CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning

Prognostic implications of pre-transcatheter aortic valve replacement computed tomography-derived coronary plaque characteristics and stenosis severity

OBJECTIVES

The study aimed to investigate the prognostic value of pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) in assessing physiological stenosis severity (CTA-derived fractional flow reserve (CT-FFR)) and high-risk plaque characteristics (HRPC).

MATERIALS AND METHODS

Among TAVR patients who underwent pre-procedure CTA, the presence and number of HRPCs (minimum lumen area of < 4 mm2, plaque burden ≥ 70%, low-attenuating plaques, positive remodeling, napkin-ring sign, or spotty calcification) as well as CT-FFR were assessed. The risk of vessel-oriented composite outcome (VOCO, a composite of vessel-related ischemia-driven revascularization, vessel-related myocardial infarction, or cardiac death) was compared according to the number of HRPC and CT-FFR categories.

RESULTS

Four hundred and twenty-seven patients (68.4% were male) with 1072 vessels were included. Their mean age was 70.6 ± 10.6 years. Vessels with low CT-FFR (≤ 0.80) (41.7% vs. 15.8%, adjusted hazard ratio (HRadj) 1.96; 95% confidence interval (CI): 1.28-2.96; p = 0.001) or lesions with ≥ 3 HRPC (38.7% vs. 16.0%, HRadj 1.81; 95%CI 1.20-2.71; p = 0.005) demonstrated higher VOCO risk. In the CT-FFR (> 0.80) group, lesions with ≥ 3 HRPC showed a significantly higher risk of VOCO than those with < 3 HRPC (34.7% vs. 13.0%; HRadj 2.04; 95%CI 1.18-3.52; p = 0.011). However, this relative increase in risk was not observed in vessels with positive CT-FFR (≤ 0.80).

CONCLUSIONS

In TAVR candidates, both CT-FFR and the presence of ≥ 3 HRPC were associated with an increased risk of adverse clinical events. However, the value of HRPC differed with the CT-FFR category, with more incremental predictability among vessels with negative CT-FFR but not among vessels with positive CT-FFR.

CLINICAL RELEVANCE STATEMENT

In transcatheter aortic valve replacement (TAVR) candidates, pre-TAVR CTA provided the opportunity to assess coronary physiological stenosis severity and high-risk plaque characteristics, both of which are associated with worse clinical outcomes.

KEY POINTS

• The current study investigated the prognostic value of coronary physiology significance and plaque characteristics in transcatheter aortic valve replacement patients. • The combination of coronary plaque vulnerability and physiological significance showed improved accuracy in predicting clinical outcomes in transcatheter aortic valve replacement patients. • Pre-transcatheter aortic valve replacement CT can be a one-stop-shop tool for coronary assessments in clinical practice.

Additional prognostic impact of plaque characterization with on-site CT-derived fractional flow reserve in coronary CT angiography

BACKGROUND

On-site computed tomography-derived fractional flow reserve (CT-FFR) is a feasible method for examining lesion-specific ischemia, and plaque analysis of coronary CT angiography (CCTA) is useful for predicting future cardiac events. However, their utility and association on a per-vessel level remain unclear.

METHODS

We analyzed vessels showing 50-90 % stenosis on CCTA where planned revascularization was not performed after CCTA within 90 days. Relevant features, including CT-FFR and the plaque burden [necrotic core to the total plaque volume (% necrotic core), and non-calcified plaque (NCP) to vessel volume (% NCP)] using a novel algorithm for analyzing plaque to predict vessel-oriented composite outcomes (VOCO), including cardiac death, non-fatal myocardial infarction, and unplanned vessel-related revascularization, were assessed.

RESULTS

In 256 patients (68.7 ± 9.4 years; 73.8 % male) with 354 vessels (10.5 % CT-FFR ≤ 0.80), VOCO occurred in 24 vessels (6.8 %) during a median follow-up of 3.6 years. Multivariable Cox analysis revealed CT-FFR ≤ 0.80 had the pronounced impact on VOCO, and moreover, higher % necrotic core and % NCP were independently associated with VOCO [adjusted hazard ratio 3.43 (95 % confidence interval 1.42-8.29) and 4.05 (1.19-13.71), respectively], especially for vessels with CT-FFR > 0.80.

CONCLUSIONS

In vessels without planned revascularization, per-vessel CT-FFR ≤ 0.80 was the notable predictor of future cardiac events. Additionally, necrotic core volume and NCP were identified as independent predictors along with CT-FFR.

Long-term prognostic implications of CT angiography-derived fractional flow reserve: Results from the DISCOVER-FLOW study

BACKGROUND & OBJECTIVES

The long-term prognostic implications of CT angiography-derived fractional flow reserve (FFRCT) remains unclear. We aimed to explore the long-term outcomes of FFRCT in the first-in-human study of it.

MATERIALS & METHODS

A total of 156 vessels from 102 patients with stable coronary artery disease, who underwent coronary CT angiography (CCTA) and invasive FFR measurement, were followed. The primary endpoint was target vessel failure (TVF), including cardiovascular death, target vessel myocardial infarction, and target vessel revascularization. Outcome analysis with FFRCT was performed on a per-vessel basis using a marginal Cox proportional hazard model.

RESULTS

During median 9.9 years of follow-up, TVF occurred in 20 (12.8%) vessels. FFRCT ​≤0.80 discriminated TVF (hazard ratio [HR] 2.61, 95% confidence interval [CI] 1.06, 6.45). Among 94 vessels with deferral of percutaneous coronary intervention (PCI), TVF risk was inversely correlated with FFRCT ​(HR 0.62 per 0.1 increase, 95% CI 0.44, 0.86), with the cumulative incidence of TVF being 2.6%, 15.2%, and 28.6% for vessels with FFRCT ​>0.90, 0.81-0.90, and ≤0.80, respectively (p-for-trend 0.005). Predictive value for clinical outcomes of FFRCT was similar to that of invasive FFR (c-index 0.79 vs 0.71, P ​= ​0.28). The estimated TVF risk was higher in the deferral of PCI group than the PCI group for vessels with FFRCT ≤0.81.

CONCLUSION

FFRCT showed improved long-term risk stratification and displayed a risk continuum similar to invasive FFR.

CLINICAL TRIAL REGISTRATION

NCT01189331.

Clinical Outcomes Based on Coronary Computed Tomography-Derived Fractional Flow Reserve and Plaque Characterization

BACKGROUND

Coronary computed tomography angiography (CTA) followed by computed tomography angiography-derived fractional flow reserve (FFRCT) is now commonly used for the management of chronic coronary syndrome (CCS). CTA-verified high-risk plaque (HRP) characteristics have also been reported to be associated with a greater likelihood of adverse cardiac events but have not been used for management decisions.

OBJECTIVES

The aim of this study was to evaluate clinical outcomes based on a combination of point-of-care computed tomography angiography-derived fractional flow reserve (POC-FFRCT) and the presence of HRP in CCS patients initially treated medically or with revascularization based on invasive coronary angiography findings.

METHODS

CTA was performed as the initial test in 5,483 patients presenting with CCS between September 2015 and December 2020 followed by invasive coronary angiography and revascularization as necessary. POC-FFRCT assessment and HRP characterization were obtained subsequently in 745 consecutive patients. We investigated how HRP and POC-FFRCT, which were not available during the original clinical decision making, correlated with the endpoint defined as a composite of cardiac death, acute coronary syndrome, and a need for unplanned revascularization.

RESULTS

Cardiac events occurred in 20 patients (2.7%) during a median follow-up of 744 days. The event rate was significantly higher in patients with POC-FFRCT <0.80 compared with POC-FFRCT ≥0.8 (5.4 vs 0.5 per 100 vessel years; log-rank P < 0.0001) and in patients with HRP compared to those without HRP (3.6 vs 0.8 per 100 vessel years; log-rank P = 0.0001). POC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events (HR: 16.67; 95% CI: 2.63-105.39; P = 0.002) compared with POC-FFRCT ≥0.8 and absent HRP. For the vessels with POC-FFRCT <0.80 and HRP, a significantly higher rate of adverse events was observed in patients who did not undergo revascularization compared with those revascularized (16.4 vs 1.4 per 100 vessel years; log-rank P = 0.006).

CONCLUSIONS

POC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events, and revascularization of HRP lesions with abnormal POC-FFRCT was associated with a lower event rate.

Prognostic Implications of Quantitative Flow Ratio and Plaque Characteristics in Intravascular Ultrasound-Guided Treatment Strategy

BACKGROUND

Quantitative flow ratio (QFR) is a method for evaluating fractional flow reserve without the use of an invasive coronary pressure wire or pharmacological hyperemic agent.

OBJECTIVES

The aim of this study was to investigate the prognostic implications of QFR and plaque characteristics in patients who underwent intravascular ultrasound (IVUS)-guided treatment for intermediate lesions.

METHODS

Among the IVUS-guided strategy group in the FLAVOUR (Fractional Flow Reserve and Intravascular Ultrasound for Clinical Outcomes in Patients with Intermediate Stenosis) trial, vessels suitable for QFR analysis were included in this study. High-risk features were defined as low QFR (≤0.90), quantitative high-risk plaque characteristics (qn-HRPCs) (minimal lumen area ≤3.5 mm2, or plaque burden ≥70%), and qualitative high-risk plaque characteristics (ql-HRPCs) (attenuated plaque, positive remodeling, or plaque rupture) assessed using IVUS. The primary clinical endpoint was target vessel failure (TVF), defined as a composite of cardiac death, target vessel myocardial infarction, and target vessel revascularization.

RESULTS

A total of 415 (46.1%) vessels could be analyzable for QFR. The numbers of qn-HRPCs and ql-HRPCs increased with decreasing QFR. Among deferred vessels, those with 3 high-risk features exhibits a significantly higher risk of TVF compared with those with ≤2 high-risk features (12.0% vs 2.7%; HR: 4.54; 95% CI: 1.02-20.29).

CONCLUSIONS

Among the IVUS-guided deferred group, vessels with qn-HRPC and ql-HRPC with low QFR (≤0.90) exhibited a significantly higher risk for TVF compared with those with ≤2 features. Integrative assessment of angiography-derived fractional flow reserve and anatomical and morphological plaque characteristics is recommended to improve clinical outcomes in patients undergoing IVUS-guided deferred treatment.

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

Life's Essential 8 and Life's Simple 7 in Relation to Coronary Atherosclerosis: Results From the Population-Based SCAPIS Project

OBJECTIVE

To examine the associations between the American Heart Association scores ("Life's Essential 8" [LE8] and "Life's Simple 7" [LS7]) and 2 subclinical coronary atherosclerosis indicators: coronary computed tomographic angiography (CCTA)-stenosis and coronary artery calcium (CAC).

PATIENTS AND METHODS

We included a population-based sample, aged 50 to 64 years, recruited between 2013 and 2018 from the Swedish Cardiopulmonary Bioimage Study (n=24,819, 50.3% women). CCTA-stenosis was graded as no stenosis, stenosis (1%-49%) or severe stenosis (≥50%), whereas CAC was graded as 0, 1 to 99, 100 to 399, or ≥400 Agatston units. Multinomial logistic regression and receiver operating characteristic (ROC) curves were used to study the associations between cardiovascular health scores and subclinical coronary atherosclerosis.

RESULTS

Odds ratios (ORs) for CCTA-stenosis and severe CCTA-stenosis between the lowest (<50 points) vs the highest (≥80 points) LE8 group were 4.18 (95% CI, 3.56 to 4.91) and 11.17 (95% CI, 8.36 to 14.93), respectively. For corresponding CAC results, ORs were 3.36 (95% CI, 2.84 to 3.98), 7.72 (95% CI, 6.03 to 9.89), and 14.94 (95% CI, 10.47 to 21.31) for CAC scores of 1 to 99, 100 to 399, and ≥400, respectively. Area under ROC curves for predicting any stenosis were 0.642 (95% CI, 0.635 to 0.649) and 0.631 (95% CI, 0.624 to 0.638, P<.001) for LE8 and LS7, respectively.

CONCLUSION

Our data indicate that LE8 showed a strong, graded, and inverse association with CCTA-stenosis and CAC score. The capacity to predict CCTA-stenosis was comparable between LE8 and LS7, although LE8 had slightly higher prediction capacity of any stenosis. This study provides novel evidence that the LE8 score may be a useful tool for monitoring cardiovascular health.

Left atrioventricular coupling index assessed using cardiac CT as a prognostic marker of cardiovascular death

PURPOSE

The purpose of this study was to investigate the prognostic value of left atrioventricular coupling index (LACI) assessed by cardiac computed tomography (CT), to predict cardiovascular death in consecutive patients referred for cardiac CT with coronary analysis.

MATERIALS AND METHODS

Between 2010 and 2020, we conducted a single-centre study with all consecutive patients without known cardiovascular disease referred for cardiac CT. LACI was defined as the ratio of left atrial to left ventricle end-diastolic volumes. The primary outcome was cardiovascular death. Cox regressions were used to evaluate the association between LACI and primary outcome after adjustment for traditional risk factors and cardiac CT angiography findings.

RESULTS

In 1,444 patients (mean age, 70 ± 12 [standard deviation] years; 43% men), 67 (4.3%) patients experienced cardiovascular death after a median follow-up of 6.8 (Q1, Q3: 5.9, 9.1) years. After adjustment, LACI was positively associated with the occurrence of cardiovascular death (adjusted hazard ratio [HR], 1.07 [95% CI: 1.05-1.09] per 1% increment; P < 0.001), and all-cause death (adjusted HR, 1.05 [95% CI: 1.03-1.07] per 1% increment; P <0.001). After adjustment, a LACI ≥ 25% showed the best improvement in model discrimination and reclassification for predicting cardiovascular death above traditional risk factors and cardiac CT findings (C-statistic improvement: 0.27; Nnet reclassification improvement = 0.826; Integrative discrimination index =0.209, all P < 0.001; likelihood-ratio-test, P < 0.001).

CONCLUSION

LACI measured by cardiac CT is independently associated with cardiovascular death and all-cause death in patients without known cardiovascular disease referred for cardiac CT, with an incremental prognostic value over traditional risk factors and cardiac CT findings.

Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume

We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.

Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches

PURPOSE OF REVIEW

In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health.

RECENT FINDINGS

Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia

BACKGROUND

This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA).

METHODS AND RESULTS

This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05).

CONCLUSION

pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.

Clinical and Vessel Characteristics Associated With Hard Outcomes After PCI and Their Combined Prognostic Implications

Background Cardiac death or myocardial infarction still occurs in patients undergoing contemporary percutaneous coronary intervention (PCI). We aimed to identify adverse clinical and vessel characteristics related to hard outcomes after PCI and to investigate their individual and combined prognostic implications. Methods and Results From an individual patient data meta-analysis of 17 cohorts of patients who underwent post-PCI fractional flow reserve measurement after drug-eluting stent implantation, 2081 patients with available clinical and vessel characteristics were analyzed. The primary outcome was cardiac death or target-vessel myocardial infarction at 2 years. The mean age of patients was 64.2±10.2 years, and the mean angiographic percent diameter stenosis was 63.9%±14.3%. Among 11 clinical and 8 vessel features, 4 adverse clinical characteristics (age ≥65 years, diabetes, chronic kidney disease, and left ventricular ejection fraction <50%) and 2 adverse vessel characteristics (post-PCI fractional flow reserve ≤0.80 and total stent length ≥54 mm) were identified to independently predict the primary outcome (all P<0.05). The number of adverse vessel characteristics had additive predictability for the primary end point to that of adverse clinical characteristics (area under the curve 0.72 versus 0.78; P=0.03) and vice versa (area under the curve 0.68 versus 0.78; P=0.03). The cumulative event rate increased in the order of none, either, and both of adverse clinical characteristics ≥2 and adverse vessel characteristics ≥1 (0.3%, 2.4%, and 5.3%; P for trend <0.01). Conclusions In patients undergoing drug-eluting stent implantation, adverse clinical and vessel characteristics were associated with the risk of cardiac death or target-vessel myocardial infarction. Because these characteristics showed independent and additive prognostic value, their integrative assessment can optimize post-PCI risk stratification. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04684043. www.crd.york.ac.uk/prospero/. Unique Identifier: CRD42021234748.

Comparison of CT-derived Plaque Characteristic Index With CMR Perfusion for Ischemia Diagnosis in Stable CAD

BACKGROUND

Coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR) have been used to diagnose lesion-specific ischemia in patients with coronary artery disease. The aim of this study was to investigate the diagnostic performance of CCTA-derived plaque characteristic index compared with myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) derived from CMR perfusion in the assessment of lesion-specific ischemia.

METHODS

Between October 2020 and March 2022, consecutive patients with suspected or known coronary artery disease, who were clinically referred for invasive coronary angiography were prospectively enrolled. All participants sequentially underwent CCTA and CMR and invasive fractional flow reserve within 2 weeks. The diagnostic performance of CCTA-derived plaque characteristics, CMR perfusion-derived stress MBF, and MPR were compared. Lesions with fractional flow reserve ≤0.80 were considered to be hemodynamically significant stenosis.

RESULTS

Nighty-two patients with 141 vessels were included in this study. Plaque length, minimum luminal area, plaque area, percent area stenosis, total atheroma volume, vessel volume, lipid-rich volume, spotty calcium, napkin-ring signs, stress MBF, and MPR in flow-limiting stenosis group were significantly different from nonflow-limiting group. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of lesion-specific ischemia diagnosis were 61.0%, 55.3%, 63.1%, 35.6%, and 79.3% for stress MBF, and 89.4%, 89.5%, 89.3%, 75.6%, 95.8% for MPR; meanwhile, 82.3%, 79.0%, 84.5%, 65.2%, and 91.6% for CCTA-derived plaque characteristic index.

CONCLUSIONS

In our prospective study, CCTA-derived plaque characteristics and MPR derived from CMR performed well in diagnosing lesion-specific myocardial ischemia and were significantly better than stress MBF in stable coronary artery disease.

Coronary Physiology-Based Approaches for Plaque Vulnerability: Implications for Risk Prediction and Treatment Strategies

In the catheterization laboratory, the measurement of physiological indexes can help identify functionally significant lesions and has become one of the standard methods to guide treatment decision-making. Plaque vulnerability refers to a coronary plaque susceptible to rupture, enabling risk prediction before coronary events, and it can be detected by defining a certain type of plaque morphology on coronary imaging modalities. Although coronary physiology and plaque vulnerability have been considered different attributes of coronary artery disease, the underlying pathophysiological basis and clinical data indicate a strong correlation between coronary hemodynamic properties and vulnerable plaque. In prediction of coronary events, emerging data have suggested independent and additional implications of a physiology-based approach to a plaque-based approach. This review covers the fundamental interplay between coronary physiology and plaque morphology during disease progression with clinical data supporting this relationship and examines the clinical relevance of physiological indexes in prediction of clinical outcomes and therapeutic decision-making along with plaque vulnerability.

Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability

Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.

Imaging Early Life Cardiovascular Phenotype

The growing epidemics of obesity, hypertension, and diabetes, in addition to worsening environmental factors such as air pollution, water scarcity, and climate change, have fueled the continuously increasing prevalence of cardiovascular diseases (CVDs). This has caused a markedly increasing burden of CVDs that includes mortality and morbidity worldwide. Identification of subclinical CVD before overt symptoms can lead to earlier deployment of preventative pharmacological and nonpharmacologic strategies. In this regard, noninvasive imaging techniques play a significant role in identifying early CVD phenotypes. An armamentarium of imaging techniques including vascular ultrasound, echocardiography, magnetic resonance imaging, computed tomography, noninvasive computed tomography angiography, positron emission tomography, and nuclear imaging, with intrinsic strengths and limitations can be utilized to delineate incipient CVD for both clinical and research purposes. In this article, we review the various imaging modalities used for the evaluation, characterization, and quantification of early subclinical cardiovascular diseases.

Learning Curve of Perclose ProGlide Utilization During Percutaneous Coronary Intervention

OBJECTIVE

This study aimed to investigate the learning curve (LC) of Perclose ProGlide (Chicago, IL: Abbott Laboratories) utilization for percutaneous coronary intervention (PCI) for the first time.

METHODS

The study was conducted in a prospective manner and the final sample of the study was determined as 80 patients. Patients' characteristics, diameter of common femoral artery (CFA), distance from skin to CFA, degree of calcification (<50% or ≥50%), procedure-related parameters, complications, and success of procedures were recorded. Patients were equally divided into four groups and groups were compared according to patient demographic properties, procedure-related parameters, complications, and success.

RESULTS

The mean age and mean BMI of the study population were 55.5 years and 27.5 kg/m², respectively. The mean procedure time was 144.8 minutes (min) in group 1, 138.9 min in group 2, 122.2 min in group 3, and 101.1 min in group 4, and the difference was statistically shorter in favor of group 3 and group 4 (p=0.023). Moreover, mean fluoroscopy time significantly decreased after 20 cases (p=0.030). Hospitalization period was significantly shortened following 40 procedures (p=0.031). Complications were detected in five patients in group 1, four patients in group 2, and one patient in group 4 (p=0.044). Success was significantly higher in group 3 and group 4 in comparison to group 1 and group 2 (p=0.040).

CONCLUSION

This study showed that procedure time and hospitalization time significantly decreased after 40 cases and fluoroscopy time significantly decreased after 20 cases. Moreover, after 40 procedures, the success of Perclose ProGlide utilization during PCI significantly increased and complications of the procedure significantly decreased.

Long-term prognostic implications of hemodynamic and plaque assessment using coronary CT angiography

BACKGROUND AND AIMS

Hemodynamic and plaque characteristics can be analyzed using coronary CT angiography (CTA). We aimed to explore long-term prognostic implications of hemodynamic and plaque characteristics using coronary CT angiography (CTA).

METHODS

Invasive fractional flow reserve (FFR) and CTA-derived FFR (FFR_CT) were undertaken for 136 lesions in 78 vessels and followed-up to 10 years until December 2020. FFR_CT, wall shear stress (WSS), change in FFR_CT across the lesion (ΔFFR_CT), total plaque volume (TPV), percent atheroma volume (PAV), and low-attenuation plaque volume (LAPV) for target lesions [L] and vessels [V] were obtained by independent core laboratories. Their collective influence was evaluated for the clinical endpoints of target vessel failure (TVF) and target lesion failure (TLF).

RESULTS

During a median follow-up of 10.1 years, PAV[V] (per 10% increase, HR 2.32 [95% CI 1.11-4.86], p = 0.025), and FFR_CT[V] (per 0.1 increase, HR 0.56 [95% CI 0.37-0.84], p = 0.006) were independent predictors of TVF for the per-vessel analysis, and WSS[L] (per 100 dyne/cm² increase, HR 1.43 [1.09-1.88], p = 0.010), LAPV[L] (per 10 mm³ increase, HR 3.81 [1.16-12.5], p = 0.028), and ΔFFR_CT[L] (per 0.1 increase, HR 1.39 [1.02-1.90], p = 0.040) were independent predictors of TLF for the per-lesion analysis after adjustment for clinical and lesion characteristics. The addition of both plaque and hemodynamic predictors improved the predictability for 10-year TVF and TLF of clinical and lesion characteristics (all p < 0.05).

CONCLUSIONS

Vessel- and lesion-level hemodynamic characteristics, and vessel-level plaque quantity, and lesion-level plaque compositional characteristics assessed by CTA offer independent and additive long-term prognostic value.

Outcomes of non-ischaemic coronary lesions with high-risk plaque characteristics on coronary CT angiography

BACKGROUND

The integrative implications of quantitative and qualitative plaque characteristics on clinical outcomes and therapeutic guidance have not been fully investigated.

AIMS

We aimed to investigate the combined prognostic value of quantitative and qualitative plaque measures and their interactions with treatment modalities and physiological lesion severity.

METHODS

Among 697 vessels from 458 patients who underwent fractional flow reserve (FFR)-guided treatment, quantitative high-risk plaque (qn-HRP; plaque burden ≥70% and minimum lumen area <3.3 mm2) and qualitative HRP (ql-HRP; low-attenuation plaque or positive remodelling) were defined on coronary computed tomography angiography (CCTA). The primary endpoint was the vessel-oriented composite outcome (VOCO; a composite of cardiac death, myocardial infarction, or revascularisation).

RESULTS

The mean baseline FFR was 0.85±0.12, and 25.8% underwent percutaneous coronary intervention (PCI) during the index procedure. In medically treated lesions, both qn-HRP and ql-HRP were associated with an increased risk of VOCO (p<0.05). Relative to the lesions with qn-HRP(-)/ql-HRP(-),those with qn-HRP(+)/ql-HRP(+) showed a higher risk of VOCO (hazard ratio [HR] 8.36, 95% confidence interval [CI]: 2.86-24.44). The PCI group showed a lower risk for VOCO than the medical treatment group (HR 0.31, 95% CI: 0.11-0.91) in lesions with qn-HRP(+)/ql-HRP(+). This difference was consistent in lesions with an FFR of 0.81-0.90 (HR 0.19, 95 CI: 0.04-0.90), but not in those with an FFR of>0.90.

CONCLUSIONS

In non-ischaemic lesions, ql-HRP and qn-HRP showed a synergistic impact on risk assessment and had prognostic interactions with FFR and treatment modalities. Therefore, they need to be integrated into risk stratification and the optimisation of a treatment strategy.

CLINICALTRIALS

gov: NCT04037163.

PET for Detection and Reporting Coronary Microvascular Dysfunction: A JACC: Cardiovascular Imaging Expert Panel Statement

Angina pectoris and dyspnea in patients with normal or nonobstructive coronary vessels remains a diagnostic challenge. Invasive coronary angiography may identify up to 60% of patients with nonobstructive coronary artery disease (CAD), of whom nearly two-thirds may, in fact, have coronary microvascular dysfunction (CMD) that may account for their symptoms. Positron emission tomography (PET) determined absolute quantitative myocardial blood flow (MBF) at rest and during hyperemic vasodilation with subsequent derivation of myocardial flow reserve (MFR) affords the noninvasive detection and delineation of CMD. Individualized or intensified medical therapies with nitrates, calcium-channel blockers, statins, angiotensin-converting enzyme inhibitors, angiotensin II type 1-receptor blockers, beta-blockers, ivabradine, or ranolazine may improve symptoms, quality of life, and outcome in these patients. Standardized diagnosis and reporting criteria for ischemic symptoms caused by CMD are critical for optimized and individualized treatment decisions in such patients. In this respect, it was proposed by the cardiovascular council leadership of the Society of Nuclear Medicine and Molecular Imaging to convene thoughtful leaders from around the world to serve as an independent expert panel to develop standardized diagnosis, nomenclature and nosology, and cardiac PET reporting criteria for CMD. This consensus document aims to provide an overview of the pathophysiology and clinical evidence of CMD, its invasive and noninvasive assessment, standardization of PET-determined MBFs and MFR into "classical" (predominantly related to hyperemic MBFs) and "endogen" (predominantly related to resting MBF) normal coronary microvascular function or CMD that may be critical for diagnosis of microvascular angina, subsequent patient care, and outcome of clinical CMD trials.

Great debates in cardiac computed tomography: OPINION: "Artificial intelligence and the future of cardiovascular CT - Managing expectation and challenging hype"

This manuscript has been written as a follow-up to the "AI/ML great debate" featured at the 2021 Society of Cardiovascular Computed Tomography (SCCT) Annual Scientific Meeting. In debate style, we highlighti the need for expectation management of AI/ML, debunking the hype around current AI techniques, and countering the argument that in its current day format AI/ML is the "silver bullet" for the interpretation of daily clinical CCTA practice.

Deep learning-based detection of functionally significant stenosis in coronary CT angiography

Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.

Relationship of Plaque Features at Coronary CT to Coronary Hemodynamics and Cardiovascular Events

Background Plaque assessments with coronary CT angiography (CCTA) and coronary flow indexes have prognostic implications. Purpose To investigate the association and additive prognostic value of plaque burden and characteristics at CCTA with coronary pressure and flow. Materials and Methods Data of patients with coronary artery disease who underwent CCTA within 90 days before physiologic assessments at tertiary cardiovascular centers between January 2011 and December 2018 were retrospectively analyzed, which included fractional flow reserve (FFR), resting distal coronary artery pressure (Pd)-to-aortic pressure (Pa) ratio (hereafter, Pd/Pa), coronary flow reserve (CFR), hyperemic flow (1/hyperemic mean transit time [Tmn]), resting flow (1/resting Tmn), and index of microcirculatory resistance (IMR). Four high-risk plaque (HRP) attributes at CCTA defined high disease burden (plaque burden, ≥70%; minimum lumen area, <4 mm²) and adverse plaque (low-attenuation plaque, positive remodeling). Their lesion-specific relationships with coronary hemodynamic parameters and major adverse cardiovascular events (MACE) were investigated using a generalized estimating equation and marginal Cox model. Results Among 406 lesions from 335 patients (mean age, 67 years ± 10 [SD]; 259 men), high disease burden is predicted by FFR (odds ratio [OR], 0.55; P < .001), resting Pd/Pa (OR, 0.47; P < .001), CFR (OR, 0.85; P = .004), and hyperemic flow (OR, 0.91; P = .03), and adverse plaque by FFR (OR, 0.67; P < .001), resting Pd/Pa (OR, 0.69; P = .001), hyperemic flow (OR, 0.76; P = .006), resting flow (OR, 0.54; P = .001), and IMR (OR, 1.27; P = .008). High disease burden (hazard ratio [HR], 4.0; P = .004) and adverse plaque (HR, 2.7; P = .02) were associated with a higher risk of MACE (n = 27) over median 2.9-year follow-up. In six lesion subsets with normal flow or pressure, at least three HRP attributes predicted a higher MACE rate (HR range, 2.6-6.3). Conclusion High-risk plaque features and plaque burden at coronary CT angiography were associated with cardiovascular events independent of coronary hemodynamic parameters. Clinical trial registration no. NCT04037163 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Leipsic and Tzimas in this issue.

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

BACKGROUND

A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences.

OBJECTIVE

This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases.

METHODS

Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases.

RESULTS

For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion.

CONCLUSION

Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis

Background

Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images.

Methods

Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR.

Results

Fusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012).

Conclusion

CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT03298282].

Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome

Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

Artificial Intelligence Advances in the World of Cardiovascular Imaging

The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.

Effect of Coronary Disease Characteristics on Prognostic Relevance of Residual Ischemia After Stent Implantation

Objectives: We investigated the influence of coronary disease characteristics on prognostic implications of residual ischemia after coronary stent implantation.

Methods: This study included 1,476 patients with drug-eluting stent implantation and available pre- and post-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) measurements. Residual ischemia was defined as post-PCI FFR ≤ 0.80. Coronary disease characteristics with significant interaction hazard ratios (HRs) for clinical outcomes with residual ischemia were defined as interaction characteristics with residual ischemia (ICwRI). The primary outcome was target vessel failure (TVF)—a composite of cardiac death, target vessel myocardial infarction, and target vessel revascularization—at 2 years.

Results: The mean pre- and post-PCI FFR were 0.68 ± 0.11 and 0.87 ± 0.07, respectively. During the median follow-up duration of 2.0 years, the cumulative incidence of TVF was 6.1%. The 203 vessels (13.8%) with residual ischemia had higher risks of TVF compared to that for post-PCI FFR >0.80 (P < 0.001). ICwRI with a significant interaction HR with residual ischemia included pre-PCI SYNTAX score >17 and pre-PCI FFR ≤ 0.62. Each ICwRI had a direct prognostic effect not mediated by residual ischemia. The association between an increased TVF risk and residual ischemia was significant in patients with 0 or 1 ICwRI [hazard ratio (HR) 3.25, 95% confidence interval (CI) 1.90–5.57, P < 0.001] but not in those with 2 ICwRI (HR 0.47, 95% CI 0.14–1.64, P = 0.24). Among patients with post-PCI FFR >0.80, those with 2 ICwRI showed similar TVF risks to those with residual ischemia (HR 1.55, 95% CI 0.79–3.02, P = 0.20).

Conclusions: Coronary disease characteristics including pre-PCI SYNTAX score and pre-PCI FFR affected the prognostic implications of residual ischemia. The prognostic relevance of residual ischemia was attenuated in patients with multiple interacting characteristics.

Interactions Between Morphological Plaque Characteristics and Coronary Physiology: From Pathophysiological Basis to Clinical Implications

High-risk coronary plaque refers to a distinct set of plaque characteristics prone to future coronary events. Coronary physiology represents a group of indexes reflective of the local physiological environment and hemodynamic changes in the macrovascular and microvascular system. Although a large body of evidence has supported the clinical relevance of these 2 factors, currently, identifying plaque morphology cannot reliably capture the lesion subset that causes hard events. Also, the guideline-directed approach based on physiological indexes cannot fully predict and prevent clinical events. In parallel, there is accumulating evidence that these 2 aspects of coronary artery disease influence each other with significant clinical implications, despite traditionally being considered to have separate effects on significances, treatments, and outcomes. In this state-of-the-art review, we explore the clinical evidence of pathophysiological interplay of physiological indexes related to local hemodynamics, epicardial stenosis, and microvascular dysfunction with plaque morphological characteristics that provide a better understanding of the nature of coronary events. Furthermore, we examine the emerging data on the complementary role between plaque morphology and coronary physiology in prognostication and how to apply this concept to overcome the limitations of individual assessment alone. Finally, we propose the potential benefit of integrative assessment of coronary anatomy, plaque quantity and quality, and physiological aspects of a target lesion and vessels for personalized risk profiling and optimized treatment strategy.

Highlights of the 16th annual scientific meeting of the society of cardiovascular computed tomography

The 16th Society of Cardiovascular Computed Tomography (SCCT) annual scientific meeting welcomed 781 digital attendees from 55 countries. The program included 27 sessions across three simultaneously streaming channels, 11 exhibitors, 153 poster presentations, and 32 ​hours of on demand videos. The main themes of the meeting included coronary artery disease, valvular heart disease, structural heart disease, and advanced analytics including machine learning. This article summaries the main themes of the meeting and some of the key presentations, which will shape the future of cardiovascular computed tomography in clinical practice.

Prognostic Implications of Comprehensive Whole Vessel Plaque Quantification Using Coronary Computed Tomography Angiography

BACKGROUND

The prognostic value of whole vessel plaque quantification has not been fully understood.

OBJECTIVES

We aimed to investigate the clinical relevance of whole vessel plaque quantification on coronary computed tomography angiography.

METHODS

In a total of 1,013 vessels with fractional flow reserve (FFR) measurement and available coronary computed tomography angiography, high-risk plaque characteristics (HRPC) included minimum lumen area <4 mm², plaque burden ≥70%, low attenuation plaque, positive remodeling, spotty calcification, and napkin-ring sign; and high-risk vessel characteristics (HRVC) included total plaque volume ≥306.5 mm³, fibrofatty and necrotic core volume ≥4.46 mm³, or percent total atheroma volume ≥32.2% in a target vessel, based on corresponding optimal cutoff values. Survival analysis for vessel-oriented composite outcome (VOCO) (a composite of cardiac death, target vessel myocardial infarction, or target vessel revascularization) at 5 years was performed using marginal Cox proportional hazard models.

RESULTS

Whole vessel plaque quantification had incremental predictability in addition to % diameter stenosis and HRPC (P < 0.001) in predicting FFR ≤0.80. Among 517 deferred vessels based on FFR >0.80, the number of HRVC was significantly associated with the risk of VOCO (HR: 2.54; 95% CI: 1.77-3.64) and enhanced the predictability for VOCO of % diameter stenosis and the number of HRPC (P < 0.001). In a landmark analysis at 2 years, the number of HRVC showed sustained prognostic implications beyond 2 years, but the number of HRPC did not.

CONCLUSIONS

Whole vessel plaque quantification can provide incremental predictability for low FFR and additive prognostic value in deferred vessels with high FFR over anatomical severity and lesion plaque characteristics. (CCTA-FFR Registry for Risk Prediction; NCT04037163).

High-Risk Morphological and Physiological Coronary Disease Attributes as Outcome Markers After Medical Treatment and Revascularization

OBJECTIVES

This study sought to evaluate the prognostic impact of plaque morphology and coronary physiology on outcomes after medical treatment or percutaneous coronary intervention (PCI).

BACKGROUND

Although fractional flow reserve (FFR) is currently best practice, morphological characteristics of coronary artery disease also contribute to outcomes.

METHODS

A total of 872 vessels in 538 patients were evaluated by invasive FFR and coronary computed tomography angiography. High-risk attributes (HRA) were defined as high-risk physiological attribute (invasive FFR ≤0.8) and high-risk morphological attributes including: 1) local plaque burden (minimum lumen area <4 mm² and plaque burden ≥70%); 2) adverse plaque characteristics ≥2; and 3) global plaque burden (total plaque volume ≥306.5 mm³ and percent atheroma volume ≥32.2%). The primary outcome was the composite of revascularization, myocardial infarction, or cardiac death at 5 years.

RESULTS

The mean FFR was 0.88 ± 0.08, and PCI was performed in 239 vessels. The primary outcome occurred in 54 vessels (6.2%). All high-risk morphological attributes were associated with the increased risk of adverse outcomes after adjustment for FFR ≤0.8 and demonstrated direct prognostic effect not mediated by FFR ≤0.8. The 5-year event risk proportionally increased as the number of HRA increased (p for trend <0.001) with lower risk in the PCI group than the medical treatment group in vessels with 1 or 2 HRA (9.7% vs. 14.7%), but not in vessels with either 0 or ≥3 HRA. Of the vessels with pre-procedural FFR ≤0.8, ischemia relief by PCI (pre-PCI FFR ≤0.8 and post-PCI FFR >0.8) significantly reduced vessel-oriented composite outcome risk compared with medical treatment alone in vessels with 0 or 1 high-risk morphological attributes (hazard ratio: 0.33; 95% confidence interval: 0.12 to 0.93; p = 0.035), but the risk reduction was attenuated in vessels with ≥2 high-risk morphological attributes.

CONCLUSIONS

High-risk morphological attributes offered additive prognostic value to coronary physiology and may optimize selection of treatment strategies by adding to FFR-based risk predictions (CCTA-FFR Registry for Development of Comprehensive Risk Prediction Model; NCT04037163).