Comparison of Physician Visual Assessment With Quantitative Coronary Angiography in Assessment of Stenosis Severity in China

Intracoronary ECG ST-segment shift remission time during reactive myocardial hyperemia: a new method to assess hemodynamic coronary stenosis severity

Fractional flow reserve (FFR) measurements are recommended for assessing hemodynamic coronary stenosis severity. Intracoronary ECG (icECG) is easily obtainable and highly sensitive in detecting myocardial ischemia due to its close vicinity to the myocardium. We hypothesized that the remission time of myocardial ischemia on icECG after a controlled coronary occlusion accurately detects hemodynamically relevant coronary stenosis. This retrospective, observational study included patients with chronic coronary syndrome undergoing hemodynamic coronary stenosis assessment immediately following a strictly 1-min proximal coronary artery balloon occlusion with simultaneous icECG recording. icECG was used for a beat-to-beat analysis of the ST-segment shift during reactive hyperemia immediately following balloon deflation. The time from coronary balloon deflation until the ST-segment shift reached 37% of its maximum level, i.e., icECG ST-segment shift remission time (τ-icECG in seconds), was obtained by an automatic algorithm. τ-icECG was tested against the simultaneously obtained reactive hyperemia FFR at a threshold of 0.80 as a reference parameter. From 120 patients, 139 icECGs (age, 68 ± 10 yr old) were analyzed. Receiver operating characteristic (ROC) analysis of τ-icECG for the detection of hemodynamically relevant coronary stenosis at an FFR of ≤0.80 was performed. The area under the ROC curve was equal to 0.621 (P = 0.0363) at an optimal τ-icECG threshold of 8 s (sensitivity, 61%; specificity, 67%). τ-icECG correlated inversely and linearly with FFR (P = 0.0327). This first proof-of-concept study demonstrates that τ-icECG, a measure of icECG ST segment-shift remission after a 1-min coronary artery balloon occlusion accurately detects hemodynamically relevant coronary artery stenosis according to FFR at a threshold of ≥8 s.NEW & NOTEWORTHY Invasive hemodynamic measurements are recommended by the current cardiology guidelines to guide percutaneous coronary interventions in the setting of chronic coronary syndrome. However, those pressure-derived indices demonstrate several theoretical and practical limitations. Thus, this study demonstrates the accuracy of a novel, pathophysiology-driven approach using intracoronary ECG for the identification of hemodynamically relevant coronary lesions by quantitatively assessing myocardial ischemia remission.

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

AI in interventional cardiology: Innovations and challenges

Artificial Intelligence (AI) permeates all areas of our lives. Even now, we all use AI algorithms in our daily activities, and medicine is no exception. The potential of AI technology is hard to overestimate; AI has already proven its effectiveness in many fields of science and technology. A vast number of methods have been proposed and are being implemented in various areas of medicine, including interventional cardiology. A hallmark of this discipline is the extensive use of visualization techniques not only for diagnosis but also for the treatment of patients with coronary heart disease. The implementation of instrumental AI will reduce costs, in a broad sense. In this article, we provide an overview of AI research in interventional cardiology, practical applications, as well as the problems hindering the widespread use of neural network technologies in interventional cardiology.

Evaluation of stenoses using AI video models applied to coronary angiography

The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.

Relation of Vasoreactivity in the Left and Right Coronary Arteries During Acetylcholine Spasm Provocation Testing

The diagnosis of vasospastic angina (VSA) according to Japanese guidelines involves an initial intracoronary acetylcholine (ACh) provocation test in the left coronary artery (LCA) followed by testing in the right coronary artery (RCA). However, global variations in test protocols often lead to the omission of ACh provocation in the RCA, potentially resulting in the underdiagnosis of VSA. This study assessed the validity of the LCA-only ACh provocation approach for the VSA diagnosis and whether vasoreactivity in the LCA aids in determining further provocation in the RCA. A total of 273 patients who underwent sequential intracoronary ACh provocation testing in the LCA and RCA were included. Patients with a positive ACh provocation test in the LCA were excluded. Relations between vasoreactivity in the LCA and ACh test outcomes (positivity and adverse events) in the RCA were evaluated. In patients with negative ACh test results in the LCA, subsequent ACh testing was positive in the RCA in 23 of 273 (8.4%) patients. In patients with minimal LCA vasoconstriction (<25%), only 3.0% had a positive ACh test in the RCA, whereas the ACh test in the RCA was positive in 13.5% of those with LCA constriction of 25% to 90% (p = 0.002). No major adverse events occurred during ACh testing in the RCA. In conclusion, for the VSA diagnosis, the omission of ACh provocation in the RCA may be clinically acceptable, particularly when vasoconstriction induced by ACh injection was minimal in the LCA. Further studies are needed to define ACh provocation protocols worldwide.

A Narrative Review of Revascularization in Chronic Coronary Syndrome/Disease: Concepts and Misconceptions

Ischemic heart disease represents a significant global burden of morbidity and mortality. While revascularization strategies are well defined in acute settings, there are uncertainties regarding chronic coronary artery disease treatment. Recent trials have raised doubts about the necessity of revascularization for "stable", chronic coronary syndromes or disease, leading to a shift towards a more conservative approach. However, the issue remains far from settled. In this narrative review, we offer a summary of the most pertinent evidence regarding revascularization for chronic coronary disease, while reflecting on less-often-discussed details of major clinical trials. The cumulative evidence available indicates that there can be a prognostic benefit from revascularization in chronic coronary syndrome patients, provided there is significant ischemia, as demonstrated by either imaging or coronary physiology. Trials that have effectively met this criterion consistently demonstrate a reduction in rates of spontaneous myocardial infarction, which holds both prognostic and clinical significance. The prognostic benefit of revascularization in patients with heart failure with reduced ejection fraction remains especially problematic, with a single contemporary trial favouring surgical revascularization. The very recent publication of a trial focused on revascularizing non-flow-limiting "vulnerable" plaques adds further complexity to the field. The ongoing debates surrounding revascularization in chronic coronary syndromes emphasize the importance of personalized strategies. Revascularization, added to the foundational pillar of medical therapy, should be considered, taking into account symptoms, patient preferences, coronary anatomy and physiology, ischemia tests and intra-coronary imaging.

Quantitative Angiography: The Dawn of a New Era in Cardiovascular Medicine

This comprehensive review explores the transformative role of quantitative angiography in the landscape of cardiovascular medicine. Tracing the historical evolution of cardiovascular diagnostics, we emphasize the significance of angiography in diagnosis and treatment. The primary focus on quantitative angiography reveals its capacity to move beyond qualitative assessments, providing clinicians with precise measurements and objective parameters. This paradigm shift enhances diagnostic accuracy, promising far-reaching implications for the future of cardiovascular medicine. The ability to tailor interventions based on meticulous measurements optimizes therapeutic strategies and positions the field on the brink of a new era where personalized approaches become the norm. However, challenges such as image quality, radiation exposure, and interpretation variability persist, necessitating a collective call to action for continued research and development. As we confront these issues, collaborative efforts across disciplines are essential to refine existing technologies and usher in innovative solutions. This review concludes with a resounding call for ongoing research initiatives, large-scale clinical studies, and collective commitment to propel quantitative angiography into a universally accepted standard, ensuring its full realization in enhancing patient care and outcomes in cardiovascular medicine.

Quantitative Coronary Angiography vs Intravascular Ultrasonography to Guide Drug-Eluting Stent Implantation: A Randomized Clinical Trial

Importance

Although intravascular ultrasonography (IVUS) guidance promotes favorable outcomes after percutaneous coronary intervention (PCI), many catheterization laboratories worldwide lack access.

Objective

To investigate whether systematic implementation of quantitative coronary angiography (QCA) to assist angiography-guided PCI could be an alternative strategy to IVUS guidance during stent implantation.

Design, Setting, and Participants

This randomized, open-label, noninferiority clinical trial enrolled adults (aged ≥18 years) with chronic or acute coronary syndrome and angiographically confirmed native coronary artery stenosis requiring PCI. Patients were enrolled in 6 cardiac centers in Korea from February 23, 2017, to August 23, 2021, and follow-up occurred through August 25, 2022. All principal analyses were performed according to the intention-to-treat principle.

Interventions

After successful guidewire crossing of the first target lesion, patients were randomized in a 1:1 ratio to receive either QCA- or IVUS-guided PCI.

Main Outcomes and Measures

The primary outcome was target lesion failure at 12 months, defined as a composite of cardiac death, target vessel myocardial infarction, or ischemia-driven target lesion revascularization. The trial was designed assuming an event rate of 8%, with the upper limit of the 1-sided 97.5% CI of the absolute difference in 12-month target lesion failure (QCA-guided PCI minus IVUS-guided PCI) to be less than 3.5 percentage points for noninferiority.

Results

The trial included 1528 patients who underwent PCI with QCA guidance (763; mean [SD] age, 64.1 [9.9] years; 574 males [75.2%]) or IVUS guidance (765; mean [SD] age, 64.6 [9.5] years; 622 males [81.3%]). The post-PCI mean (SD) minimum lumen diameter was similar between the QCA- and IVUS-guided PCI groups (2.57 [0.55] vs 2.60 [0.58] mm, P = .26). Target lesion failure at 12 months occurred in 29 of 763 patients (3.81%) in the QCA-guided PCI group and 29 of 765 patients (3.80%) in the IVUS-guided PCI group (absolute risk difference, 0.01 percentage points [95% CI, -1.91 to 1.93 percentage points]; hazard ratio, 1.00 [95% CI, 0.60-1.68]; P = .99). There was no difference in the rates of stent edge dissection (1.2% vs 0.7%, P = .25), coronary perforation (0.2% vs 0.4%, P = .41), or stent thrombosis (0.53% vs 0.66%, P = .74) between the QCA- and IVUS-guided PCI groups. The risk of the primary end point was consistent regardless of subgroup, with no significant interaction.

Conclusions and Relevance

Findings of this randomized clinical trial indicate that QCA and IVUS guidance during PCI showed similar rates of target lesion failure at 12 months. However, due to the lower-than-expected rates of target lesion failure in this trial, the findings should be interpreted with caution.

Trial Registration

ClinicalTrials.gov Identifier: NCT02978456.

Noninvasive cardiac-specific biomarkers for the diagnosis and prevention of vascular stenosis in cardiovascular disorder

Background

The most frequent lesion in the blood vessels feeding the myocardium is vascular stenosis, a condition that develops slowly but can prove to be deadly in a long run. Non-invasive biomarkers could play a significant role in timely diagnosis, detection and management for vascular stenosis events associated with cardiovascular disorders.

Aims

The study aimed to investigate high sensitivity troponin I (hs-TnI), cardiac troponin I (c-TnI) and high sensitivity C-reactive protein (hs-CRP) that may be used solely or in combination in detecting the extent of vascular stenosis in CVD patients.

Methodology

274 patients with dyspnea/orthopnea complaints visiting the cardiologists were enrolled in this study. Angiographic study was conducted on the enrolled patients to examine the extent of stenosis in the five prominent vessels (LDA, LCX, PDA/PLV, RCA, and OM) connected to the myocardium. Samples from all the cases suspected to be having coronary artery stenosis were collected, and subjected to biochemical evaluation of certain cardiac inflammatory biomarkers (c-TnI, hsTn-I and hs-CRP) to check their sensitivity with the level of vascular stenosis. The extent of mild and culprit stenosis was detected during angiographic examination and the same was reported in the form significant (≥50% stenosis in the vessels) and non-significant (<50% stenosis in the vessels) Carotid Stenosis. Ethical Clearance for the study was provided by Dr. Ram Manohar Lohia Institute of Medical Sciences Institutional Ethical Committee. Informed consent was obtained from all the participants enrolled in the study.

Results

We observed that 85% of the total population enrolled in this study was suffering from hypertension followed by 62.40% detected with sporadic episodes of chest pain. Most of the subjects (42% of the total population) had stenosis in their LAD followed by 38% who had stenosis in their RCA. Almost 23% patients were reported to have stenosis in their LCX followed by OM (18% patients), PDA/PLV (13%) and only 10% patients had blockage problem in their diagonal. 24% of the subjects were found to have stenosis in a single vessel and hence were categorized in the Single Vessel Disease (SVD) group while 76% were having stenosis in two or more than two arteries (Multiple Vessel Disease). hs-TnI level was found to be correlated with the levels of stenosis and was higher in the MVD group as compared to the SVD group.

Conclusion

hs-TnI could be used as a novel marker as it shows prominence in detecting the level of stenosis quite earlier as compared to c-TnI which gets detected only after a long duration in the CVD patients admitted for angiography. hs- CRP gets readily detected as inflammation marker in these patients and hence could be used in combination with hs-TnI to detect the risk of developing coronary artery disease.

Role of intravascular ultrasound and optical coherence tomography in intracoronary imaging for coronary artery disease: a systematic review

Coronary angiography has long been the standard for coronary imaging, but it has limitations in assessing vessel wall anatomy and guiding percutaneous coronary intervention (PCI). Intracoronary imaging techniques like intravascular ultrasound (IVUS) and optical coherence tomography (OCT) can overcome these limitations. IVUS uses ultrasound and OCT uses near-infrared light to visualize coronary pathology in unique ways due to differences in temporal and spatial resolution. These techniques have evolved to offer clinical utility in plaque characterization and vessel assessment during PCI. Meta-analyses and adjusted observational studies suggest that both IVUS and OCT-guided PCI correlate with reduced cardiovascular risks compared to angiographic guidance alone. While IVUS demonstrates consistent clinical outcome benefits, OCT evidence is less robust. IVUS has progressed from early motion detection to high-resolution systems, with smaller compatible catheters. OCT utilizes near infrared light to achieve unparalleled resolutions, but requires temporary blood clearance for optimal imaging. Enhanced visualization and guidance make IVUS and OCT well-suited for higher risk PCI in patients with diabetes and chronic kidney disease by allowing detailed visualization of complex lesions and ensuring optimal stent deployment and positioning in PCI for patients with type 2 diabetes and chronic kidney disease, improving outcomes. IVUS and recent advancements in zero- and low-contrast OCT techniques can reduce nephrotoxic contrast exposure, thus helping to minimize PCI complications in these high-risk patient groups. IVUS and OCT provide valuable insights into coronary pathophysiology and guide interventions precisely compared to angiography alone. Both have comparable clinical outcomes, emphasizing the need for tailored imaging choices based on clinical scenarios. Continued refinement and integration of intravascular imaging will likely play a pivotal role in optimizing coronary interventions and outcomes. This systematic review aims to delve into the nuances of IVUS and OCT, highlighting their strengths and limitations as PCI adjuncts.

Machine Learning in Invasive and Noninvasive Coronary Angiography

PURPOSE OF REVIEW

The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine.

RECENT FINDINGS

The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD. The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.

Segmentation of X-ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity

BACKGROUND

Visual assessment of the percentage diameter stenosis (%DSVE ) of lesions is essential in coronary angiography (CAG) interpretation. We have previously developed an artificial intelligence (AI) model capable of accurate CAG segmentation. We aim to compare operators' %DSVE in angiography versus AI-segmented images.

METHODS

Quantitative coronary analysis (QCA) %DS (%DSQCA ) was previously performed in our published validation dataset. Operators were asked to estimate %DSVE of lesions in angiography versus AI-segmented images in separate sessions and differences were assessed using angiography %DSQCA as reference.

RESULTS

A total of 123 lesions were included. %DSVE was significantly higher in both the angiography (77% ± 20% vs. 56% ± 13%, p < 0.001) and segmentation groups (59% ± 20% vs. 56% ± 13%, p < 0.001), with a much smaller absolute %DS difference in the latter. For lesions with %DSQCA of 50%-70% (60% ± 5%), an even higher discrepancy was found (angiography: 83% ± 13% vs. 60% ± 5%, p < 0.001; segmentation: 63% ± 15% vs. 60% ± 5%, p < 0.001). Similar, less pronounced, findings were observed for %DSQCA  < 50% lesions, but not %DSQCA  > 70% lesions. Agreement between %DSQCA /%DSVE across %DSQCA strata (<50%, 50%-70%, >70%) was approximately twice in the segmentation group (60.4% vs. 30.1%; p < 0.001). %DSVE inter-operator differences were smaller with segmentation.

CONCLUSION

%DSVE was much less discrepant with segmentation versus angiography. Overestimation of %DSQCA  < 70% lesions with angiography was especially common. Segmentation may reduce %DSVE overestimation and thus unwarranted revascularization.

CathAI: fully automated coronary angiography interpretation and stenosis estimation

Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.

Deep Learning Model for Coronary Angiography

The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera movements, and uneven illumination. More accurate and intelligent coronary angiography diagnostic models are necessary for improving the above problems. In this study, 2980 medical images from 949 patients are collected and a novel deep learning-based coronary angiography (DLCAG) diagnose system is proposed. Firstly, we design a module of coronary classification. Then, we introduce RetinaNet to balance positive and negative samples and improve the recognition accuracy. Additionally, DLCAG adopts instance segmentation to segment the stenosis of vessels and depict the degree of the stenosis vessels. Our DLCAG is available at http://101.132.120.184:8077/ . When doctors use our system, all they need to do is login to the system, upload the coronary angiography videos. Then, a diagnose report is automatically generated.

Hospital-Level Variability in Use of Intracoronary Imaging for Percutaneous Coronary Intervention in the United States

Background

Intracoronary (IC) imaging for percutaneous coronary intervention (PCI) is associated with better patient outcomes and carries a class IIA guideline recommendation, but it remains rarely used. We sought to characterize hospital-level variability in IC imaging for PCI in the United States and to identify factors that may explain this variability.

Methods

Patients who underwent PCI, with or without IC imaging, in the Nationwide Readmissions Database (2016-2020) were included. A regression model with a random effect for site was used to generate the median odds ratio (MOR) of IC imaging use for a patient at one site vs another, sequentially adjusting for procedural, patient, and hospital factors to examine the extent to which different factors account for this variability.

Results

The analytic cohort included 1,328,517 PCI procedures (patient mean age 65.8 years, 32.4% female, IC imaging used in 9.2%) at 1068 hospitals. The median hospital use of IC imaging increased from 2.7% (IQR, 0.6-7.7) in 2016 to 6.3% (IQR, 1.7-17.8) in 2020. In 2020, the MOR for IC imaging during PCI was 4.6 (IQR, 4.3-5.0), indicating a >4-fold difference in the odds of a patient undergoing IC imaging with PCI at one random hospital vs another. Adjusting for procedure, patient, and hospital factors did not meaningfully alter the MOR.

Conclusion

The average US hospital uses IC imaging for <1 in 15 PCI procedures, with marked variability across hospitals. Strategies to increase and standardize the use of IC imaging are needed to improve the quality of PCI in the United States.

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

INTRODUCTION

We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported.

METHODS

Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured.

RESULTS

123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset.

CONCLUSION

the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.

Editor's Choice - Interventions to Achieve Functionality in Newly Created Arteriovenous Fistulas in the Shunt Simulation Study Cohort

OBJECTIVE

Although observational cohort studies report that interventions to achieve functionality are clinically successful in 85% of patients, the proportion of newly created autologous arteriovenous fistulas that result in functional vascular access typically is only 70 - 80%. To address this discrepancy, the selection and outcomes of interventions to achieve functionality in a multicentre prospective cohort study were analysed.

METHODS

The Shunt Simulation Study enrolled 222 patients who needed a first arteriovenous fistula in nine dialysis units in The Netherlands from 2015 to 2018 and followed these patients until one year after access creation. In this observational study, the technical and clinical success rates of interventions to achieve functionality based on lesion and intervention characteristics were analysed and the clinical outcomes of arteriovenous fistulas with assisted and unassisted functionality were compared.

RESULTS

For patients who were on dialysis treatment at the end of the study, unassisted fistula functionality was 54% and overall fistula functionality was 78%. Thirty-four per cent of arteriovenous fistulas required an intervention to achieve functionality, 68% of which eventually became functional. Seventy-five per cent of these interventions were percutaneous balloon angioplasties of vascular access stenoses. Patients with clinically successful interventions to achieve functionality had larger pre-operative vein diameters (2.8 ± 1.0 mm vs. 2.3 ± 0.6 mm, p = .036) and less often presented with thrombosed fistulas than patients with unsuccessful interventions (7% vs. 43%, p = .006). Arteriovenous fistulas with assisted functionality had similar secondary patency as fistulas with unassisted functionality (100% and 98% at six months, p = .44), although they required more interventions to maintain function (2.6 vs. 1.7 per year; rate ratio 1.52, 95% CI 1.04 - 2.18, p = .032).

CONCLUSION

Interventions to achieve functionality were needed in about a third of newly created arteriovenous fistulas. Most thrombosed fistulas were abandoned, and when selected for thrombectomy rarely reached clinical success. On the other hand, interventions to achieve functionality of patent fistulas had high clinical success rates and therefore can be done repeatedly until the fistula has become functional.

The Chinese Experience of Imaging in Cardiac Intervention: A Bird's Eye Review

Recent scientific and technological advances have greatly contributed to the development of medical imaging that could enable specific functions. It has become the primary focus of cardiac intervention in preoperative assessment, intraoperative guidance, and postoperative follow-up. This review provides a contemporary overview of the Chinese experience of imaging in cardiac intervention in recent years.

Intracoronary injection of nitroglycerine can prevent unnecessary percutaneous coronary intervention

Background

Despite the recommendation of the current guidelines, intracoronary administration of nitroglycerine during coronary angiography is often neglected. We investigated the effect of intra-coronary nitroglycerin on the relief of coronary artery stenosis in the candidates for percutaneous coronary intervention (PCI).

Methods

We included patients with angina pectoris or myocardial infarction who were candidates for PCI. In the coronary angiography, the culprit vessel involved was evaluated, and bolus nitroglycerin at a dose of 25–200 mcg was injected into the affected coronary artery. A significant change in the percentage of coronary artery stenosis was considered a positive response, and these patients were then compared with patients who did not have a substantial change in the percentage of stenosis at the same time. Univariate analysis and then multivariate logistic regression analysis was performed to determine the predictors of response to intracoronary nitroglycerin.

Results

Among 360 patients, 27 (7.5%) responded to nitroglycerine, and 333 (92.5%) were non-responsive. The mean age of patients was 60.2 ± 11.6 years, ranging from 23 to 93 years, and 265 (73.6%) were men. The study groups were not significantly different in the baseline demographic characteristics. The presence of multivessel disease (Odds ratio (OR) = 16.26, 95% confidence interval (CI):2.07–127.6; P = 0.008) and stenosis in the left circumflex artery (OR = 3.62, 95% CI: 1.03–12.70; P = 0.044) were the independent predictors for nonresponse to nitroglycerine, leading to PCI.

Conclusion

In some cases, especially those without multivessel diseases, intracoronary nitroglycerine administration can efficiently relieve coronary stenosis and prevent unnecessary PCI.

Procedural Characteristics of Intravascular Ultrasound–Guided Percutaneous Coronary Intervention and Their Clinical Implications

Background

Despite the clinical benefits to intravascular ultrasound (IVUS) guidance for percutaneous coronary intervention (PCI), most patients with coronary artery disease undergo angiography‐guided PCI alone in the real‐world setting. We sought to investigate the procedural characteristics of IVUS‐guided PCI and their clinical outcomes, as compared with angiography‐guided PCI.

Methods and Results

This was a cohort study using patient‐level data from the IVUS‐XPL (Impact of Intravascular Ultrasound Guidance on the Outcomes of Xience Prime Stents in Long Lesions) and ULTIMATE (Intravascular Ultrasound Guided Drug Eluting Stents Implantation in All‐Comers Coronary Lesions) clinical trials. A total of 2848 patients with 3872 native coronary lesions were included and procedural characteristics assessed by quantitative coronary angiography (QCA) were compared between IVUS and angiography guidance. Stent‐to‐reference vessel diameter ratio (ie, QCA stent sizing) was greater (1.11±0.16 versus 1.07±0.14, P <0.001) and high‐pressure postdilation was more frequently performed (83.7% versus 75.4%, P <0.001) with IVUS guidance, whereas residual stent edge dissections were more frequent in lesions treated with IVUS guidance (4.6% versus 0.7%, P <0.001). Given the dissection risk, optimal QCA stent sizing for IVUS guidance was a stent‐to‐QCA reference vessel diameter ratio ≥1.1 to <1.3. Among 1424 patients (1969 lesions) treated with angiography guidance, QCA stent sizing <1.0 was observed in 651 (33.1%) lesions, while QCA stent sizing ≥1.1 to <1.3 was observed in only 526 (26.7%) lesions. Under angiography guidance, patients with both QCA stent sizing ≥1.1 to <1.3 and high‐pressure postdilation (235 of 1424, 16.5%) had a lower risk of 3‐year target lesion failure compared with others (hazard ratio, 0.532; 95% CI, 0.293–0.966 [ P =0.038]).

Conclusions

IVUS‐guided PCI resulted in larger QCA‐assessed stent sizing and more frequent postdilation with high‐pressure inflations. These procedures may further improve long‐term clinical outcomes in patients undergoing PCI without IVUS.

Registration

URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01308281 (IVUS‐XPL); NCT02215915 (ULTIMATE).

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

PURPOSE OF REVIEW

Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA).

RECENT FINDINGS

Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31-14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience

Background

To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.

Methods

We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1–Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.

Results

The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2–+ 16.6% and + 5.9–+ 16.1%, respectively) and NPVs (range + 3.7–+ 13.4% and + 2.7–+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61).

Conclusions

Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA.

Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00756-y.

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features

BACKGROUND

Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures.

METHODS

A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union).

RESULTS

With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images.

CONCLUSIONS

The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

Sex differences in assessing stenosis severity between physician visual assessment and quantitative coronary angiography

BACKGROUND

Physician visual assessment (PVA) in invasive coronary angiography (ICA) is the current clinical method to determine stenosis severity and guide percutaneous coronary intervention. This study sought to evaluate the effect of sex differences in assessing coronary stenosis severity between PVA and quantitative coronary angiography (QCA).

METHODS

209 patients with coronary artery disease (288 coronary lesions) underwent ICA and fractional flow reserve (FFR). ICA image processing including PVA and QCA was used to quantify diameter stenosis (DS). The difference of DS (ΔDS) between PVA and QCA was defined as DS_PVA-DS_QCA. DS ≥50% was considered anatomically obstructive. FFR ≤0.8 was defined as myocardial ischemia.

RESULTS

Mean ± SD age was 63 ± 9 years. There were no significant differences in DS_PVA (61.1 ± 16.3% vs 60.1 ± 18.9%) and DS_QCA (53.1 ± 12.1% vs 55.4 ± 14.3%) between females and males. However, ΔDS between PVA and QCA was higher in females (8.0 ± 10.9%) than in males (4.7 ± 10.9%) (P = 0.03). Thirty-four of 72 vessels (47.2%) in female patients and 75 of 216 vessels (34.7%) in male patients were classified differently by at least one grade using PVA compared to QCA assessment. DS_PVA and DS_QCA were negatively correlated with FFR in females (r_PVA = -0.397, r_QCA = -0.448) with an even stronger negative correlation in males (r_PVA = -0.607, r_QCA = -0.607). ROC analysis demonstrated that DS_QCA had better discrimination capability for myocardial ischemia (FFR ≤ 0.80) than DS_PVA in both sexes (P < 0.05).

CONCLUSIONS

A systematic bias was found in PVA (QCA reference) for overestimating severity of coronary artery disease in females compared to males.

Stent visualization methods to guide percutaneous coronary interventions and assess long-term patency

Evaluation of acute percutaneous coronary intervention (PCI) results and long-term follow-up remains challenging with ongoing stent designs. Several imaging tools have been developed to assess native vessel atherosclerosis and stent expansion, improving overall PCI results and reducing adverse cardiac events. Quantitative coronary analysis has played a crucial role in quantifying the extent of coronary artery disease and stent results. Digital stent enhancement methods have been well validated and improved stent strut visualization. Intravascular imaging remains the gold standard in PCI guidance but adds costs and time to the procedure. With a recent shift towards non-invasive imaging assessment and coronary computed tomography angiography imaging have shown promising results. We hereby review novel stent visualization techniques used to guide PCI and assess stent patency in the modern PCI era.

A New Method for Detecting Myocardial Ischemia Based on ECG T-Wave Area Curve (TWAC)

In recent years, coronary heart disease (CHD) has become one of the main diseases that endanger human health, with a high mortality and disability rate. Myocardial ischemia (MI) is the main symptom in the development of CHD. Continuous and severe myocardial ischemia will lead to myocardial infarction. The clinical manifestations of MI are mainly the changes of ST-T segment of ECG, that is, ST segment and T wave. Nearly one third of patients with CHD, however, has no obvious ECG changes. In this paper, a new method for detecting MI based on the T-wave area curve (TWAC) was proposed. Through observation and analysis of clinical data, it was found that there exist significant correlation between the morphology of TWAC and MI. The TWAC morphology of normal subject is smooth and gentle, while the TWAC morphology of patients with coronary stenosis is mostly jagged, and the curve becomes more severe with more severe stenosis. The preliminary test results show that the sensitivity, specificity, and accuracy of the proposed method for detecting MI are 84.3, 83.6, and 84%, respectively. This study shows that the TWAC based approach may be an effective method for detecting MI, especially for the CHD patients with no obvious ECG changes.

Functional assessment of myocardial ischaemia by intracoronary ECG

Introduction

In patients with chronic coronary syndrome, percutaneous coronary intervention targets haemodynamically significant stenoses, that is, those thought to cause ischaemia. Intracoronary ECG (icECG) detects ischaemia directly where it occurs. Thus, the goal of this study was to test the accuracy of icECG during pharmacological inotropic stress to determine functional coronary lesion severity in comparison to the structural parameter of quantitative angiographic per cent diameter stenosis (%S), as well as to the haemodynamic indices of fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR).

Method

The primary study endpoint of this prospective trial was the maximal change in icECG ST-segment shift during pharmacological inotropic stress induced by dobutamine plus atropine obtained within 1 min after reaching maximal heart rate(=220 - age). IcECG was acquired by attaching an alligator clamp to the angioplasty guidewire positioned downstream of the stenosis. For the pressure-derived stenosis severity ratios, coronary perfusion pressure and simultaneous aortic pressure were continuously recorded.

Results

There was a direct linear relation between icECG ST-segment shift and %S: icECG=−0.8+0.03*%S (r²=0.164; p<0.0001). There were inverse linear correlations between FFR and %S: FFR=1.1–6.1*10–3*%S (r²=0.494; p<0.0001), and between iFR and %S: iFR=1.27–8.6*10–3*%S (r²=0.461; p<0.0001). Using a %S-threshold of ≥50% as the reference for structural stenosis relevance, receiver operating characteristics-analysis of absolute icECG ST-segment shift during hyperemia showed an area under the curve (AUC) of 0.678±0.054 (p=0.002; sensitivity=85%, specificity=50% at 0.34 mV). AUC for FFR was 0.854±0.037 (p<0.0001; sensitivity=64%, specificity=96% at 0.78), and for iFR it was 0.816±0.043 (p<0.0001;sensitivity=62%, specificity=96% at 0.83).

Conclusions

Hyperaemic icECG ST-segment shift detects structurally relevant coronary stenotic lesions with high sensitivity, while they are identified highly specific by FFR and iFR.

A digital biomarker of diabetes from smartphone-based vascular signals

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

Myocardial Infarction With Nonobstructive Coronary Arteries

Myocardial infarction with nonobstructive coronary arteries (MINOCA) is the current term used to describe patients who have a myocardial infarction but have normal, non-obstructed coronary arteries on a coronary angiogram. There is still much debate over the definition, diagnosis, management and treatment of MINOCA. However, MINOCA is not a benign condition; prompt recognition and diagnosis can lead to better management and treatment and thus improve patient outcomes. This review article will update the most recent definition of MINOCA, discuss epidemiology and etiology, and review the diagnostic workup and management options for patients presenting with signs and symptoms of MINOCA.

Mis-estimation of coronary lesions and rectification by SYNTAX score feedback for coronary revascularization appropriateness

BACKGROUND

Imprecise interpretation of coronary angiograms was reported and resulted in inappropriate revascularization. Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score is a comprehensive system to evaluate the complexity of the overall lesions. We hypothesized that a real-time SYNTAX score feedback from image analysts may rectify the mis-estimation and improve revascularization appropriateness in patients with stable coronary artery disease (CAD).

METHODS

In this single-center, historical control study, patients with stable CAD with coronary lesion stenosis ≥50% were consecutively recruited. During the control period, SYNTAX scores were calculated by treating cardiologists. During the intervention period, SYNTAX scores were calculated by image analysts immediately after coronary angiography and were provided to cardiologists in real-time to aid decision-making. The primary outcome was revascularization deemed inappropriate by Chinese appropriate use criteria for coronary revascularization.

RESULTS

A total of 3245 patients were enrolled and assigned to the control group (08/2016-03/2017, n = 1525) or the intervention group (03/2017-09/2017, n = 1720). For SYNTAX score tertiles, 17.9% patients were overestimated and 4.3% were underestimated by cardiologists in the control group. After adjustment, inappropriate revascularization significantly decreased in the intervention group compared with the control group (adjusted odds ratio [OR]: 0.83; 95% confidence interval [CI]: 0.73-0.95; P = 0.007). Both inappropriate percutaneous coronary intervention (adjusted OR: 0.82; 95% CI: 0.74-0.92; P < 0.001) and percutaneous coronary intervention utilization (adjusted OR: 0.88; 95% CI: 0.79-0.98; P = 0.016) decreased significantly in the intervention group. There was no significant difference in 1-year adverse cardiac events between the control group and the intervention group.

CONCLUSIONS

Real-time SYNTAX score feedback significantly reduced inappropriate coronary revascularization in stable patients with CAD.

CLINICAL TRIAL REGISTRATION

Nos. NCT03068858 and NCT02880605; https://www.clinicaltrials.gov.

Contemporary Cardiac MRI in Chronic Coronary Artery Disease

Chronic coronary artery disease remains an unconquered clinical problem, affecting an increasing number of people worldwide. Despite the improved understanding of the disease development, the implementation of the many advances in diagnosis and therapy is lacking. Many clinicians continue to rely on patient's symptoms and diagnostic methods, which do not enable optimal clinical decisions. For example, echocardiography and invasive coronary catheterisation remain the mainstay investigations for stable angina patients in many places, despite the evidence on their limitations and availability of better diagnostic options. Cardiac MRI is a powerful diagnostic method, supporting robust measurements of crucial markers of cardiac structure and function, myocardial perfusion and scar, as well as providing detailed insight into myocardial tissue. Accurate and informative diagnostic readouts can help with guiding therapy, monitoring disease progress and tailoring the response to treatment. In this article, the authors outline the evidence supporting the state-of-art applications based on cardiovascular magnetic resonance, allowing the clinician optimal use of this insightful diagnostic method in everyday clinical practice.

Deep learning segmentation of major vessels in X-ray coronary angiography

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

Accuracy of 3-dimensional and 2-dimensional quantitative coronary angiography for predicting physiological significance of coronary stenosis: a FAVOR II substudy

Background

Three-dimensional quantitative coronary angiography (3D-QCA) enables reconstruction of a coronary artery in 3D from two angiographic image projections. This study compared the diagnostic accuracy of 3D-QCA vs. 2-dimensional (2D) QCA in predicting physiologically significant coronary stenosis, using fractional flow reserve (FFR) as the reference standard.

Methods

All interrogated vessels in the FAVOR II China study and the FAVOR II Europe-Japan study were assessed by 2D-QCA and 3D-QCA according to standard operating procedures in core laboratories. QCA analysts were blinded to the corresponding FFR values.

Results

A total of 645 vessels from 576 patients with 3D-QCA, 2D-QCA, and FFR were analyzed. Using the conventional cut-off value of 50% for percent diameter stenosis (DS%), 3D-QCA was more accurate in predicting FFR ≤0.80 than 2D-QCA [accuracy 74.0% (95% CI: 69.9-77.7%) vs. 64.9% (95% CI: 61.3-68.7%), difference: 9.1%, P<0.001]. Sensitivity was higher by 3D-QCA compared with 2D-QCA [69.1% (95% CI: 63.0-75.1%) vs. 47.1% (95% CI: 40.5-53.6%), difference: 22.0%, P<0.001] and specificity was similar [76.5% (95% CI: 72.5-80.6%) vs. 74.4% (95% CI: 70.2-78.6%), difference: 2.1%, P=0.40]. Area under the receiver operating characteristic curve was significantly higher for 3D-QCA than for 2D-QCA [0.81 (95% CI: 0.77-0.84) vs. 0.66 (95% CI: 0.62-0.71), P<0.001].

Conclusions

3D-QCA demonstrated better diagnostic performance in predicting physiologically significant coronary stenosis compared with 2D-QCA, when FFR was used as the reference standard.

Coronary computed tomography angiography equals invasive angiography for the prediction of coronary revascularization

INTRODUCTION

Growing role of coronary computed tomography angiography (CTA) as a diagnostic tool in patients with suspected coronary artery disease (CAD) calls for better recognition of its value in clinical decision making as compared to the gold standard of invasive coronary angiography (ICA).

AIM

To assess the diagnostic value of quantitative coronary computed tomography angiography (QCT) as compared to quantitative coronary angiography (QCA) for the prediction of coronary revascularization.

MATERIAL AND METHODS

In this prospective observational study we included 100 patients who underwent ICA following CTA. Quantitative diameter stenosis analysis (qCTA) was performed with Syngo.via (Siemens Medical Systems) software by an experienced investigator blinded to results of ICA. Quantitative Coronary Angiography (QCA) was chosen to define %DS in a repetitive manner. ICA images were submitted to Qangio XA (Medis, Leiden, The Netherlands) software for QCA analysis.

RESULTS

Eighty out of 400 analysed vessels were revascularized. Per-vessel diagnostic accuracy, sensitivity, specificity, PPV an NPV were 80%, 98%, 73%, 48% and 99% for QCT and 81%, 99%, 73%, 48% and 100% for QCA, respectively, for the prediction of revascularization. AUC was similar: 0.88 for QCT and 0.89 for QCA (p = NS).

CONCLUSIONS

These real-world data support the concept that CTA is as precise in prediction of coronary revascularization as ICA. This may add to the discussion about CTA having the potential to replace ICA for diagnosing vessels qualified for intervention, reserving the invasive diagnostic approach for those with the highest probability of revascularization.

Middle-aged man who could not afford an angioplasty

Coronary artery disease managed by percutaneous coronary intervention (PCI) has been noted for profit-driven overuse medicine. Concerns mount over inappropriate use of PCI for patients in India. We describe the case of a 55-year-old Indian man who presented for a second opinion following an urgent recommendation for PCI by two cardiologists following a recent acute myocardial infarction even though the patient was symptom-free and out of the window period for primary PCI. The proposed intervention placed the patient at financial risk for insolvency. This case report highlights the challenges and consequences of inappropriate overuse of PCI. Also, we outline the current lack of shared decision-making among patients and physicians for the PCI procedure. The challenges, inherent in the assumptions that overuse of PCI is evidence-based, are discussed including recommendations for the practice of evidence based medicine for this intervention.