The mechanical index (MI) has been used by the US Food and Drug Administration (FDA) since 1992 for regulatory decisions regarding the acoustic output of diagnostic ultrasound equipment. Its formula is based on predictions of acoustic cavitation under specific conditions. Since its implementation over 2 decades ago, new imaging modes have been developed that employ unique beam sequences exploiting higher-order acoustic phenomena, and, concurrently, studies of the bioeffects of ultrasound under a range of imaging scenarios have been conducted. In 2012, the American Institute of Ultrasound in Medicine Technical Standards Committee convened a working group of its Output Standards Subcommittee to examine and report on the potential risks and benefits of the use of conditionally increased acoustic pressures (CIP) under specific diagnostic imaging scenarios. The term “conditionally” is included to indicate that CIP would be considered on a per-patient basis for the duration required to obtain the necessary diagnostic information. This document is a result of that effort. In summary, a fundamental assumption in the MI calculation is the presence of a preexisting gas body. For tissues not known to contain preexisting gas bodies, based on theoretical predications and experimentally reported cavitation thresholds, we find this assumption to be invalid. We thus conclude that exceeding the recommended maximum MI level given in the FDA guidance could be warranted without concern for increased risk of cavitation in these tissues. However, there is limited literature assessing the potential clinical benefit of exceeding the MI guidelines in these tissues. The report proposes a 3-tiered approach for CIP that follows the model for employing elevated output in magnetic resonance imaging and concludes with summary recommendations to facilitate Institutional Review Board (IRB)-monitored clinical studies investigating CIP in specific tissues.

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Background Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan–Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010–2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087–2.387, P = 0.018). Conclusion High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

The continued severity of the global epidemic situation has led to a rising risk of imported cases in China, and domestic cluster epidemic events caused by imported cases have occurred from time to time, repeatedly causing nation-wide disruption. To deeply explain this phenomenon, this study adopted the grounded theory method, using the 5·21 Guangzhou COVID-19 outbreak and 7·20 Nanjing COVID-19 outbreak as examples to study the risk transmission mechanism of domestic cluster epidemic caused by overseas imported cases. The study found that the risk factors for the phenomenon mainly include the following seven aspects: external protection, operations and supervision, international and domestic environment, contaminated objects, virus characteristics, management efficacy, and individual factors. These risk factors together constitute the “detonator”, “risk source”, “risk carrier,” and “risk amplifier” in the risk transmission process. In addition, this study also found that the transmission mechanism of domestic clusters caused by imported cases is a process of secondary risk amplification. The increase in risk carriers leads to a surge in secondary risks compared with the first, which leads to the outbreak of domestic clusters. Finally, based on the characteristics of the transmission mechanism and risk transmission components, this study provides some suggestions on risk mitigation for public departments to optimize China’s epidemic prevention policies.

Purpose Based on the Chinese context, this study uses severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) outbreaks as examples to identify the risk factors that lead to the major emerging infectious diseases outbreak, and put forward risk governance strategies to improve China’s biosecurity risk prevention and control capabilities. Material and Methods This study combines grounded theory and WSR methodology, and utilizes the NVivo 12.0 qualitative analysis software to identify the risk factors that led to the major emerging infectious diseases outbreak. The research data was sourced from 168 publicly available official documents, which are highly authoritative and reliable. Results This study identified 10 categories of Wuli risk factors, 6 categories of logical Shili risk factors, and 8 categories of human Renli risk factors that contributed to the outbreak of major emerging infectious diseases. These risk factors were distributed across the early stages of the outbreak, and have different mechanisms of action at the macro and micro levels. Conclusion This study identified the risk factors that lead to the outbreak of major emerging infectious disease, and discovered the mechanism of the outbreak at the macro and micro levels. At the macro level, Wuli risk factors are the forefront antecedents that lead to the outbreak of the crisis, Renli factors are the intermediate regulatory factors, and Shili risk factors are the back-end posterior factors. At the micro level, there are risk coupling, risk superposition, and risk resonance interactions among various risk factors, leading to the outbreak of the crisis. Based on these interactive relationships, this study proposes risk governance strategies that are helpful for policymakers in dealing with similar crises in the future.