diagnostic performance of CCTA is limited by the relatively low specificity, especially in patients with heavily calcified lesions. The discrepancy between the degree of stenosis and ischemia is well established based on both invasive and non-invasive tests. The rapid evolution of computational flow dynamics has allowed the simulation of CCTA derived fractional flow reserve (FFR-CT), which improves specificity by combining anatomic and functional information regarding coronary atherosclerosis. FFR-CT has been extensively validated against invasively measured FFR as the reference standard. Due to recent technological advancements FFR-CT values can also be calculated locally, without offsite processing. Wall shear stress (WSS) and axial plaque stress (APS) are additional key hemodynamic elements of atherosclerotic plaque characteristics, which can also be measured using CCTA images. Current evidence suggests that WSS and APS are important hemodynamic features of adverse coronary plaques. CCTA based hemodynamic calculations could therefore improve prognostication and the management of patients with stable CAD.

Imaging myocardial ischemia: from emerging techniques to state-of-the-art

The widespread clinical use of cardiovascular imaging inspires constant improvement in imaging technology and post-processing applications. Recent advances in hardware and software have brought about important developments in the assessment of myocardial ischemia such as the rapid evaluation of cardiac volumes and function, ability for detection of subtle myocardial changes, and the combination of anatomic and functional assessment of a coronary artery stenosis via a single modality, which was previously not possible in a noninvasive fashion. These milestones indicate the start of a new era, a paradigm shift that broadens the role of noninvasive imaging. The thematic series Myocardial tissue characterization in ischemic heart disease introduces a set of narrative review and original articles by world renowned authors demonstrating such novel advancements and the state-of-the-art techniques in cardiac imaging.

Impact of respiratory motion artifact on coronary image quality of one beat coronary CT angiography

BACKGROUND

Accuracy of CT-derived fractional flow reserve depends on good image quality. Thus, improving image quality during coronary CT angiography (CCTA) is important.

OBJECTIVE

To investigate impact of respiratory motion artifact on coronary image quality focusing on vessel diameter and territory during one beat CCTA by a 256-row detector.

METHODS

We retrospectively reviewed patients who underwent CCTA under free-breathing (n = 100) and breath-holding (n = 100), respectively. Coronary image quality is defined as 4-1 from excellent to poor (non-diagnostic) and respiratory motion artifact severity is also scored on a 4-point scale from no artifact to severe artifact. Coronary image quality and respiratory motion artifact severity of all images were evaluated by two radiologists independently.

RESULTS

Compared with free-breathing group, the image qualities are significantly higher in per-segment, per-vessel and per-patient levels (P < 0.001) and proportion of segments with excellent image quality also improves significantly (73.6% vs 60.1%, P < 0.001) in breath-holding group. The image quality improvement occurs in medium-sized coronary arterial segments. Coronary image quality improves with respiratory motion artifacts decreasing in both groups, respectively.

CONCLUSION

During one heartbeat CCTA, breath-holding is still recommended to improve coronary image quality due to improvement of the image quality in the medium-sized coronary arteries.

Umbrella review and multivariate meta-analysis of diagnostic test accuracy studies on hybrid non-invasive imaging for coronary artery disease

BACKGROUND

The diagnosis of coronary artery disease (CAD) remains challenging. It is uncertain whether hybrid imaging can improve diagnostic accuracy for CAD.

METHODS

This is a systematic review and multivariate meta-analysis. We searched PubMed and The Cochrane Library for recent (≥ 2010) systematic reviews of diagnostic test accuracy studies on non-invasive imaging for CAD. Study-level data were extracted from them, and pooled with pairwise and multivariate meta-analytic methods, using invasive coronary angiography (ICA) or invasive fractional flow reserve (FFR) as reference standards, focusing on sensitivity and specificity.

RESULTS

Details from 661 original studies (71,823 patients) were pooled. Pairwise meta-analysis using ICA as reference showed that anatomic imaging was associated with the best diagnostic accuracy (sensitivity = 0.95 [95% confidence interval 0.94-0.96], specificity = 0.83 [0.81-0.85]), whereas using FFR as reference identified hybrid imaging as the best test (sensitivity = 0.87 [0.83-0.90], specificity = 0.82 [0.76-0.87]). Multivariate meta-analysis confirmed the superiority of anatomic imaging using ICA as reference (sensitivity = 0.96, specificity = 0.83), and hybrid imaging using FFR as reference (sensitivity = 0.88 [0.86-0.91], specificity = 0.82 [0.77-0.87]).

CONCLUSIONS

Non-invasive hybrid imaging tests appear superior to anatomic or functional only tests to diagnose ischemia-provoking coronary lesions, whereas anatomic imaging is best to diagnose and/or rule out angiographically significant CAD.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO Registry Number CRD42018088528.

Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey

Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.

EFFECT OF HEMODYNAMIC PARAMETERS ON FRACTIONAL FLOW RESERVE

Background: The fractional flow reserve (FFR) is the gold standard used to diagnose whether coronary stenosis triggers myocardial ischemia. Myocardial ischemia is not only related to the degree of coronary artery disease but also to hemodynamic parameters such as mean arterial pressure, flow, and so on. This paper will explore the effects of hemodynamic parameters on FFR. Methods: Construct an ideal vascular model of moderately stenosis lesions (40–70%) with different hemodynamic environments. A pressure waveform was set as the inlet boundary, a microcirculation resistance in the hyperemia state was set as the outlet boundary, and different hemodynamic environments were constructed by changing the mean arterial pressure and flow at rest. The microcirculation resistance in the resting state was determined by the mean arterial pressure and flow, and the microcirculation resistance in the hyperemia state was 0.24 times than in the resting state. Results:Flow at rest was found to have the greatest impact on FFR, followed by arterial pressure. Both a decrease in flow and an increase in mean arterial pressure caused an increase in the FFR value. Conclusion:Based on the degree of stenosis of the diseased blood vessel, systolic pressure, diastolic blood pressure, and blood flow through the diseased blood vessel in the resting state, a preliminary judgment can be directly made as to whether the stenosis causes myocardial ischemia.

How to guide PCI?: A network meta-analysis

BACKGROUND

Traditional coronary angiography (CA) as a main technique has been used to determine the coronary artery anatomy and guide percutaneous coronary intervention (PCI). We mainly focused on whether the new techniques could improve the patients' mortality, major adverse cardiovascular events (MACEs), and myocardial infarction.

METHODS

For the network meta-analysis, we searched the trials of different PCI guidances from MEDLINE, Current Contents Connect, Google Scholar, EMBASE, Cochrane Library, PubMed, Science Direct, and Web of Science. The last search date was December 10, 2018.

RESULTS

The analyses of all results found that there was no significant difference in mortality among the groups. Randomized clinical trials (RCT) analysis showed that intravascular ultrasound (IVUS)-guided PCI was significantly superior to CA, fractional flow reserve, instantaneous wave-free ratio, optical coherence tomography. However, CA, fractional flow reserve, instantaneous wave-free ratio, and optical coherence tomography showed no difference in reducing mortality. The analyses of all results found that there was no significant difference in the incidence of MACEs among the groups. RCTs analysis showed that IVUS-guided PCI was significantly superior to CA, but there was no significant difference among the other groups. The analyses of all results or RCTs showed that there was no significant difference in myocardial infarction incidence among the groups.

CONCLUSION

IVUS-guided PCI is an effective method to decrease all-cause death MACEs.

Artificial Intelligence in Cardiovascular Imaging

The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potential to improve patient care at every stage of the imaging chain. Current literature indicates that in the short-term, AI has the capacity to reduce human error and save time in the clinical workflow through automated segmentation of cardiac structures. In the future, AI may expand the informational value of diagnostic images based on images alone or a combination of images and clinical variables, thus facilitating disease detection, prognosis, and decision making. This review describes the role of AI, specifically machine learning, in multimodality imaging, including echocardiography, nuclear imaging, computed tomography, and cardiac magnetic resonance, and highlights current uses of AI as well as potential challenges to its widespread implementation.

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.

On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm - Clinical Effectiveness in a Retrospective Multicenter Cohort

BACKGROUND

This study evaluated the diagnostic capability of on-site coronary computed tomography-derived computational fractional flow reserve (CT-FFR) determinations for detecting coronary artery disease (CAD), as assessed by invasive fractional flow reserve (FFR).Methods and Results:Seventy-four patients with coronary artery calcium scores <1,500 who underwent coronary CT angiography (CTA) and invasive FFR measurements within 90 days were retrospectively reviewed. CT-FFR was computed using a prototype machine-learning (ML) algorithm in 91 vessels; 47 vessels of 42 patients were determined to have significant CAD (FFR ≤0.8). Correlation between CT-FFR and FFR was good (r=0.786, P<0.001). Per-vessel area under the curve was significantly larger for CT-FFR (0.907, 95% confidence interval: 0.828-0.958) than for CTA stenosis ≥50% (0.595, 0.487-0.697) or ≥70% (0.603, 0.495-0.705) (both P<0.001). Standard coronary CTA classifications recommended further functional tests in 57 patients with moderate or worse stenosis on CTA. CT-FFR analysis (mean analysis time: 16.4±7.5 min) corrected the standard coronary CTA classification in 18 of 74 patients and confirmed it in 45 of 74 patients. Thus, the per-patient diagnostic accuracy of the classifications was improved from 66% (54-77%) to 85% (75-92%).

CONCLUSIONS

On-site CT-FFR based on a ML algorithm can provide good diagnostic performance for detecting hemodynamically significant CAD, suggesting the high value of coronary CTA for selected patients in clinical practice.