Chinese Expert Consensus on Critical Care Ultrasound Applications at COVID-19 Pandemic

The spread of new coronavirus (SARS-Cov-2) follows a different pattern than previous respiratory viruses, posing a serious public health risk worldwide. World Health Organization (WHO) named the disease as COVID-19 and declared it a pandemic. COVID-19 is characterized by highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. Chest X-ray, computerized tomography (CT), and ultrasound are commonly used imaging modalities. Among them, ultrasound, due to its portability and non-invasiveness, can be easily moved to the bedside for examination at any time. In addition, with use of 4G or 5G networks, remote ultrasound consultation can also be performed, which allows ultrasound to be used in isolated medial areas. Besides, the contact surface of ultrasound probe with patients is small and easy to be disinfected. Therefore, ultrasound has gotten lots of positive feedbacks from the frontline healthcare workers, and it has played an indispensable role in the course of COVID-19 diagnosis and follow up

Preliminary clinical study of left ventricular myocardial strain in patients with non-ischemic dilated cardiomyopathy by three-dimensional speckle tracking imaging

<p>Abstract</p> <p>Background</p> <p>Non-ischemic dilated cardiomyopathy (DCM) is the most common cardiomyopathy worldwide, with significant mortality. Correct evaluation of the patient's myocardial function has important clinical significance in the diagnosis, therapeutic effect assessment and prognosis in non-ischemic DCM patients. This study evaluated the feasibility of three-dimensional speckle tracking imaging (3D-STE) for assessment of the left ventricular myocardial strain in patients with non-ischemic dilated cardiomyopathy (DCM).</p> <p>Methods</p> <p>Apical full-volume images were acquired from 65 patients with non-ischemic DCM (DCM group) and 59 age-matched normal controls (NC group), respectively. The following parameters were measured by 3D-STE: the peak systolic radial strain (RS), circumferential strain (CS), longitudinal strain (LS) of each segment. Then all the parameters were compared between the two groups.</p> <p>Results</p> <p>The peak systolic strain in different planes had certain regularities in normal groups, radial strain (RS) was the largest in the mid region, the smallest in the apical region, while circumferential strain (CS) and longitudinal strain (LS) increased from the basal to the apical region. In contrast, the regularity could not be applied to the DCM group. RS, CS, LS were significantly decreased in DCM group as compared with NC group (<it>P </it>< 0.001 for all). The interobserver, intraobserver and test-retest reliability were acceptable.</p> <p>Conclusions</p> <p>3D-STE is a reliable tool for evaluation of left ventricular myocardial strain in patients with non-ischemic DCM, with huge advantage in clinical application.</p

Pregnancy and neonatal outcomes of COVID-19: The PAN-COVID study

Objective To assess perinatal outcomes for pregnancies affected by suspected or confirmed SARS-CoV-2 infection. Methods Prospective, web-based registry. Pregnant women were invited to participate if they had suspected or confirmed SARS-CoV-2 infection between 1st January 2020 and 31st March 2021 to assess the impact of infection on maternal and perinatal outcomes including miscarriage, stillbirth, fetal growth restriction, pre-term birth and transmission to the infant. Results Between April 2020 and March 2021, the study recruited 8239 participants who had suspected or confirmed SARs-CoV-2 infection episodes in pregnancy between January 2020 and March 2021. Maternal death affected 14/8197 (0.2%) participants, 176/8187 (2.2%) of participants required ventilatory support. Pre-eclampsia affected 389/8189 (4.8%) participants, eclampsia was reported in 40/ 8024 (0.5%) of all participants. Stillbirth affected 35/8187 (0.4 %) participants. In participants delivering within 2 weeks of delivery 21/2686 (0.8 %) were affected by stillbirth compared with 8/4596 (0.2 %) delivering ≥ 2 weeks after infection (95 % CI 0.3–1.0). SGA affected 744/7696 (9.3 %) of livebirths, FGR affected 360/8175 (4.4 %) of all pregnancies. Pre-term birth occurred in 922/8066 (11.5%), the majority of these were indicated pre-term births, 220/7987 (2.8%) participants experienced spontaneous pre-term births. Early neonatal deaths affected 11/8050 livebirths. Of all neonates, 80/7993 (1.0%) tested positive for SARS-CoV-2. Conclusions Infection was associated with indicated pre-term birth, most commonly for fetal compromise. The overall proportions of women affected by SGA and FGR were not higher than expected, however there was the proportion affected by stillbirth in participants delivering within 2 weeks of infection was significantly higher than those delivering ≥ 2 weeks after infection. We suggest that clinicians’ threshold for delivery should be low if there are concerns with fetal movements or fetal heart rate monitoring in the time around infection

Artificial Intelligence-assisted Medical Imaging in Interventional Management of Valvular Heart Disease

The integration of medical imaging and artificial intelligence (AI) has revolutionized interventional therapy of valvular heart diseases (VHD), owing to rapid development in multimodality imaging and healthcare big data. Medical imaging techniques, such as echocardiography, cardiovascular magnetic resonance (CMR) and computed tomography (CT), play an irreplaceable role in the whole process of pre-, intra- and post-procedural intervention of VHD. Different imaging techniques have unique advantages in different stages of interventional therapy. Therefore, single imaging technique can’t fully meet the requirements of complicated clinical scenarios. More importantly, a single intraoperative image provides only limited vision of the surgical field, which could be a potential source for unsatisfactory prognosis. Besides, the non-negligible inter- and intra-observer variability limits the precise quantification of heart valve structure and function in daily clinical practice. With the help of analysis clustered and regressed by big data and exponential growth in computing power, AI broken grounds in the interventional therapy of VHD, including preoperative planning, intraoperative navigation, and postoperative follow-up. This article reviews the state-of-the-art progress and directions in the application of AI for medical imaging in the interventional therapy of VHD