Septic cardiomyopathy: From pathophysiology to the clinical setting

The onset of cardiomyopathy is a common feature in sepsis, with relevant effects on its pathophysiology and clinical care. Septic cardiomyopathy is characterized by reduced left ventricular (LV) contractility eventually associated with LV dilatation with or without right ventricle failure. Unfortunately, such a wide range of ultrasonographic findings does not reflect a deep comprehension of sepsis-induced cardiomyopathy, but rather a lack of consensus about its definition. Several echocardiographic parameters intrinsically depend on loading conditions (both preload and afterload) so that it may be challenging to discriminate which is primitive and which is induced by hemodynamic perturbances. Here, we explore the state of the art in sepsis-related cardiomyopathy. We focus on the shortcomings in its definition and point out how cardiac performance dynamically changes in response to different hemodynamic clusters. A special attention is also given to update the knowledge about molecular mechanisms leading to myocardial dysfunction and that recall those of myocardial hibernation. Ultimately, the aim of this review is to highlight the unsolved issue in the field of sepsis-induced cardiomyopathy as their implementation would lead to improve risk stratification and clinical care

Deep learning supplants visual analysis by experienced operators for the diagnosis of cardiac amyloidosis by cine-CMR

BACKGROUND: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. METHOD: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. RESULTS: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 ( CONCLUSION: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis

Classification of cardiomyopathies from MR cine images using convolutional neural network with transfer learning

The automatic classification of various types of cardiomyopathies is desirable but has never been performed using a convolutional neural network (CNN). The purpose of this study was to evaluate currently available CNN models to classify cine magnetic resonance (cine-MR) images of cardiomyopathies. METHOD: Diastolic and systolic frames of 1200 cine-MR sequences of three categories of subjects (395 normal, 411 hypertrophic cardiomyopathy, and 394 dilated cardiomyopathy) were selected, preprocessed, and labeled. Pretrained, fine-tuned deep learning models (VGG) were used for image classification (sixfold cross-validation and double split testing with hold-out data). The heat activation map algorithm (Grad-CAM) was applied to reveal salient pixel areas leading to the classification. RESULTS: The diastolic-systolic dual-input concatenated VGG model cross-validation accuracy was 0.982 ± 0.009. Summed confusion matrices showed that, for the 1200 inputs, the VGG model led to 22 errors. The classification of a 227-input validation group, carried out by an experienced radiologist and cardiologist, led to a similar number of discrepancies. The image preparation process led to 5% accuracy improvement as compared to nonprepared images. Grad-CAM heat activation maps showed that most misclassifications occurred when extracardiac location caught the attention of the network. CONCLUSIONS: CNN networks are very well suited and are 98% accurate for the classification of cardiomyopathies, regardless of the imaging plane, when both diastolic and systolic frames are incorporated. Misclassification is in the same range as inter-observer discrepancies in experienced human readers

Epicardial fat and atrial fibrillation: the perils of atrial failure

International audienceAbstract Obesity is a heterogeneous condition, characterized by different phenotypes and for which the classical assessment with body mass index may underestimate the real impact on cardiovascular (CV) disease burden. An epidemiological link between obesity and atrial fibrillation (AF) has been clearly demonstrated and becomes even more tight when ectopic (i.e. epicardial) fat deposition is considered. Due to anatomical and functional features, a tight paracrine cross-talk exists between epicardial adipose tissue (EAT) and myocardium, including the left atrium (LA). Alongside—and even without—mechanical atrial stretch, the dysfunctional EAT may determine a pro-inflammatory environment in the surrounding myocardial tissue. This evidence has provided a new intriguing pathophysiological link with AF, which in turn is no longer considered a single entity but rather the final stage of atrial remodelling. This maladaptive process would indeed include structural, electric, and autonomic derangement that ultimately leads to overt disease. Here, we update how dysfunctional EAT would orchestrate LA remodelling. Maladaptive changes sustained by dysfunctional EAT are driven by a pro-inflammatory and pro-fibrotic secretome that alters the sinoatrial microenvironment. Structural (e.g. fibro-fatty infiltration) and cellular (e.g. mitochondrial uncoupling, sarcoplasmic reticulum fragmentation, and cellular protein quantity/localization) changes then determine an electrophysiological remodelling that also involves the autonomic nervous system. Finally, we summarize how EAT dysfunction may fit with the standard guidelines for AF. Lastly, we focus on the potential benefit of weight loss and different classes of CV drugs on EAT dysfunction, LA remodelling, and ultimately AF onset and recurrence

Epicardial fat and atrial fibrillation: the perils of atrial failure

reserved6si: Obesity is a heterogeneous condition, characterized by different phenotypes and for which the classical assessment with body mass index may underestimate the real impact on cardiovascular (CV) disease burden. An epidemiological link between obesity and atrial fibrillation (AF) has been clearly demonstrated and becomes even more tight when ectopic (i.e. epicardial) fat deposition is considered. Due to anatomical and functional features, a tight paracrine cross-talk exists between epicardial adipose tissue (EAT) and myocardium, including the left atrium (LA). Alongside-and even without-mechanical atrial stretch, the dysfunctional EAT may determine a pro-inflammatory environment in the surrounding myocardial tissue. This evidence has provided a new intriguing pathophysiological link with AF, which in turn is no longer considered a single entity but rather the final stage of atrial remodelling. This maladaptive process would indeed include structural, electric, and autonomic derangement that ultimately leads to overt disease. Here, we update how dysfunctional EAT would orchestrate LA remodelling. Maladaptive changes sustained by dysfunctional EAT are driven by a pro-inflammatory and pro-fibrotic secretome that alters the sinoatrial microenvironment. Structural (e.g. fibro-fatty infiltration) and cellular (e.g. mitochondrial uncoupling, sarcoplasmic reticulum fragmentation, and cellular protein quantity/localization) changes then determine an electrophysiological remodelling that also involves the autonomic nervous system. Finally, we summarize how EAT dysfunction may fit with the standard guidelines for AF. Lastly, we focus on the potential benefit of weight loss and different classes of CV drugs on EAT dysfunction, LA remodelling, and ultimately AF onset and recurrence.mixedPoggi, Andrea Lorenzo; Gaborit, Bénédicte; Schindler, Thomas Hellmut; Liberale, Luca; Montecucco, Fabrizio; Carbone, FedericoPoggi, Andrea Lorenzo; Gaborit, Bénédicte; Schindler, Thomas Hellmut; Liberale, Luca; Montecucco, Fabrizio; Carbone, Federic

Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR

Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis