The Role of Multimodality Imaging (CT & MR) as a Guide to the Management of Chronic Coronary Syndromes.

Chronic coronary syndrome (CCS) is one of the leading cardiovascular causes of morbidity, mortality, and use of medical resources. After the introduction by international guidelines of the same level of recommendation to non-invasive imaging techniques in CCS evaluation, a large debate arose about the dilemma of choosing anatomical (with coronary computed tomography angiography (CCTA)) or functional imaging (with stress echocardiography (SE), cardiovascular magnetic resonance (CMR), or nuclear imaging techniques) as a first diagnostic evaluation. The determinant role of the atherosclerotic burden in defining cardiovascular risk and prognosis more than myocardial inducible ischemia has progressively increased the use of a first anatomical evaluation with CCTA in a wide range of pre-test probability in CCS patients. Functional testing holds importance, both because the role of revascularization in symptomatic patients with proven ischemia is well defined and because functional imaging, particularly with stress cardiac magnetic resonance (s-CMR), gives further prognostic information regarding LV function, detection of myocardial viability, and tissue characterization. Emerging techniques such as stress computed tomography perfusion (s-CTP) and fractional flow reserve derived from CT (FFRCT), combining anatomical and functional evaluation, appear capable of addressing the need for a single non-invasive examination, especially in patients with high risk or previous revascularization. Furthermore, CCTA in peri-procedural planning is promising to acquire greater importance in the non-invasive planning and guiding of complex coronary revascularization procedures, both by defining the correct strategy of interventional procedure and by improving patient selection. This review explores the different roles of non-invasive imaging techniques in managing CCS patients, also providing insights into preoperative planning for percutaneous or surgical myocardial revascularization.

DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. METHODS: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. RESULTS: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. CONCLUSIONS: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Dynamic trend of lung fluid movement during exercise in heart failure: From lung imaging to alveolar-capillary membrane function.

BACKGROUND: In chronic heart failure (HF), exercise-induced increase in pulmonary capillary pressure may cause an increase of pulmonary congestion, or the development of pulmonary oedema. We sought to assess in HF patients the exercise-induced intra-thoracic fluid movements, by measuring plasma brain natriuretic peptide (BNP), lung comets and lung diffusion for carbon monoxide (DLCO) and nitric oxide (DLNO), as markers of hemodynamic load changes, interstitial space and alveolar-capillary membrane fluids, respectively. METHODS AND RESULTS: Twenty-four reduced ejection fraction HF patients underwent BNP, lung comets and DLCO/DLNO measurements before, at peak and 1 h after the end of a maximal cardiopulmonary exercise test. BNP significantly increased at peak from 549 (328-841) to 691 (382-1207, p < 0.0001) pg/mL and almost completely returned to baseline value 1 h after exercise. Comets number increased at peak from 9.4 ± 8.2 to 24.3 ± 16.7, returning to baseline (9.7 ± 7.4) after 1 h (p < 0.0001). DLCO did not change significantly at peak (from 18.01 ± 4.72 to 18.22 ± 4.73 mL/min/mmHg), but was significantly reduced at 1 h (16.97 ± 4.26 mL/min/mmHg) compared to both baseline (p = 0.0211) and peak (p = 0.0174). DLNO showed a not significant trend toward lower values 1 h post-exercise. CONCLUSIONS: Moderate/severe HF patients have a 2-step intra-thoracic fluid movement with exercise: the first during active exercise, from the vascular space toward the interstitial space, as confirmed by comets increase, without any effect on diffusion, and the second, during recovery, toward the alveolar-capillary membrane, clearing the interstitial space but worsening gas diffusion.

A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.

Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients.

Circulating small extracellular vesicles (sEVs) contribute to inflammation, coagulation and vascular injury, and have great potential as diagnostic markers of disease. The ability of sEVs to reflect myocardial damage assessed by Cardiac Magnetic Resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) is unknown. To fill this gap, plasma sEVs were isolated from 42 STEMI patients treated by primary percutaneous coronary intervention (pPCI) and evaluated by CMR between days 3 and 6. Nanoparticle tracking analysis showed that sEVs were greater in patients with anterior STEMI (p = 0.0001), with the culprit lesion located in LAD (p = 0.045), and in those who underwent late revascularization (p = 0.038). A smaller sEV size was observed in patients with a low myocardial salvage index (MSI, p = 0.014). Patients with microvascular obstruction (MVO) had smaller sEVs (p < 0.002) and lower expression of the platelet marker CD41-CD61 (p = 0.039). sEV size and CD41-CD61 expression were independent predictors of MVO/MSI (OR [95% CI]: 0.93 [0.87-0.98] and 0.04 [0-0.61], respectively). In conclusion, we provide evidence that the CD41-CD61 expression in sEVs reflects the CMR-assessed ischemic damage after STEMI. This finding paves the way for the development of a new strategy for the timely identification of high-risk patients and their treatment optimization.

Does the AVNeo valve reduce wall stress on the aortic wall? A cardiac magnetic resonance analysis with 4D-flow for the evaluation of aortic valve replacement with the Ozaki technique.

OBJECTIVES: Aortic valve neocuspidalization aims to replace the 3 aortic cusps with autologous pericardium pre-treated with glutaraldehyde, and it is a surgical alternative to the classical aortic valve replacement (AVR). Image-based patient-specific computational fluid dynamics allows the derivation of shear stress on the aortic wall [wall shear stress (WSS)]. Previous studies support a potential link between increased WSS and histological alterations of the aortic wall. The aim of this study is to compare the WSS of the ascending aorta in patients undergoing aortic valve neocuspidalization versus AVR with biological prostheses. METHODS: This is a prospective nonrandomized clinical trial. Each patient underwent a 4D-flow cardiac magnetic resonance scan after surgery, which informed patient-specific computational fluid dynamics models to evaluate WSS at the ascending aortic wall. The adjusted variables were calculated by summing the residuals obtained from a multivariate linear model (with ejection fraction and left ventricle outflow tract-aorta angle as covariates) to the mean of the variables. RESULTS: Ten patients treated with aortic valve neocuspidalization were enrolled and compared with 10 AVR patients. The aortic valve neocuspidalization group showed a significantly lower WSS in the outer curvature segments of the proximal and distal ascending aorta as compared to AVR patients (P = 0.0179 and 0.0412, respectively). WSS levels remained significantly lower along the outer curvature of the proximal aorta in the aortic valve neocuspidalization population, even after adjusting the WSS for the ejection fraction and the left ventricle outflow tract-aorta angle [2.44 Pa (2.17-3.01) vs 1.94 Pa (1.72-2.01), P = 0.02]. CONCLUSIONS: Aortic valve neocuspidalization hemodynamical features are potentially associated with a lower WSS in the ascending aorta as compared to commercially available bioprosthetic valves.

Early Biological Valve Failure: Structural Valve Degeneration, Thrombosis, or Endocarditis?

Biological valve failure (BVF) is an inevitable condition that compromises the durability of biological heart valves (BHVs). It stems from various causes, including rejection, thrombosis, and endocarditis, leading to a critical state of valve dysfunction. Echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging play pivotal roles in the diagnostic multimodality workup of BVF. By providing a comprehensive overview of the pathophysiology of BVF and the diagnostic approaches in different clinical scenarios, this review aims to aid clinicians in their decision-making process. The significance of early detection and appropriate management of BVF cannot be overstated, as these directly impact patients' prognosis and their overall quality of life. Ensuring timely intervention and tailored treatments will not only improve outcomes but also alleviate the burden of this condition on patients' life. By prioritizing comprehensive assessments and adopting the latest advancements in diagnostic technology, medical professionals can significantly enhance their ability to manage BVF effectively.

A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography.

Introduction: Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods: Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results: Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion: These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.

Cardiac computed tomography: from anatomy to function.

Ischaemic heart disease (IHD) is one of the world's leading causes of morbidity and mortality. Likewise, the diagnosis and risk stratification of patients with coronary artery disease (CAD) have always been based on the detection of the presence and extent of ischaemia by physical or pharmacological stress tests with or without the aid of imaging methods (e.g. exercise stress, test, stress echocardiography, single-photon emission computed tomography, or stress cardiac magnetic resonance). These methods show high performance to assess obstructive CAD, whilst they do not show accurate power to detect non-obstructive CAD. The introduction into clinical practice of coronary computed tomography angiography, the only non-invasive method capable of analyzing the coronary anatomy, allowed to add a crucial piece in the puzzle of the assessment of patients with suspected or chronic IHD. The current review evaluates the technical aspects and clinical experience of coronary computed tomography in the evaluation of atherosclerotic burden with a special focus about the new emerging application such as functional relevance of CAD with fractional flow reserve computed tomography (CT)-derived (FFRct), stress CT perfusion, and imaging inflammatory makers discussing the strength and weakness of each approach.

Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy.

BACKGROUND: Cardiac computed tomography (CCT) was recently validated to measure extracellular volume (ECV) in the setting of cardiac amyloidosis, showing good agreement with cardiovascular magnetic resonance (CMR). However, no evidence is available with a whole-heart single source, single energy CT scanner in the clinical context of newly diagnosed left ventricular dysfunction. Therefore, the aim of this study was to test the diagnostic accuracy of ECVCCT in patients with a recent diagnosis of dilated cardiomyopathy, having ECVCMR as the reference technique. METHODS: 39 consecutive patients with newly diagnosed dilated cardiomyopathy (LVEF <50%) scheduled for clinically indicated CMR were prospectively enrolled. Myocardial segment evaluability assessment with each technique, agreement between ECVCMR and ECVCCT, regression analysis, Bland-Altman analysis and interclass correlation coefficient (ICC) were performed. RESULTS: Mean age of enrolled patients was 62 â€‹± â€‹11 years, and mean LVEF at CMR was 35.4 â€‹± â€‹10.7%. Overall radiation exposure for ECV estimation was 2.1 â€‹± â€‹1.1 â€‹mSv. Out of 624 myocardial segments available for analysis, 624 (100%) segments were assessable by CCT while 608 (97.4%) were evaluable at CMR. ECVCCT demonstrated slightly lower values compared to ECVCMR (all segments, 31.8 â€‹± â€‹6.5% vs 33.9 â€‹± â€‹8.0%, p â€‹< â€‹0.001). At regression analysis, strong correlations were described (all segments, r â€‹= â€‹0.819, 95% CI: 0.791 to 0.844). On Bland-Altman analysis, bias between ECVCMR and ECVCCT for global analysis was 2.1 (95% CI: -6.8 to 11.1). ICC analysis showed both high intra-observer and inter-observer agreement for ECVCCT calculation (0.986, 95%CI: 0.983 to 0.988 and 0.966, 95%CI: 0.960 to 0.971, respectively). CONCLUSIONS: ECV estimation with a whole-heart single source, single energy CT scanner is feasible and accurate. Integration of ECV measurement in a comprehensive CCT evaluation of patients with newly diagnosed dilated cardiomyopathy can be performed with a small increase in overall radiation exposure.

Dramatic disease regression in a case of HFrEF with end-stage renal failure treated with sacubitril/valsartan and SGLT2i.

The amount of evidence for guideline-directed new heart failure (HFrEF) disease-modifying drugs in the context of chronic kidney disease (CKD) is relatively modest, especially in end-stage CKD. We report a case of dramatic reverse remodelling and disease regression in a naïve HFrEF young woman on haemodialysis treated with sacubitril/valsartan and SGLT2i. At 10-month follow-up, the patient normalized left ventricle and atrial volumes and improved ejection fraction to the normal range, assessed both by echocardiography and cardiac magnetic resonance. Cardiac biomarkers and exercise performance improved consensually. The haemodialysis protocol and the loop diuretic dose were unchanged within the whole period.

Value of 3D echocardiography in the diagnosis of arrhythmogenic right ventricular cardiomyopathy.

AIMS: The 2010 Task Force Criteria (TFC) require that both right ventricular (RV) regional wall-motion abnormalities (WMA) and specific RV size cut-offs be met in order to fulfil one of the major criterion for arrhythmogenic right ventricular cardiomyopathy (ARVC) diagnosis. Currently, 2D echocardiography (2DE) and cardiovascular magnetic resonance imaging (cMRI) are used to determine if these criteria are met. Little is known about the diagnostic value of 3D echocardiography (3DE) in ARVC. The aim of this study was to determine whether a combination of 2DE-3DE is non-inferior to the currently used 2DE-cMRI combination in the diagnosis of patients with ARVC. METHODS AND RESULTS: Thirty-nine individuals (47±15 years) with suspected ARVC underwent evaluation of the RV with cMRI, 2DE, and 3DE. 3DE and cMRI were independently used to obtain RV volumes, ejection fraction (EF) and determine the presence of segmental RV WMA. Studies were blindly classified as meeting criteria for ARVC in accordance with the 2010 TFC. Kappa statistics were used to test the concordance between 2DE-cMRI and 2DE-3DE approaches. Using the 2DE-cMRI approach, 3/39 were not affected, 5/39 possible, 8/39 borderline, and 23/39 definite ARVC. The proposed 2DE-3DE approach yielded 5/39 not affected, 7/39 possible, 8/39 borderline, and 19/39 definite diagnoses. The two approaches were highly concordant (k = 0.71; 95% confidence interval: 0.44-0.84). Although 3DE underestimated RV volumes in comparison with cMRI, interfering, in some instances with the fulfilment of a major criterion, it was able to identify more RV WMA (28/39) than 2DE (11/39), with a detection-rate comparable to cMRI (33/39) highlighting a unique advantage. CONCLUSION: The combination of 2DE-3DE for ARVC diagnosis is comparable to the conventional 2DE-cMRI approach. 3DE should be performed in all suspected ARVC patients to aide in the detection of WMA.

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

BACKGROUND: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS: In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS: Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension.

BACKGROUND: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)-based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. METHODS: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. RESULTS: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. CONCLUSIONS: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

PURPOSE: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. METHODS: One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. RESULTS: Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). CONCLUSION: Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..

Changing Paradigms in the Diagnosis of Ischemic Heart Disease by Multimodality Imaging.

Coronary artery disease (CAD) represents the most common cardiovascular disease, with high morbidity and mortality. Historically patients with chest pain of suspected coronary origin have been assessed with functional tests, capable to detect haemodynamic consequences of coronary obstructions through depiction of electrocardiographic changes, myocardial perfusion defects or regional wall motion abnormalities under stress condition. Stress echocardiography (SE), single-photon emission computed tomography (SPECT), positron emission tomography (PET) and cardiovascular magnetic resonance (CMR) represent the functional techniques currently available, and technical developments contributed to increased diagnostic performance of these techniques. More recently, cardiac computed tomography angiography (cCTA) has been developed as a non-invasive anatomical test for a direct visualisation of coronary vessels and detailed description of atherosclerotic burden. Cardiovascular imaging techniques have dramatically enhanced our knowledge regarding physiological aspects and myocardial implications of CAD. Recently, after the publication of important trials, international guidelines recognised these changes, updating indications and level of recommendations. This review aims to summarise current standards with main novelties and specific limitations, and a diagnostic algorithm for up-to-date clinical management is also proposed.

Is EuroSCORE II still a reliable predictor for cardiac surgery mortality in 2022? A retrospective study study.

OBJECTIVES: The European System for Cardiac Operation Risk Evaluation II (EuroSCORE II) is the most common tool used to evaluate the perioperative risk of mortality after cardiac surgery in Europe, and its use is currently recommended by the relevant guidelines. However, recently, its role has been questioned: Several papers have suggested that these algorithms may no longer be adequate for risk prediction due to an overestimation of adult cardiac surgical risk. Our goal was to validate the EuroSCORE II in the prediction of 30-day in-hospital mortality in patients undergoing open cardiac surgery in a high-volume hospital. METHODS: In this retrospective cohort study, we included all patients who underwent cardiac surgery from January 2016 to May 2022 within the departments of cardiac surgery of the Monzino Cardiology Centre in Milan, Italy. We evaluated the discrimination power of the EuroSCORE II by using the receiver operating characteristic curve and the corresponding area under the curve. We performed calibration plots to assess the concordance between the model's prediction and the observed outcomes. RESULTS: A total of 4,034 patients were included (mean age = 65.1 years; 68% males), of which 674 (16.7%) underwent isolated coronary artery bypass grafting. The EuroSCORE II showed a good discrimination power in predicting 30-day in-hospital mortality (area under the curve = 0.834). However, for interventions performed in an elective setting, very low values of the EuroSCORE II overestimated the observed mortality, whereas for interventions performed in an emergency setting, EuroSCORE II values above 10 extensively underestimated the observed mortality. CONCLUSIONS: Our study suggests that the EuroSCORE II seems not to be a reliable score in estimating the true risk of death, especially in high-risk patients.

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management.

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of "big data." AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.