Incidence, Predictors, and Outcomes of In-Hospital Cardiac Arrest in COVID-19 Patients Admitted to Intensive and Non-Intensive Care Units: Insights From the AHA COVID-19 CVD Registry.

Background Limited information is available regarding in-hospital cardiac arrest (IHCA) in patients with COVID-19. Methods and Results We leveraged the American Heart Association COVID-19 Cardiovascular Disease (AHA COVID-19 CVD) Registry to conduct a cohort study of adults hospitalized for COVID-19. IHCA was defined as those with documentation of cardiac arrest requiring medication or electrical shock for resuscitation. Mixed effects models with random intercepts were used to identify independent predictors of IHCA and mortality while accounting for clustering at the hospital level. The study cohort included 8518 patients (6080 not in the intensive care unit [ICU]) with mean age of 61.5 years (SD 17.5). IHCA occurred in 509 (5.9%) patients overall with 375 (73.7%) in the ICU and 134 (26.3%) patients not in the ICU. The majority of patients at the time of ICHA were not in a shockable rhythm (76.5%). Independent predictors of IHCA included older age, Hispanic ethnicity (odds ratio [OR], 1.9; CI, 1.4-2.4; P<0.001), and non-Hispanic Black race (OR, 1.5; CI, 1.1-1.9; P=0.004). Other predictors included oxygen use on admission, quick Sequential Organ Failure Assessment score on admission, and hypertension. Overall, 35 (6.9%) patients with IHCA survived to discharge, with 9.1% for ICU and 0.7% for non-ICU patients. Conclusions Older age, Black race, and Hispanic ethnicity are independent predictors of IHCA in patients with COVID-19. Although the incidence is much lower than in ICU patients, approximately one-quarter of IHCA events in patients with COVID-19 occur in non-ICU settings, with the latter having a substantially lower survival to discharge rate.

Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging . METHODS: From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups. RESULTS: Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001). CONCLUSIONS: The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.

OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. RESULTS: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. CONCLUSION: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy.

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.