Left ventricular hypertrophy: hypertensive or hypertrophic cardiomyopathy? What a dilemma! A case report

In the presence of the left ventricle hypertrophy (LVH), the differential diagnosis with hypertrophic cardiomyopathy (HCM) or some phenocopy must be always considered, which can be easily suspected when the hypertrophy is markedly asymmetric. However, when the hypertrophy is homogeneous, especially if the patient has concomitant hypertension, it may be a challenge to distinguish between hypertensive and HCM, although some clinical features may help us to suspect it. In addition, patients with HCM may present with exertional angina due to microcirculation involvement in the setting of the hypertrophy itself or dynamic obstruction in the left ventricular outflow tract, but in some cases, the presence of concomitant coronary artery disease must be suspected as the cause of angina, especially if the patient has an intermediate or high-risk probability of having ischemic heart disease. We present the case of a 46-year-old Afro-American man with poorly controlled hypertension who was found to have severe LVH, and who presented with symptoms of exertional angina during follow-up. We will review the clinical features that can help us in the differential diagnosis in this context.

Hypertension: An Important But Reversible Cause of Systolic Dysfunction in a Cohort of Pediatric Patients

Cardiac dysfunction due to hypertension (CDHTN) in pediatrics is not well described. We aimed to describe the presentation and outcomes of pediatric CDHTN and identify clinical features associated with resolution of dysfunction. A single-center retrospective cohort study of patients ≤ 21 years with CDHTN from January 2005-September 2020 was performed. Patients with systolic dysfunction without another cause, blood pressure > 95th percentile, and physician judgment that dysfunction was secondary to hypertension were included. Demographics, clinical characteristics, echocardiographic findings, and outcomes were examined using Fisher's exact and Mann-Whitney U tests. Multiple correspondence analysis was used to explore the relationship of resolution of dysfunction to clinical features. Thirty-four patients were analyzed at a median age of 10.9 (IQR 0.3-16.9) years. Patients were divided into groups < 1 year (n = 12) and ≥ 1 year (n = 22). Causes of hypertension were varied by age, with renovascular disease most common in infants (42%) and medical renal disease most common in older patients (77%). Echocardiography demonstrated mild LV dilation (median LV end-diastolic z-score 2.6) and mild LV hypertrophy (median LV mass z-score 2.4). Most patients (81%) had resolution of dysfunction, particularly infants (92%). One patient died and one patient was listed for heart transplant. None required mechanical circulatory support (MCS). No clinical features were statistically associated with resolution of dysfunction. Hypertension is an important but reversible cause of systolic dysfunction in children. Patients are likely to recover with low mortality and low utilization of MCS or transplantation. Further studies are needed to confirm features associated with resolution of dysfunction.

Weakly supervised video-based cardiac detection for hypertensive cardiomyopathy

INTRODUCTION

Parameters, such as left ventricular ejection fraction, peak strain dispersion, global longitudinal strain, etc. are influential and clinically interpretable for detection of cardiac disease, while manual detection requires laborious steps and expertise. In this study, we evaluated a video-based deep learning method that merely depends on echocardiographic videos from four apical chamber views of hypertensive cardiomyopathy detection.

METHODS

One hundred eighty-five hypertensive cardiomyopathy (HTCM) patients and 112 healthy normal controls (N) were enrolled in this diagnostic study. We collected 297 de-identified subjects' echo videos for training and testing of an end-to-end video-based pipeline of snippet proposal, snippet feature extraction by a three-dimensional (3-D) convolutional neural network (CNN), a weakly-supervised temporally correlated feature ensemble, and a final classification module. The snippet proposal step requires a preliminarily trained end-systole and end-diastole timing detection model to produce snippets that begin at end-diastole, and involve contraction and dilatation for a complete cardiac cycle. A domain adversarial neural network was introduced to systematically address the appearance variability of echo videos in terms of noise, blur, transducer depth, contrast, etc. to improve the generalization of deep learning algorithms. In contrast to previous image-based cardiac disease detection architectures, video-based approaches integrate spatial and temporal information better with a more powerful 3D convolutional operator.

RESULTS

Our proposed model achieved accuracy (ACC) of 92%, area under receiver operating characteristic (ROC) curve (AUC) of 0.90, sensitivity(SEN) of 97%, and specificity (SPE) of 84% with respect to subjects for hypertensive cardiomyopathy detection in the test data set, and outperformed the corresponding 3D CNN (vanilla I3D: ACC (0.90), AUC (0.89), SEN (0.94), and SPE (0.84)). On the whole, the video-based methods remarkably appeared superior to the image-based methods, while few evaluation metrics of image-based methods exhibited to be more compelling (sensitivity of 93% and negative predictive value of 100% for the image-based methods (ES/ED and random)).

CONCLUSION

The results supported the possibility of using end-to-end video-based deep learning method for the automated diagnosis of hypertensive cardiomyopathy in the field of echocardiography to augment and assist clinicians.

TRIAL REGISTRATION

Current Controlled Trials ChiCTR1900025325, Aug, 24, 2019. Retrospectively registered.

Development and validation of machine learning algorithms to predict posthypertensive origin in left ventricular hypertrophy

BACKGROUND

Left ventricular hypertrophy is often associated with hypertension, which is not necessarily the cause of hypertrophy. Non-hypertension-related aetiologies often have a strong impact on patient management, and therefore require a thorough and careful workup. When considering all left ventricular hypertrophies, even the mild ones, the number of patients who need a workup increases drastically. This raises the need for a tool to evaluate the pretest probability of the origin of left ventricular hypertrophy.

AIM

To predict the hypertensive origin of left ventricular hypertrophy using machine learning on first-line clinical, laboratory and echocardiographic variables.

METHODS

We used a retrospective single-centre population of 591 patients with left ventricular hypertrophy, starting at 12mm maximal left ventricular wall thickness. After splitting data in a training and testing set, we trained three different algorithms: decision tree; random forest; and support vector machine. Model performances were validated on the testing set.

RESULTS

All models exhibited good areas under receiver operating characteristic curves: 0.82 (95% confidence interval: 0.77-0.88) for the decision tree; 0.90 (95% confidence interval 0.85-0.94) for the random forest; and 0.90 (95% confidence interval: 0.85-0.94) for the support vector machine. After threshold selection, the last model had the best balance between its specificity of 0.96 (95% confidence interval: 0.91-0.99) and its sensitivity of 0.31 (95% confidence interval: 0.17-0.44). All algorithms relied on similar most influential predictor variables. Online calculators were developed and made publicly available.

CONCLUSIONS

Machine learning models were able to determine the hypertensive origin of left ventricular hypertrophy with good performances. Implementation in clinical practice could reduce the number of aetiological workups needed in patients presenting with left ventricular hypertrophy.

The potential roles of Von Willebrand factor and neutrophil extracellular traps in the natural history of hypertrophic and hypertensive cardiomyopathy

Inflammation is often applied broadly to human disease. Despite its general familiarity, inflammation is highly complex. There are numerous injurious, immune and infectious determinants, functional elements and signaling pathways, ranging from genetic to epigenetic, environmental, racial, molecular and cellular that participate in disease onset and progression, phenotypic heterogeneity, and treatment selection and response. In addition, inflammation can be tissue and organ specific, adding a layer of complexity to achieving a detailed and translatable understanding of its role in health and disease. The following review takes a close look at inflammation in the context of two common heart diseases, hypertrophic cardiomyopathy and hypertensive cardiomyopathy.

The innate immune system in chronic cardiomyopathy: a European Society of Cardiology (ESC) scientific statement from the Working Group on Myocardial Function of the ESC

Activation of the immune system in heart failure (HF) has been recognized for over 20 years. Initially, experimental studies demonstrated a maladaptive role of the immune system. However, several phase III trials failed to show beneficial effects in HF with therapies directed against an immune activation. Preclinical studies today describe positive and negative effects of immune activation in HF. These different effects depend on timing and aetiology of HF. Therefore, herein we give a detailed review on immune mechanisms and their importance for the development of HF with a special focus on commonalities and differences between different forms of cardiomyopathies. The role of the immune system in ischaemic, hypertensive, diabetic, toxic, viral, genetic, peripartum, and autoimmune cardiomyopathy is discussed in depth. Overall, initial damage to the heart leads to disease specific activation of the immune system whereas in the chronic phase of HF overlapping mechanisms occur in different aetiologies.