Scientific reports
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One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. ⋯ By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.
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Clinical Trial Observational Study
Evaluation of myocardial injury patterns and ST changes among critical and non-critical patients with coronavirus-19 disease.
Novel coronavirus disease (COVID-19) has led to a major public health crisis globally. Currently, myocardial damage is speculated to be associated with COVID-19, which can be seen as one of the main causes of death of patients with COVID-19. We therefore, aim to investigate the effects of COVID-19 disease on myocardial injury in hospitalized patients who have been tested positive for COVID-19 pneumonia in this study. ⋯ Results analyzed by a logistic regression model showing COVID-19 direct contribution to myocardial injury in these patients. COVID-19 disease directly leads to cardiovascular damage among critical and non-critical patients. Myocardial injury is associated not only with abnormal ECG changes but also with myocardial dysfunction on echocardiography and more commonly observed among critical patients.