Novel QCA methodologies and angiographic scores

Comparison of different guidance strategies to percutaneous coronary intervention: A network meta-analysis of randomized clinical trials

BACKGROUND

The results of randomized clinical trials comparing the outcomes of different strategies for driving PCI are mixed, and it remains unclear which technique for driving PCI offers the greatest benefit. The aim of the study was to compare the clinical efficacy of different techniques to guide percutaneous coronary intervention (PCI).

METHODS

We search major electronic databases for randomized clinical trials evaluating clinical outcomes of PCI with stent implantation guided by coronary angiography (CA), fractional flow reserve (FFR), instantaneous wave-free ratio (iFR), intravascular ultrasound (IVUS) and optical coherence tomography (OCT). The primary outcome was cardiac death.

RESULTS

The results from 39 randomized trials (29,614 patients) were included in the network meta-analyses. Compared with CA, the use of OCT (RR: 0.33, 95 % CI: 0.19-0.54), IVUS (RR: 0.47, 95 % CI: 0.31-0.71) and FFR (RR: 0.61, 95 % CI: 0.38-0.97) were associated with reduced risk of cardiac death; there were no differences between OCT, IVUS and OCT was ranked as the best strategy. PCI guidance using OCT, FFR and IVUS was also associated with a reduction of myocardial infarction. The use of OCT or IVUS for PCI guidance was associated with a significant reduction in target lesion failure, target vessel revascularization, target lesion revascularization and stent thrombosis, compared with CA. OCT-guided PCI was associated with a significant reduction in all-cause death compared with CA-guided PCI and with a reduction in TLF compared with FFR- and iFR-guided PCI. Pooled estimates were mostly consistent across several sensitivity analyses.

CONCLUSIONS

Compared with angiography-guided PCI, both an intravascular imaging-guided strategy and a physiology-guided strategy are associated with better clinical outcomes.

Clinical Significance of Optical Coherence Tomography-Guided Percutaneous Coronary Intervention for In-Stent Restenosis Within Drug-Eluting Stents: Impact on Patient Outcomes

BACKGROUND

The evidence for optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) in improving the prognosis of individuals with in-stent restenosis (ISR) is lacking.

METHODS AND RESULTS

This retrospective study enrolled 588 consecutive individuals with drug-eluting stent ISR undergoing PCI from March 2010 to March 2022. Two hundred seven (35.2%) underwent OCT guidance, and 381 (64.8%) underwent angiography guidance. Clinical outcomes were analyzed using survival curves. The primary clinical endpoint was 2-year major adverse cardiovascular events (MACEs), a composite of all-cause death, myocardial infarction, and target-vessel revascularization. Compared with angiography guidance, OCT guidance demonstrated a higher frequency of drug-coated balloon use and adjunctive therapeutic modalities, including predilation, postdilation, nonslip element balloons, and noncompliant balloons (P<0.05). Following PCI, the OCT-guided group achieved a significantly larger minimum lumen diameter (2.36 versus 2.15 mm, P<0.001) and a lower percentage diameter stenosis (17% versus 20%, P<0.001) than the angiography-guided group. Survival analysis revealed significantly lower 2-year MACEs in the OCT-guided group compared with the angiography-guided group (7% versus 15%, P=0.007), validated in the propensity matching analysis (7% versus 15%, P=0.001). Multiple sensitivity analyses showed that OCT-guided PCI treatment was an independent protective factor for 2-year MACEs in individuals with drug-eluting stent ISR.

CONCLUSIONS

Compared with angiography guidance, OCT guidance is associated with a lower 2-year MACE risk among individuals with drug-eluting stent ISR. Therefore, OCT should be actively considered for guiding PCI treatment in individuals with drug-eluting stent ISR.

REGISTRATION

Url: clinicaltrials.gov. Identifier: NCT03809754.

Patient-specific computational simulation of coronary artery bypass grafting

INTRODUCTION

Coronary artery bypass graft surgery (CABG) is an intervention in patients with extensive obstructive coronary artery disease diagnosed with invasive coronary angiography. Here we present and test a novel application of non-invasive computational assessment of coronary hemodynamics before and after bypass grafting.

METHODS AND RESULTS

We tested the computational CABG platform in n = 2 post-CABG patients. The computationally calculated fractional flow reserve showed high agreement with the angiography-based fractional flow reserve. Furthermore, we performed multiscale computational fluid dynamics simulations of pre- and post-CABG under simulated resting and hyperemic conditions in n = 2 patient-specific anatomies 3D reconstructed from coronary computed tomography angiography. We computationally created different degrees of stenosis in the left anterior descending artery, and we showed that increasing severity of native artery stenosis resulted in augmented flow through the graft and improvement of resting and hyperemic flow in the distal part of the grafted native artery.

CONCLUSIONS

We presented a comprehensive patient-specific computational platform that can simulate the hemodynamic conditions before and after CABG and faithfully reproduce the hemodynamic effects of bypass grafting on the native coronary artery flow. Further clinical studies are warranted to validate this preliminary data.

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera

PURPOSE

Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

METHODS

546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well.

RESULTS

Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%).

CONCLUSION

Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.

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.

Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies

Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. In this paper, we evaluate the performance of a purely image based workflow relying on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies. A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible. A second DNN predicts cardiac phase labels for each frame. Only in the training and evaluation phases for the second DNN, ECG signals are used to provide ground truth labels for each angiographic frame. The networks were trained on 56,655 coronary angiographies from 6820 patients and evaluated on 20,780 coronary angiographies from 6261 patients. No exclusion criteria related to patient state (stable or acute CAD), previous interventions (PCI or CABG), or pathology were formulated. Cardiac phase detection had an accuracy of 98.8 %, a sensitivity of 99.3 % and a specificity of 97.6 % on the evaluation set. EDF prediction had a precision of 98.4 % and a recall of 97.9 %. Several sub-group analyses were performed, indicating that the cardiac phase detection performance is largely independent from acquisition angles, the heart rate of the patient, and the angiographic view (LCA / RCA). The average execution time of cardiac phase detection for one angiographic series was on average less than five seconds on a standard workstation. We conclude that the proposed image based workflow potentially obviates the need for manual frame selection and ECG acquisition, representing a relevant step towards automated CAD assessment.

Applying Deep Neural Networks over Homomorphic Encrypted Medical Data

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

Impact of Coronary Artery Disease Severity Assessed With the SYNTAX Score on Outcomes Following Transcatheter Aortic Valve Replacement

Background

The influence of coronary artery disease (CAD) on clinical and echocardiographic outcomes after transcatheter aortic valve replacement (TAVR) is still controversial. We sought to evaluate the impact of CAD severity as measured by the SYNTAX score (SS) on patients undergoing TAVR.

Methods and Results

A total of 377 patients who underwent TAVR in 2 high‐volume centers in North America were included in our retrospective analysis. A blinded angiographic core laboratory calculated the SS on all available coronary angiograms with the use of quantitative coronary analysis. Patients were stratified into 4 groups: (1) no CAD (SS=0); (2) low SS (SS between 1 and 22); (3) intermediate SS (SS between 23 and 32); and (4) high SS (SS ≥33). Patients who had undergone percutaneous coronary intervention within 6 months prior to TAVR were separated into 2 categories based on their residual SS (<8 and ≥8). Patients with previous coronary artery bypass grafting (CABG) were divided into 2 groups: (1) low CABG SS and (2) high CABG SS. The primary end point was a composite of all‐cause mortality, myocardial infarction, and stroke. At 30 days and 1 year, both the presence and the severity of CAD had no impact on the rate of the combined primary end point and on all‐cause mortality, cardiovascular mortality, and myocardial infarction. Patients with less complete revascularization (residual SS ≥8 versus residual SS <8 and low CABG SS versus high CABG SS, had similar rates of the combined primary end point, all‐cause mortality, cardiovascular mortality, MI, and stroke, at both 30 days and 1 year.

Conclusions

In our core laboratory–validated study, neither the severity of CAD nor completeness of revascularization after percutaneous coronary intervention or CABG were associated with clinical outcomes after TAVR, at both 30 days and 1 year.

Coronary CT angiography in calcified coronary plaques: Comparison of diagnostic accuracy between bifurcation angle measurement and coronary lumen assessment for diagnosing significant coronary stenosis

BACKGROUND

To investigate the diagnostic value of coronary CT angiography (CCTA) by bifurcation angle measurement in the assessment of calcified plaques compared to conventional coronary lumen analysis.

METHODS

Fifty-three patients with calcified plaques identified on CCTA in the left coronary artery were included in the study. Minimal lumen diameter (MLD) and bifurcation angle between the left anterior descending (LAD) and left circumflex (LCx) arteries were measured and compared between CCTA and invasive coronary angiography (ICA), while the areas under the curves (AUCs) by receiver-operating characteristic curve analysis (ROC) were compared between CCTA and ICA with regard to the diagnostic value of using bifurcation angle as a criterion.

RESULTS

On a per-vessel assessment, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and 95% confidence interval (CI) with the use of bifurcation angle for determining coronary stenosis were 100% (86%, 100%), 79% (59%, 92%), 81% (62%, 92%), and 100% (85%, 100%) for CCTA, and 100% (86%, 100%), 82% (63%, 94%), 83% (65%, 94%), and 100% (85%, 100%) for ICA, respectively. While the sensitivity and NPV remained unchanged, the specificity and PPV of CCTA by MLD were 33% (21%, 47%) and 43% (31%, 56%). The AUCs by ROC curve analysis for CCTA and ICA bifurcation angle measurements demonstrated no significant difference (p>0.05, 0.79 vs 0.86, and 0.70 vs 0.68 at the LAD and LCx assessment, respectively).

CONCLUSION

Coronary CT angiography by bifurcation angle measurement shows significant improvement in the diagnosis of calcified plaques with diagnostic value comparable to invasive coronary angiography.

Comparative cath-lab assessment of coronary stenosis by radiology technician, junior and senior interventional cardiologist in patients treated with coronary angioplasty

BACKGROUND

Exact quantification of plaque extension during coronary angioplasty (PCI) usually falls on interventional cardiologist (IC). Quantitative coronary stenosis assessment (QCA) may be possibly committed to the radiology technician (RT), who usually supports cath-lab nurse and IC during PCI. We therefore sought to investigate the reliability of QCA performed by RT in comparison with IC.

METHODS

Forty-four consecutive patients with acute coronary syndrome underwent PCI; target coronary vessel size beneath target coronary lesion (S) and target coronary lesion length (L) were assessed by the RT, junior IC (JIC), and senior IC (SIC) and then compared. SIC evaluation, which determined the final stent selection for coronary stenting, was considered as a reference benchmark.

RESULTS

RT performance with QCA support in assessing target vessel size and target lesion length was not significantly different from SIC (r = 0.46, p < 0.01; r = 0.64, p < 0.001, respectively) as well as JIC (r = 0.79, r = 0.75, p < 0.001, respectively). JIC performance was significantly better than RT in assessing target vessel size (p < 0.05), while not significant when assessing target lesion length.

CONCLUSIONS

RT may reliably assess target lesion by using adequate QCA software in the cath-lab in case of PCI; RT performance does not differ from SIC.

Angiographic validation of the American College of Cardiology Foundation-the Society of Thoracic Surgeons Collaboration on the Comparative Effectiveness of Revascularization Strategies study

BACKGROUND

The goal of this study was to compare angiographic interpretation of coronary arteriograms by sites in community practice versus those made by a centralized angiographic core laboratory.

METHODS AND RESULTS

The study population consisted of 2013 American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR) records with 2- and 3- vessel coronary disease from 54 sites in 2004 to 2007. The primary analysis compared Registry (NCDR)-defined 2- and 3-vessel disease versus those from an angiographic core laboratory analysis. Vessel-level kappa coefficients suggested moderate agreement between NCDR and core laboratory analysis, ranging from kappa=0.39 (95% confidence intervals, 0.32-0.45) for the left anterior descending artery to kappa=0.59 (95% confidence intervals, 0.55-0.64) for the right coronary artery. Overall, 6.3% (n=127 out of 2013) of those patients identified with multivessel disease at NCDR sites had had 0- or 1-vessel disease by core laboratory reading. There was no directional bias with regard to overcall, that is, 12.3% of cases read as 3-vessel disease by the sites were read as <3-vessel disease by the core laboratory, and 13.9% of core laboratory 3-vessel cases were read as <3-vessel by the sites. For a subset of patients with left main coronary disease, registry overcall was not linked to increased rates of mortality or myocardial infarction.

CONCLUSIONS

There was only modest agreement between angiographic readings in clinical practice and those from an independent core laboratory. Further study will be needed because the implications for patient management are uncertain.

QCA, IVUS and OCT in interventional cardiology in 2011

Over the past 30 years, quantitative coronary arteriography (QCA) has been used extensively as an objective and reproducible tool in clinical research to assess changes in vessel dimensions as a result of interventions, but also as a tool to provide evidence to the interventionalist prior to and after an intervention and at follow-up when necessary. With the increasing complexities of bifurcation stenting, corresponding analytical tools for bifurcation analysis have been developed with extensive reporting schemes. Although intravascular ultrasound (IVUS) has been around for a long time as well, more recent radiofrequency analysis provides additional information about the vessel wall composition; likewise optical coherence tomography (OCT) provides detailed information about the positions of the stent struts and the quality of the stent placement. Combining the information from the X-ray lumenogram and the intravascular imaging devices is mentally a challenging task for the interventionalist. To support the registration of these intravascular images with the X-ray images, 3D QCA has been developed and registered with the IVUS or OCT images, so that at every position along the vessel of interest the luminal data and the vessel wall data by IVUS or the stent strut data by OCT can be combined. From the 3D QCA the selection of the optimal angiographic views can also be facilitated. It is the intention of this overview paper to provide an extensive description of the techniques that we have developed and validated over the past 30 years.

Six-year prognosis of diabetic patients with coronary artery disease

BACKGROUND

Diabetes is associated with increased cardiovascular mortality. The aim of our study was to determine the prognostic factors for mortality in patients with type 2 diabetes (T2DM) and coronary artery disease (CAD) who underwent coronary angiography and percutaneous coronary intervention.

MATERIALS AND METHODS

Four hundred and forty-five consecutive T2DM patients with significant CAD (≥ 75% stenosis) were included in our analysis. All patients underwent standard clinical examination, laboratory tests and transthoracic echocardiography with measurement of the left ventricular ejection fraction. Severity of CAD at the coronary angiography was evaluated using the Gensini score. Clinical follow-up was completed at 1, 3 and 6 years.

RESULTS

During a mean follow-up of 73·3 ± 22·1 months, 109 patients died (24·5%). Significant determinants of an increased risk of death at multivariable analysis were age (p < 0·001), serum creatinine (p = 0·001), peripheral vascular disease (p = 0·002), serum glucose (p = 0·004), serum fibrinogen (p = 0·011) and history of heart failure (HF, p = 0·011). When all the variables were entered as categorical variables, with continuous variables split at their median value, only history of HF, estimated glomerular filtration rate, serum glucose, serum fibrinogen (all p < 0·0001) and beta-blocker therapy at discharge (p = 0·027) were selected.

CONCLUSIONS

Our study shows a relatively good prognosis of patients with T2DM. Comorbidities, namely HF and renal impairment, are main determinants of survival.