Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients.

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field

ESC core curriculum for the general cardiologist (2013)

[No abstract available

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

Prognostic implications of left ventricular global longitudinal strain in heart failure patients with narrow QRS complex treated with cardiac resynchronization therapy: a subanalysis of the randomized EchoCRT trial

Aim: Left ventricular (LV) global longitudinal strain (GLS) reflects LV systolic function and correlates inversely with the extent of LV myocardial scar and fibrosis. The present subanalysis of the Echocardiography Guided CRT trial investigated the prognostic value of LV GLS in patients with narrow QRS complex. Methods and results: Left ventricular (LV) global longitudinal strain (GLS) was measured on the apical 2-, 4- and 3-chamber views using speckle tracking analysis. Measurement of baseline LV GLS was feasible in 755 patients (374 with cardiac resynchronization therapy (CRT)-ON and 381 with CRT-OFF). The median value of LV GLS in the overall population was 7.9%, interquartile range 6.2–10.1%. After a mean follow-up period of 19.4 months, 95 patients in the CRT-OFF group and 111 in the CRT-ON group reached the combined primary endpoint of all-cause mortality and heart failure hospitalization. Each 1% absolute unit decrease in LV GLS was independently associated with 11% increase in the risk to reach the primary endpoint (Hazard ratio 1.11; 95% confidence interval 95% 1.04–1.17, P < 0.001), after adjusting for ischaemic cardiomyopathy and randomization treatment among other clinically relevant variables. When categorizing patients according to quartiles of LV GLS, the primary endpoint occurred more frequently in patients in the lowest quartile (<6.2%) treated with CRT-ON vs. CRT-OFF (45.6% vs. 28.7%, P = 0.009) whereas, no differences were observed in patients with LV GLS ≥6.2% treated with CRT-OFF vs. CRT-ON (23.7% vs. 24.5%, respectively; P  = 0.62). Conclusion: Low LV GLS is associated with poor outcome in heart failure patients with QRS width <130 ms, independent of randomization to CRT or not. Importantly, in the group of patients with the lowest LV GLS quartile, CRT may have a detrimental effect on clinical outcomes

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Agreement of 2D transthoracic echocardiography with cardiovascular magnetic resonance imaging after ST-elevation myocardial infarction

Background: This study was designed to investigate the agreement of 2D transthoracic echocardiography (2D TTE) with cardiovascular magnetic resonance imaging (CMR) in a contemporary population of ST-elevation myocardial infarction (STEMI) patients. Methods: In this subanalysis of the GIPS-III trial, a randomized controlled trial investigating the administration of metformin in STEMI patients to prevent reperfusion injury, we studied 259 patients who underwent same-day CMR and 2D TTE assessments four months after hospitalization for a first STEMI. Bland-Altman analyses were performed to assess agreement between LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV ejection fraction (LVEF), and LV mass measurements. Sensitivity and specificity of 2D TTE to detect categories of LVEF (≤35%, 35–50%, ≥50%) was determined. Linear regression of absolute differences in measurements between imaging modalities was used to investigate whether patient characteristics impact measurement bias. Results: Pairwise difference (bias) and 95% limits of agreement between CMR and 2D TTE measurements were +84 (37, 147) ml for LVEDV, +39 (6, 85) ml for LVESV, -1.1 ± 13.5% for LVEF, and -75 (-154, -14) g for LV mass. Sensitivity and specificity of 2D TTE to detect subjects with moderately depressed LVEF (35–50%) as measured by CMR were 52% and 88% respectively. We observed a significant effect of enzymatic infarct size on bias between 2D TTE and CMR in measuring LVESV and LVEF (P = 0.029, P = 0.001 respectively), of age and sex on bias between 2D TTE and CMR in measuring LV mass (P = 0.027, P < 0.001) and LVEDV (P = 0.001, P = 0.039), and of heart rate on bias between 2D TTE and CMR in LV volume measurements (P = 0.004, P = 0.016). Conclusions: Wide limits of agreement, underestimation of LV volumes and overestimation of LV mass was observed when comparing 2D TTE to CMR. Enzymatic infarct size, age, sex, and heart rate are potential sources of bias between imaging modalities