Background: The aim of the study was to document cardiovascular clinical findings, cardiac imaging and laboratory markers in children presenting with the novel multisystem inflammatory syndrome (MIS-C) associated with COVID-19 infection. Methods: A real-time internet-based survey endorsed by the Association for European Paediatric and Congenital Cardiologists (AEPC) Working Groups for Cardiac Imaging and Cardiovascular Intensive Care. Inclusion criteria was children 0-18 years admitted to hospital between February 1 and June 6, 2020 with diagnosis of an inflammatory syndrome and acute cardiovascular complications. Results: A total of 286 children from 55 centers in 17 European countries were included. The median age was 8.4 years (IQR 3.8-12.4 years) and 67% were males. The most common cardiovascular complications were shock, cardiac arrhythmias, pericardial effusion and coronary artery dilatation. Reduced left ventricular ejection fraction was present in over half of the patients and a vast majority of children had raised cardiac troponin (cTnT) when checked. The biochemical markers of inflammation were raised in majority of patients on admission: elevated CRP, serum ferritin, procalcitonin, NT-proBNP, IL-6 level and D-dimers. There was a statistically significant correlation between degree of elevation in cardiac and biochemical parameters and need for intensive care support (p <0.05). Polymerase chain reaction (PCR) for SARS-CoV-2 was positive in 33.6% while IgM and IgG antibodies were positive in 15.7% and IgG 43.6 % cases, respectively when checked. One child died in the study cohort. Conclusions: Cardiac involvement is common in children with multisystem inflammatory syndrome associated with Covid-19 pandemic. A majority of children have significantly raised levels of NT pro-BNP, ferritin, D-dimers and cardiac troponin in addition to high CRP and procalcitonin levels. Compared to adults with Covid-19, mortality in children with MIS-C is uncommon despite multi-system involvement, very elevated inflammatory markers and need for intensive care support.

3D models are accurate replicas of the cardiovascular anatomy and improve the understanding of complex CHD. 3D models did not change the surgical decision in most of the cases (21 of 40 cases, 52.5% cases). However, in 19 of the 40 selected complex cases, 3D model helped redefining the surgical approach.

Objectives To evaluate whether three‐dimensional (3D) printed models can be used to improve interventional simulation and planning in patients with aortic arch hypoplasia. Background: Stenting of a hypoplastic transverse arch is technically challenging, and complications such as stent migration and partial obstruction of the origin of the head and neck vessels are highly dependent on operator skills and expertise. Methods: Using magnetic resonance imaging (MRI) data, a 3D model of a repaired aortic coarctation of a 15‐year‐old boy with hypoplastic aortic arch was printed. Simulation of the endovascular stenting of the hypoplastic arch was carried out under fluoroscopic guidance in the 3D printed model, and subsequently in the patient. A Bland–Altman analysis was used to evaluate the agreement between measurements of aortic diameter in the 3D printed model and the patient's MRI and X‐ray angiography. Results: The 3D printed model proved to be radio‐opaque and allowed simulation of the stenting intervention. The assessment of optimal stent position, size, and length was found to be useful for the actual intervention in the patient. There was excellent agreement between the 3D printed model and both MRI and X‐ray angiographic images (mean bias and standard deviation of 0.36 ± 0.45 mm). Conclusions: 3D printed models accurately replicate patients' anatomy and are helpful in planning endovascular stenting in transverse arch hypoplasia. This opens a door for potential simulation applications of 3D models in the field of catheterization and cardiovascular interventions. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

BackgroundShortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes.MethodsA systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded.ResultsTotally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced.Conclusions and implication of key findingsPredominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

Cardiovascular models constructed by rapid prototyping techniques are extremely helpful for planning corrective surgery in patients with complex congenital malformations. Therefore they may potentially reduce operative time and morbi-mortality.

The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

BackgroundSystemic-to-pulmonary collateral flow (SPCF) may constitute a risk factor for increased morbidity and mortality in patients with single-ventricle physiology (SV). However, clinical research is limited by the complexity of multi-vessel two-dimensional (2D) cardiovascular magnetic resonance (CMR) flow measurements. We sought to validate four-dimensional (4D) velocity acquisition sequence for concise quantification of SPCF and flow distribution in patients with SV.Methods29 patients with SV physiology prospectively underwent CMR (1.5 T) (n = 14 bidirectional cavopulmonary connection [BCPC], age 2.9 ± 1.3 years; and n = 15 Fontan, 14.4 ± 5.9 years) and 20 healthy volunteers (age, 28.7 ± 13.1 years) served as controls. A single whole-heart 4D velocity acquisition and five 2D flow acquisitions were performed in the aorta, superior/inferior caval veins, right/left pulmonary arteries to serve as gold-standard. The five 2D velocity acquisition measurements were compared with 4D velocity acquisition for validation of individual vessel flow quantification and time efficiency. The SPCF was calculated by evaluating the disparity between systemic (aortic minus caval vein flows) and pulmonary flows (arterial and venour return). The pulmonary right to left and the systemic lower to upper body flow distribution were also calculated.ResultsThe comparison between 4D velocity and 2D flow acquisitions showed good Bland-Altman agreement for all individual vessels (mean bias, 0.05±0.24 l/min/m2), calculated SPCF (−0.02±0.18 l/min/m2) and significantly shorter 4D velocity acquisition-time (12:34 min/17:28 min,p < 0.01). 4D velocity acquisition in patients versus controls revealed (1) good agreement between systemic versus pulmonary estimator for SPFC; (2) significant SPCF in patients (BCPC 0.79±0.45 l/min/m2; Fontan 0.62±0.82 l/min/m2) and not in controls (0.01 + 0.16 l/min/m2), (3) inverse relation of right/left pulmonary artery perfusion and right/left SPCF (Pearson = −0.47,p = 0.01) and (4) upper to lower body flow distribution trend related to the weight (r = 0.742, p < 0.001) similar to the controls.Conclusions4D velocity acquisition is reliable, operator-independent and more time-efficient than 2D flow acquisition to quantify SPCF. There is considerable SPCF in BCPC and Fontan patients. SPCF was more pronounced towards the respective lung with less pulmonary arterial flow suggesting more collateral flow where less anterograde branch pulmonary artery perfusion.

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