AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry

Scar; MRI; Arrhythmic eventsCicatriu; Ressonància magnètica; Esdeveniments arítmicsCicatriz; Resonancia magnética; Eventos arrítmicosBackground Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63, P = .01, respectively). Modeling with competing risks yielded for dense and total scar (AUC: 0.67 vs 0.61, P = .01 and AUC: 0.66 vs 0.61, P = .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning–based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT0335264

Cardiac Magnetic Resonance as Risk Stratification Tool in Non-Ischemic Dilated Cardiomyopathy Referred for Implantable Cardioverter Defibrillator Therapy—State of Art and Perspectives

Non-ischemic dilated cardiomyopathy (DCM) is a disease characterized by left ventricular dilation and systolic dysfunction. Patients with DCM are at higher risk for ventricular arrhythmias and sudden cardiac death (SCD). According to current international guidelines, left ventricular ejection fraction (LVEF) <= 35% represents the main indication for prophylactic implantable cardioverter defibrillator (ICD) implantation in patients with DCM. However, LVEF lacks sensitivity and specificity as a risk marker for SCD. It has been seen that the majority of patients with DCM do not actually benefit from the ICD implantation and, on the contrary, that many patients at risk of SCD are not identified as they have preserved or mildly depressed LVEF. Therefore, the use of LVEF as unique decision parameter does not maximize the benefit of ICD therapy. Multiple risk factors used in combination could likely predict SCD risk better than any single risk parameter. Several predictors have been proposed including genetic variants, electric indexes, and volumetric parameters of LV. Cardiac magnetic resonance (CMR) can improve risk stratification thanks to tissue characterization sequences such as LGE sequence, parametric mapping, and feature tracking. This review evaluates the role of CMR as a risk stratification tool in DCM patients referred for ICD

Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome

Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging

Assessment of coronary artery disease and calcified coronary plaque burden by computed tomography in patients with and without diabetes mellitus

Purpose: To compare the coronary atherosclerotic burden in patients with and without type-2 diabetes using CT Coronary Angiography (CTCA). Methods and Materials: 147 diabetic (mean age: 65 ± 10 years; male: 89) and 979 nondiabetic patients (mean age: 61 ± 13 years; male: 567) without a history of coronary artery disease (CAD) underwent CTCA. The per-patient number of diseased coronary segments was determined and each diseased segment was classified as showing obstructive lesion (luminal narrowing >50%) or not. Coronary calcium scoring (CCS) was assessed too. Results: Diabetics showed a higher number of diseased segments (4.1 ± 4.2 vs. 2.1 ± 3.0; p 400 (p < 0.001), obstructive CAD (37% vs. 18% of patients; p < 0.0001), and fewer normal coronary arteries (20% vs. 42%; p < 0.0001), as compared to nondiabetics. The percentage of patients with obstructive CAD paralleled increasing CCS in both groups. Diabetics with CCS ≤ 10 had a higher prevalence of coronary plaque (39.6% vs. 24.5%, p = 0.003) and obstructive CAD (12.5% vs. 3.8%, p = 0.01). Among patients with CCS ≤ 10 all diabetics with obstructive CAD had a zero CCS and one patient was asymptomatic. Conclusions: Diabetes was associated with higher coronary plaque burden. The present study demonstrates that the absence of coronary calcification does not exclude obstructive CAD especially in diabetics

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 &lt; 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 &lt; 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.

Prognostic relevance of comprehensive non-invasive imaging approach in a diabetic and non-diabetic asymptomatic population

Backgroud: The aim of the study was to evaluate the incremental prognostic benefit of carotid artery disease and subclinical coronary artery disease (CAD) features in addition to clinical evaluation in a diabetic and non-diabetic asymptomatic population. Methods: Over a six-year period, 10-year-FRS together with carotid ultrasound (CUS) and coronary computed tomography angiography (CCTA) were evaluated for the prediction of major adverse cardiac events (MACE). Results: Five-hundred-seventeen consecutive patients were enrolled in the study, including 328 (63%) male with a mean age of 64±10 (SD). No diabetic patients were 426 (82.4%) and diabetic patients were 91 (17.6). Mean CACS was 16 [SD 0-88] in non-diabetic patients and 64 [SD 0-166] in diabetic patients (p&lt;0.001). The patients with presence of CAD≥50% were 143 (27.7%), whom 105 (24.7%) non-diabetic and 38 (41.8%) diabetic (p=0.001). Over a median follow-up of 4.4 [3.4-5.1] years there were a total of 53 CHD events (10%) including 6 cardiac deaths (1.2%), 13 non-fatal myocardial infarction (2.5%), and 34 non ST elevation myocardial infarction (6.5%). Total events were 37 (7.1%) in non-diabetic population and 16 (17.6%) in diabetic population. The mean radiation dose during CCTA was 4.3±1.0 mSv with no difference between the two groups. The univariable analysis (Table 2) showed that hyperlipidemia, aspirin, carotid plaque, carotid disease, CAD ≥70%, % of segments with non calcific plaque, % of segments with mixed plaque, % of segments with remodeled plaque, and CACS was significant predictors of CHD events in non-diabetic population. Differently, in diabetic population only statins and % of segments with remodeled plaque were significant predictors of CHD events, while % of segments with mixed plaque did not reach statistical significance. On multivariable analysis in non-diabetic group, carotid disease was a significant independent predictor of CHD events when added to FRS (C-statistic, 95% CI: 0.62, 0.55-0.68; p=0.037). CACS were independent predictor when added to both FRS and carotid disease (C-statistic, 95% CI: 0.66, 0.60-0.73; p=0.016). CUS and CACS data were no more significant when CCTA parameters were included in the model, with the latter being the only significant independent predictors. In particular, % of segment with remodeled plaque was incremental independent predictor even when added to a model including the presence of CAD ≥70% (C-statistic, 95% CI: 0.84, 0.80-0.88; p&lt;0.001). In diabetic group % of segments with remodeled plaque represented the only independent predictor of CHD events (C- statistic, 95% CI: 0.83, 0.77-0.87; p&lt;0.001). Conclusions: In an asymptomatic at-risk population carotid disease assessment is able to predict MACE occurrence more accurately than traditional clinical scores and comparable to coronary calcium score. Coronary artery stenosis and plaque positive remodeling represent the most powerful tools of risk reclassification of this wide subset of patients. In the subgroup of diabetic subjects, the percentage of segments with remodeled plaque is the only