ABSTRACT
Since late December 2019, severe acute respiratory syndrome coronavirus 2 has spread across the world, which resulted in the World Health Organization declaring a global pandemic. Coronavirus disease 2019 (COVID-19) presents a highly variable spectrum with regard to the severity of illness. Most infected individuals exhibit a mild to moderate illness (81%); however, 14% have a serious disease and 5% develop severe acute respiratory distress syndrome (ARDS), requiring intensive care support. The mortality rate of COVID-19 continues to rise across the world. Data regarding predictors of mortality in patients with COVID 19 are still scarce but are being actively investigated. The present multicenter retrospective observational study provides a complete description of the demographic and clinical characteristics, comorbidities and laboratory abnormalities in a population of 421 hospitalized patients recruited across eight infectious disease units in Southern Italy (Sicily) with the aim of identifying the baseline characteristics predisposing COVID-19 patients to critical illness or death. In this study, older age, pre-existing comorbidities and certain changes in laboratory markers (such as neutrophilia, lymphocytopenia and increased C-reactive protein levels) at the time of admission were associated with a higher risk of mortality. Male sex, on the other hand, was not significantly associated with increased risk of mortality. Symptoms such as fatigue, older age, a number of co-pathologies and use of continuous positive airway pressure were the most significant contributors in the estimation of clinical prognosis. Further research is required to better characterize the epidemiological features of COVID-19, to understand the related predictors of death and to develop new effective therapeutic strategies.
ABSTRACT
The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients.