The diagnosis of COVID ARF in elderly: The radiological findings in elderly

The world aging population is continuously rising, and older age even in Italy is an important risk for COVID-19 infection and mortality. Lung aging is accompanied by physiological functional and morphologic-structural changes that lead to increased respiratory impairment in the elderly. Increased vulnerability of elderly patients with chronic comorbidities and weaker immune function makes them an easier target of viral infection and acute respiratory failure. The COVID-19 pandemic poses a high risk to older people. Thoracic imaging plays a pivotal role for the diagnosis, temporal evolution, complications, monitoring of therapeutic efficacy, and elderly COVID-19 patients discharge assessment. The aim of this chapter is to provide a rapid overview of the COVID-19 disease imaging, with a specific focus on older adults. © Springer Nature Switzerland AG 2020.

COVID-19: A qualitative chest CT model to identify severe form of the disease.

PURPOSE: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. MATERIALS AND METHODS: A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range: 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. RESULTS: Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score≥15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score≥15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638-0.806) vs. 0.739 (95% CI: 0.656-0.823), respectively (P=0.209). CONCLUSION: We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.