Bedside lung ultrasound (LUS) can play a role in the setting of the SarsCoV2 pneumonia pandemic. To evaluate the clinical and LUS features of COVID-19 in the ED and their potential prognostic role, a cohort of laboratory-confirmed COVID-19 patients underwent LUS upon admission in the ED. LUS score was derived from 12 fields. A prevalent LUS pattern was assigned depending on the presence of interstitial syndrome only (Interstitial Pattern), or evidence of subpleural consolidations in at least two fields (Consolidation Pattern). The endpoint was 30-day mortality. The relationship between hemogasanalysis parameters and LUS score was also evaluated. Out of 312 patients, only 36 (11.5%) did not present lung involvment, as defined by LUS score < 1. The majority of patients were admitted either in a general ward (53.8%) or in intensive care unit (9.6%), whereas 106 patients (33.9%) were discharged from the ED. In-hospital mortality was 25.3%, and 30-day survival was 67.6%. A LUS score > 13 had a 77.2% sensitivity and a 71.5% specificity (AUC 0.814; p < 0.001) in predicting mortality. LUS alterations were more frequent (64%) in the posterior lower fields. LUS score was related with P/F (R² 0.68; p < 0.0001) and P/F at FiO₂ = 21% (R² 0.59; p < 0.0001). The correlation between LUS score and P/F was not influenced by the prevalent ultrasound pattern. LUS represents an effective tool in both defining diagnosis and stratifying prognosis of COVID-19 pneumonia. The correlation between LUS and hemogasanalysis parameters underscores its role in evaluating lung structure and function.

Lung Ultrasound (LUS) is regarded to be potentially useful to diagnose lung injury in older adults living in nursing homes with suspected COVID-19 pneumonia. We aimed at evaluating presence lung injury among senior nursing home residents by LUS performed with portable wireless scanner echography. The study population consisted of 150 residents with a mean age of 88 years (85% female) residing in 12 nursing homes in Northern Italy. Subjects had to have a history of recent onset of symptoms compatible with COVID-19 pneumonia or have been exposed to the contagion of patients carrying the disease. COVID-19 testing was performed with SARS-CoV-2 nasal-pharyngeal (NP) swabs. Positive subjects to LUS scanning were considered those with non-coascelent B-lines in >3 zones, coalescent B-lines in >3 zones and with iperdensed patchy non-consolidated lungs. Sixty-three percent had positive NP testing and 65% had LUS signs of pulmonary injury. LUS had a sensitivity of 79% in predicting positive NP testing. Sixteen percent of residents tested negative for SARSCoV-2 carried the signs of COVID-19 lung injury at LUS. There were 92 patients (61%) with current or recent symptoms.Positivity to LUS scanning was reported in 73% of residents with symptoms, while it was 53% in those without (P=0.016). A positive NP testing was observed in 66% of residents with symptoms and in 57% of those without (P=0.27). We conclude that assessment of LUS by portable wireless scanner echography can be profitability utilized to diagnose lung injury among senior nursing home residents with or without symptoms compatible with COVID-19 pneumonia.

Background: COVID-19 is an emerging infectious disease, that is heavily challenging health systems worldwide. Admission Arterial Blood Gas (ABG) and Lung Ultrasound (LUS) can be of great help in clinical decision making, especially during the current pandemic and the consequent overcrowding of the Emergency Department (ED). The aim of the study was to demonstrate the capability of alveolar-to-arterial oxygen difference (AaDO₂) in predicting the need for subsequent oxygen support and survival in patients with COVID-19 infection, especially in the presence of baseline normal PaO₂/FiO₂ ratio (P/F) values. Methods: A cohort of 223 swab-confirmed COVID-19 patients underwent clinical evaluation, blood tests, ABG and LUS in the ED. LUS score was derived from 12 ultrasound lung windows. AaDO₂ was derived as AaDO₂ = ((FiO₂) (Atmospheric pressure − H₂O pressure) − (PaCO₂/R)) − PaO₂. Endpoints were subsequent oxygen support need and survival. Results: A close relationship between AaDO₂ and P/F and between AaDO₂ and LUS score was observed (R² = 0.88 and R² = 0.67, respectively; p < 0.001 for both). In the subgroup of patients with P/F between 300 and 400, 94.7% (n = 107) had high AaDO₂ values, and 51.4% (n = 55) received oxygen support, with 2 ICU admissions and 10 deaths. According to ROC analysis, AaDO₂ > 39.4 had 83.6% sensitivity and 90.5% specificity (AUC 0.936; p < 0.001) in predicting subsequent oxygen support, whereas a LUS score > 6 showed 89.7% sensitivity and 75.0% specificity (AUC 0.896; p < 0.001). Kaplan–Meier curves showed different mortality in the AaDO₂ subgroups (p = 0.0025). Conclusions: LUS and AaDO₂ are easy and effective tools, which allow bedside risk stratification in patients with COVID-19, especially when P/F values, signs, and symptoms are not indicative of severe lung dysfunction.

Coronavirus disease 2019 (Covid-19), caused by a novel enveloped RNA betacoronavirus, has recently been declared a public health emergency by the World Health Organization (WHO). The lack of knowledge at the beginning of the pandemics, associated with the inherent risk of infective spreading, makes initial recognition and management particularly complex, in terms of defining effective diagnostic and therapeutic protocols. In the Emergency setting, Lung Ultrasound (LUS) can play an important role in the management of patients with SARS-CoV2-related pneumonia, expanding from the initial diagnosis to the subsequent monitoring and follow-up. Among many other potential advantages (such has the absence of ionizing radiation, its inherent costeffectiveness, and bedside repeatability), LUS provides immediate diagnostic response and might prevent the risk of spreading the infection by moving the patient from the Emergency Room to the Radiology facilities. Aim of this short review is to define the potential role of lung ultrasound in Covid-19 patients, according to the evidence in the medical literature

The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress syndrome (ARDS), causing hundreds of thousands of casualties worldwide. Guidelines for first-line diagnosis of pneumonia suggest Chest X-rays (CXR) for patients exhibiting symptoms. Potential alternatives include Computed Tomography (CT) scans and Lung UltraSound (LUS). Deep learning (DL) has been helpful in diagnosis using CT scans, LUS, and CXR, whereby the former commonly yields more precise results. CXR and CT scans present several drawbacks, including high costs. Radiation-free LUS imaging requires high expertise, and physicians thus underutilise it. LUS demonstrated a strong correlation with CT scans and reliability in pneumonia detection, even in the early stages. Here, we present an LUS video-classification approach based on contemporary DL strategies in close collaboration with Fondazione IRCCS Policlinico San Matteo’s Emergency Department (ED) of Pavia. This research addressed SARS-CoV-2 patterns detection, ranked according to three severity scales by operating a trustworthy dataset comprising ultrasounds from linear and convex probes in 5400 clips from 450 hospitalised subjects. The main contributions of this study are related to the adoption of a standardised severity ranking scale to evaluate pneumonia. This evaluation relies on video summarisation through key-frame selection algorithms. Then, we designed and developed a video-classification architecture which emerged as the most promising. In contrast, the literature primarily concentrates on frame-pattern recognition. By using advanced techniques such as transfer learning and data augmentation, we were able to achieve an F1-Score of over 89% across all classes.