Pediatric nomograms for left ventricle biplane 2D volumes in healthy Caucasian children

Screening left ventricular systolic dysfunction in children using intrinsic frequencies of carotid pressure waveforms measured by a novel smartphone-based device

Objective.Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients.Approach. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%.Main results.The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameterω1was elevated among patients with reduced LVEF.Significance.A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.

Proof of concept: Comparative accuracy of semiautomated VR modeling for volumetric analysis of the heart ventricles

Introduction

Simpson's rule is generally used to estimate cardiac volumes. By contrast, modern methods such as Virtual Reality (VR) utilize mesh modeling to present the object's surface spatial structure, thus enabling intricate volumetric calculations. In this study, two types of semiautomated VR models for cardiac volumetric analysis were compared to the standard Philips dedicated cardiac imaging platform (PDP) which is based on Simpson's rule calculations.

Methods

This retrospective report examined the cardiac computed tomography angiography (CCTA) of twenty patients with atrial fibrillation obtained prior to a left atrial appendage occlusion procedure. We employed two VR models to evaluate each CCTA and compared them to the PDP: a VR model with Philips-similar segmentations (VR-PS) that included the trabeculae and the papillary muscles within the luminal volume, and a VR model that only included the inner blood pool (VR-IBP).

Results

Comparison of the VR-PS and the PDP left ventricle (LV) volumes demonstrated excellent correlation with a ρc of 0.983 (95% CI 0.96, 0.99), and a small mean difference and range. The calculated volumes of the right ventricle (RV) had a somewhat lower correlation of 0.89 (95% CI 0.781, 0.95), a small mean difference, and a broader range. The VR-IBP chamber size estimations were significantly smaller than the estimates based on the PDP.

Discussion

Simpson's rule and polygon summation algorithms produce similar results in normal morphological LVs. However, this correlation failed to emerge when applied to RVs and irregular chambers.

Conclusions

The findings suggest that the polygon summation method is preferable for RV and irregular LV volume and function calculations.

Left ventricular noncompaction: a disorder with genotypic and phenotypic heterogeneity-a narrative review

Background and Objective

Left ventricular noncompaction (LVNC) is a cardiomyopathy characterized by excessive trabecular formation and deep recesses in the ventricular wall, with a bilaminar structure consisting of an endocardial noncompaction layer and an epicardial compacted layer. Although genetic variants have been reported in patients with LVNC, understanding of LVNC and its pathogenesis has not yet been fully elucidated. We addressed the latest findings on genes reported to be associated with LVNC morphogenesis and possible pathologies to understand the diverse spectrum between genotype and phenotype in LVNC. Also, the latest findings and issues related to the diagnosis of LVNC were summarized.

Methods

This article is written as a commentary narrative review and will provide an update on the current literature and available data on common forms of LVNC published in the past 30 years in English through to May 2022 using PubMed.

Key Content and Findings

Familial forms of LVNC are frequent, and autosomal dominant mode of inheritance has been predominantly observed. Several of the candidate causative genes are also mutated in other cardiomyopathies, suggesting a possible shared molecular and/or cellular etiology. The most common gene functions were sarcomere function whereas genes in mice LVNC models were involved in heart development. Echocardiography and cardiac magnetic resonance imaging (CMR) are useful for diagnosis although there are no unified criteria due to overdiagnosis of imaging, poor consistency between techniques, and lack of association between trabecular severity and adverse clinical outcomes.

Conclusions

This review reflects the current lack of clarity regarding the pathogenesis and significance of LVNC and showed the complexity of imaging diagnostic criteria, interpretation of the role of LVNC as a cause, and uncertainty regarding the specific genetic basis of LVNC.

Imaging Features of Pediatric Left Ventricular Noncompaction Cardiomyopathy in Echocardiography and Cardiovascular Magnetic Resonance

Background: Left ventricular noncompaction (LVNC) is a distinct cardiomyopathy characterized by the presence of a two-layer myocardium with prominent trabeculation and deep intertrabecular recesses. The diagnosis of LVNC can be challenging because the diagnostic criteria are not uniform. The aim of our study was to evaluate echocardiographic and CMR findings in a group of children with isolated LVNC. Methods: From February 2008 to July 2021, pediatric patients under 18 years of age at the time of diagnosis with echocardiographic evidence of isolated LVNC were prospectively enrolled. The patients underwent echocardiography and contrast-enhanced cardiovascular magnetic resonance (CMR) with late gadolinium enhancement to assess myocardial noncompaction, ventricular size, and function. Results: A total of 34 patients, with a median age of 11.9 years, were recruited. The patients were followed prospectively for a median of 5.1 years. Of the 31 patients who met Jenni’s criteria in echocardiography, CMR was performed on 27 (79%). Further comprehensive analysis was performed in the group of 25 patients who met the echocardiographic and CMR criteria for LVNC. In echocardiography, the median NC/C ratio in systole was 2.60 and in diastole 3.40. In 25 out of 27 children (93%), LVNC was confirmed by CMR, according to Petersen’s criteria, with a median NC/C ratio of 3.27. Conclusions: (1) Echocardiography precisely identifies patients with LVNC. (2) Echocardiography is a good method for monitoring LV systolic function, but CMR is indicated for the precise assessment of LV remodeling and RV size and function, as well as for the detection of myocardial fibrosis.