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Pneumothorax (PNX) and pneumomediastinum (PNM) are potential complications of COVID-19, but their influence on patients' outcomes remains unclear. The aim of the study was to assess incidence, risk factors, and outcomes of severe COVID-19 complicated with PNX/PNM. Methods: A retrospective multicenter case-control analysis was conducted in COVID-19 patients admitted for respiratory failure in intermediate care units of the Treviso area, Italy, from March 2020 to April 2021. Clinical characteristics and outcomes of patients with and without PNX/PNM were compared. Results: Among 1213 patients, PNX and/or PNM incidence was 4.5%. Among these, 42% had PNX and PNM, 33.5% only PNX, and 24.5% only PNM. COVID-19 patients with PNX/PNM showed higher in-hospital (p = 0.02) and 90-days mortality (p = 0.048), and longer hospitalization length (p = 0.002) than COVID-19 patients without PNX/PNM. At PNX/PNM occurrence, one-third of subjects was not mechanically ventilated, and the respiratory support was similar to the control group. PNX/PNM occurrence was associated with longer symptom length before hospital admission (p = 0.005) and lower levels of blood lymphocytes (p = 0.017). Conclusion: PNX/PNM are complications of COVID-19 associated with a worse prognosis in terms of mortality and length of hospitalization. Although they are more frequent in ventilated patients, they can occur in non-ventilated, suggesting that mechanisms other than barotrauma might contribute to their presentation. [ABSTRACT FROM AUTHOR]

Early reports from Asia suggested that increased serum levels of the muscular enzyme creatine-(phospho)-kinase (CK/CPK) could be associated with a more severe prognosis in COVID-19. The aim of this single-center retrospective cohort study of 331 consecutive COVID-19 patients who were hospitalized during Italy's "first wave" was to verify this relationship, and to evaluate the role of possible confounding factors (age, body mass index, gender, and comorbidities). We subdivided our cohort in two groups, based on "severe" (n = 99) or "mild" (n = 232) outcomes. "Severe" disease is defined here as death and/or mechanical invasive ventilation, in contrast to "mild" patients, who were discharged alive with no need for invasive ventilation; this latter group could also include those patients who were treated with non-invasive ventilation. The CK levels at admission were higher in those subjects who later experienced more severe outcomes (median, 126; range, 10–1672 U/L, versus median, 82; range, 12–1499 U/L, p = 0.01), and hyperCKemia >200 U/L was associated with a worse prognosis. Regression analysis confirmed that increased CK acted as an independent predictor for a "severe" outcome. HyperCKemia was generally transient, returning to normal during hospitalization in the majority of both "severe" and "mild" patients. Although the direct infection of voluntary muscle is unproven, transient muscular dysfunction is common during the course of COVID-19. The influence of this novel coronavirus on voluntary muscle really needs to be clarified. [ABSTRACT FROM AUTHOR]

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well. [ABSTRACT FROM AUTHOR]