Cardiac atrial pathology in Duchenne muscular dystrophy.

INTRODUCTION/AIMS: Duchenne muscular dystrophy (DMD) is characterized by fibrofatty replacement of muscle. This has been documented in the ventricular myocardium of DMD patients, but there is limited description of atrial involvement. The purpose of this study is to examine the arrhythmia and ectopy burden in patients with DMD and non-DMD dilated cardiomyopathy (DCM) and to characterize the cardiac histopathologic changes in DMD patients across the disease spectrum. METHODS: This was a retrospective analysis of age-matched patients with DMD and non-DMD DCM who received a Holter monitor and cardiac imaging within 100 days of each other between 2010 and 2020. Twenty-four-hour Holter monitors were classified based on the most recent left ventricular ejection fraction at the time of monitoring. Cardiac histopathologic specimens from whole-heart examinations at the time of autopsy from three DMD patients and one DCM patient were reviewed. RESULTS: A total of 367 patients with 1299 Holter monitor recordings were included over the study period, with 94% representing DMD patients and 6% non-DMD DCM. Patients with DMD had more atrial ectopy across the cardiac function spectrum (p < 0.05). There was no difference in ventricular ectopy. Four DMD patients developed symptomatic atrial arrhythmias. Autopsy specimens from DMD patients demonstrated fibrofatty infiltration of both atrial and ventricular myocardium. DISCUSSION: The atrial myocardium in patients with DMD is unique. Autopsy specimens reveal fibofatty replacement of the atrial myocardium, which may be a nidus for both ectopy and arrhythmias in DMD patients.

Impact of inflammation and steroids on anti-coagulation in children supported on a ventricular assist device.

Critically ill pediatric patients supported on ventricular assist devices (VADs) are increasingly being anticoagulated on bivalirudin, but with difficulty monitoring anticoagulation. Activated partial thromboplastin time (aPTT) has recently been shown to poorly correlate with bivalirudin plasma concentrations, while dTT had excellent correlation. However, aPTT is the more common monitoring test and dTT testing is rarely used. In addition, effects of frequent clinical VAD scenarios (such as inflammation) on the accuracy of aPTT and dTT testing remains uncertain. We reviewed the effects of clinical scenarios (infection/inflammation, chylothorax, and steroids administration) on anticoagulation monitoring in 10 pediatric VAD patients less than 3 years at Cincinnati Children's Hospital Medical Center from 10/27/2020 to 5/6/2022 using bivalirudin for anticoagulation. There were 16 inflammation/infection, 3 chylothorax, and 6 steroids events. Correlation between dTT and aPTT was significantly lower after infection/inflammation, with dTT increasing prior to inflammation/infection while aPTT remained unchanged. In addition, steroids are administered to VAD patients to reduce inflammation and thus additionally stabilize anticoagulation. However, this anticoagulation stabilization effect was reflected more accurately by dTT compared to aPTT. In children requiring VAD support utilizing bivalirudin anticoagulation, inflammation/infection is a common occurrence resulting in anticoagulation changes that may be more accurately reflected by dTT as opposed to aPTT.

Wireless Pulmonary Artery Pressure Monitor Implantation in a Patient with Duchenne Muscular Dystrophy.

Assessing heart failure progression in patients with Duchenne Muscular Dystrophy (DMD) is challenging given the multi-system nature of disease. Herein we describe the first case use of an implantable pulmonary artery pressure monitor and describe the potential clinical utility of this approach in patients with DMD.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

Draft Guidance for Industry Duchenne Muscular Dystrophy, Becker Muscular Dystrophy, and Related Dystrophinopathies - Developing Potential Treatments for the Entire Spectrum of Disease.

Background: Duchenne muscular dystrophy (DMD) and related dystrophinopathies are neuromuscular conditions with great unmet medical needs that require the development of effective medical treatments. Objective: To aid sponsors in clinical development of drugs and therapeutic biological products for treating DMD across the disease spectrum by integrating advancements, patient registries, natural history studies, and more into a comprehensive guidance. Methods: This guidance emerged from collaboration between the FDA, the Duchenne community, and industry stakeholders. It entailed a structured approach, involving multiple committees and boards. From its inception in 2014, the guidance underwent revisions incorporating insights from gene therapy studies, cardiac function research, and innovative clinical trial designs. Results: The guidance provides a deeper understanding of DMD and its variants, focusing on patient engagement, diagnostic criteria, natural history, biomarkers, and clinical trials. It underscores patient-focused drug development, the significance of dystrophin as a biomarker, and the pivotal role of magnetic resonance imaging in assessing disease progression. Additionally, the guidance addresses cardiomyopathy's prominence in DMD and the burgeoning field of gene therapy. Conclusions: The updated guidance offers a comprehensive understanding of DMD, emphasizing patient-centric approaches, innovative trial designs, and the importance of biomarkers. The focus on cardiomyopathy and gene therapy signifies the evolving realm of DMD research. It acts as a crucial roadmap for sponsors, potentially leading to improved treatments for DMD.

Induced pluripotent stem cell (iPSC) modeling validates reduced GBE1 enzyme activity due to a novel variant, p.Ile694Asn, found in a patient with suspected glycogen storage disease IV.

Background: Glycogen Storage disease type 4 (GSD4), a rare disease caused by glycogen branching enzyme 1 (GBE1) deficiency, affects multiple organ systems including the muscles, liver, heart, and central nervous system. Here we report a GSD4 patient, who presented with severe hepatosplenomegaly and cardiac ventricular hypertrophy. GBE1 sequencing identified two variants: a known pathogenic missense variant, c.1544G>A (p.Arg515His), and a missense variant of unknown significance (VUS), c.2081T>A (p. Ile694Asn). As a liver transplant alone can exacerbate heart dysfunction in GSD4 patients, a precise diagnosis is essential for liver transplant indication. To characterize the disease-causing variant, we modeled patient-specific GBE1 deficiency using CRISPR/Cas9 genome-edited induced pluripotent stem cells (iPSCs). Methods: iPSCs from a healthy donor (iPSC-WT) were genome-edited by CRISPR/Cas9 to induce homozygous p.Ile694Asn in GBE1 (iPSC-GBE1-I694N) and differentiated into hepatocytes (iHep) or cardiomyocytes (iCM). GBE1 enzyme activity was measured, and PAS-D staining was performed to analyze polyglucosan deposition in these cells. Results: iPSCGBE1-I694N differentiated into iHep and iCM exhibited reduced GBE1 protein level and enzyme activity in both cell types compared to iPSCwt. Both iHepGBE1-I694N and iCMGBE1-I694N showed polyglucosan deposits correlating to the histologic patterns of the patient's biopsies. Conclusions: iPSC-based disease modeling supported a loss of function effect of p.Ile694Asn in GBE1. The modeling of GBE1 enzyme deficiency in iHep and iCM cell lines had multi-organ findings, demonstrating iPSC-based modeling usefulness in elucidating the effects of novel VUS in ultra-rare diseases.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Background: Total Cardiac Volume (TCV) based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. Objective: This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Materials and Methods: Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a Dense-Net architecture in combination with residual blocks of ResNet architecture. Results: The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). Conclusion: A deep learning based 3D-CNN model can provide accurate automatic measurement of TCV from CT images.