key: cord-0848518-1s6jood9
authors: Bonadia, Nicola; Carnicelli, Annamaria; Piano, Alfonso; Buonsenso, Danilo; Gilardi, Emanuele; Kadhim, Cristina; Torelli, Enrico; Petrucci, Martina; Di Maurizio, Luca; Biasucci, Daniele Guerino; Fuorlo, Mariella; Forte, Evelina; Zaccaria, Raffaella; Franceschi, Francesco
title: Lung Ultrasound findings are associated with mortality and need of intensive care admission in COVID-19 patients evaluated in the Emergency Department
date: 2020-07-15
journal: Ultrasound Med Biol
DOI: 10.1016/j.ultrasmedbio.2020.07.005
sha: 782a5db51c130e90b0639c37370d14909d4d8d9c
doc_id: 848518
cord_uid: 1s6jood9

Lung Ultrasound (LUS) has been recently advocated as an accurate tool to diagnose COVID-19 pneumonia. However, reports on its use are mainly based on hypotheses studies, case reports or small retrospective case series, while the prognostic role of LUS in COVID-19 patients has not yet been established. We conducted a prospective study aiming to assess the ability of LUS in predicting mortality and intensive care unit admission of COVID-19 patients evaluated in a tertiary level emergency department. Patients in our sample had a median of 6 lung areas with pathological findings (IQR 6, range 0-14), defined as a score different from 0. The median rate of lung areas involved was 71% (IQR 64%, range 0-100) while the median average score was 1.14 (IQR 0.93, range 0-3). A higher rate of pathological lung areas and of average score was significantly associated with death with estimated difference of 40.5% (95% CI 4-68%, p 0.01) and of 0.47 (95% CI 0.06 – 0.93, p 0.02), respectively. Similarly, the same parameters were associated with a significantly higher risk of intensive care unit admission with ad estimated difference of 29% (95% CI 8-50%, p 0.008) and of 0.47 (95% CI 0.05-0.93, p 0.02), respectively. Our study shows that LUS is able to detect COVID-19 pneumonia and to predict, during the first evaluation in the emergency department, patients at risk of intensive care unit admission and death.

On January 2020, a cluster of atypical pneumonia has been described in Wuhan, China (Zhu et 2 al. 2020) , and subsequently found to be caused by a novel virus, belonging to the family of beta-3 coronavirus (Lu et al. 2020a) . Since then, the virus has spread all over the world causing more than 4 three millions of infections and thousands of deaths. On March 11, 2020, 2019 new coronavirus 5 disease has been declared as a pandemic by the World Health Organization(WHO 6 2020). COVID-19 is posing several challenges for the health and economic systems of all countries 7 in the world: insufficient critical care availability, shortages of personal protective equipment, 8 shortages of healthcare workers and high rates of infection among healthcare professionals (Ranney 9 et al. 2020) . 10

Although COVID-19 is characterized by a wide range of clinical manifestations, from 11 asymptomatic or paucysymptomatic infections to critical disease and death, lung involvement is the 12 mainstay of the disease (Guan et al. 2020; Weiss and Murdoch 2020) . Chest X-ray, however, showed 13 low sensitivity in detecting COVID-19 pneumonia (Guan et al. 2020) , while chest Computed 14 Tomography (CT) scan has been proven to be highly sensitive to recognize COVID-19 15 pneumonia (Ai et al. 2020; Li and Xia 2020) and, according to some (but not all (Caruso et al. 2020) ) 16 authors, also high specificity . Considering that the microbiological isolation of the 17 SARS-CoV-2 through naso-pharingeal swab also has low sensitivity (Tahamtan and Ardebili 2020) , 18 some authors proposed the routine use of CT scan as a screening tool to diagnose patients evaluated in the emergency department ). However, this approach bears a 20 limitation. In particular, CT scan is not routinely available in most low-to-middle resource settings; 21 therefore, such an approach would not be feasible in many countries. Since every country in the 22 world has been involved by the pandemic, the routine use of CT scan cannot be easily suggested. 23

Moreover, CT scan would only be available in hospital settings. Recent evidences are suggesting that 24 the diagnosis of COVID-19 in the early phases of the disease would allow early treatment and 25 containment measures. Therefore, a tool that would allow the diagnosis of pneumonia at point-of-26 care in every resource setting would be particularly useful. In this regard, Lung Ultrasound (LUS) 27 has been recently advocated as a sensitive tool to diagnose COVID-19 pneumonia, and main LUS 28 patterns have been described Soldati et al. 2020b; 29 Volpicelli and Gargani 2020). However, reports on the use of LUS are mainly based on anecdotal 30 studies, small series, or on a larger study aimed to compare LUS with CT scan (Lu et al. 2020b) , 31 while the prognostic role of LUS in COVID-19 patients has not yet been established. 32

For this reason, we performed this prospective study aiming to evaluate the role of LUS in 33 COVID-19 patients evaluated in a tertiary Emergency Department (ED) of a referral centre for the 34 COVID19 epidemics. 35

We conducted a single-center, prospective cohort study in a tertiary Emergency Department 38 located in Rome, Italy. Our institution is a university hospital located in a metropolitan area which is 39 currently serving as a referral center for COVID19 epidemics. In our ED, patients with a clinical 40 suspicion of SARS-CoV-2 infection are admitted to a dedicated ED area. 41

Patients were recruited from 1 st to 31 st March 2020. We included symptomatic adult patients 42 with a microbiologically confirmed infection by SARS-CoV-2. Patients were enrolled if they had a 43 suggestive clinical presentation (dyspnea, fever, cough, coryza; for patients with pre-existing chronic 44 respiratory condition, worsening dyspnea or worsening respiratory failure were considered for 45 inclusion), were 18 years or older at the time of the ED admission and were willing to participate in 46 the study. Exclusion criteria were reduced life expectancy (less than six months) due to a pre-existing 47 chronic illness according to investigator clinical judgment (e.g.: advanced cancer, advanced 48 dementia), inability to collaborate for the execution of the lung ultrasound, lack of microbiological 49 confirmation of SARS-CoV-2 infection during the index hospitalization. Specifically, patients who 50 were enrolled on the basis of clinical suspicion and for whom there was no microbiological 51 confirmation of SARS-CoV-2 infection by real-time reverse-transcription-polymerase-chain-52 reaction (RT-PCR) on respiratory specimen (either from upper or lower respiratory tract) were 53 excluded from subsequent analysis. Moreover, we excluded patients who did not perform LUS in the 54 ED and those with missing data about the final outcome of the hospitalization. 55

The variables collected for each patient were the following: age, sex, clinical symptoms (fever, 56 cough, dyspnea, other), type of ventilation required during the admission (Oxygen, High-Flow-57

Oxygen-Therapy (HFOT)-Optiflow, Continuous Positive Airway Pressure (CPAP), Noninvasive 58 positive-pressure ventilation (NIPPV), invasive ventilation), electrolyte and acid-base balance (FiO 2 , 59 pH, pO 2 , pCO 2 , HCO 3 -, lactate, P/F), P/F class (no ARDS, mild, moderate or severe ARDS). In 60 addition, for each patient included in the study we collected the results of the following 61 investigations: Chest X-ray, Chest CT scan, if performed, real time RT-PCR for SARS-CoV2 on 62 oropharyngeal and nasopharyngeal swab (and bronchoalveolar lavage if performed), laboratory tests 63 (hemoglobin, white blood cell cout, neutrophil and lymphocyte count, platelets count, C-reactive 64 protein, procalcitotinin, fibrinogen, d-dimer, albumin, ferritin, lactate dehydrogenase). For each chest 65 X-ray/chest CT scan we reported if it was positive for unilateral or bilateral interstitial pattern, with 66 or without pulmonary infiltrates. 67

Furthermore, for each patient we recorded the following clinical outcomes: outcome of the 68 Emergency Department, defined asMedical Ward admission, Intensive Care Unit admission or 69 discharge; final outcome of the hospitalization, defined as discharged or dead and need for intensive 70 care admission during index hospitalization. 71

The study was approved by the Ethic Committee of our Institution (ID 3146). Informed 72 consent was obtained from each patient. 73

Our LUS COVID team used a standardized approach regarding equipment and acquisition 75 protocol, as previously described by Soldati et al (Soldati et al. 2020c ). The Soldati approach requires 76 that the patient's chest is divided in 14 areas (three posterior, two lateral and two anterior areas for 77 each side, see figure 1A -D) and for each area a single intercostal scan is performed. For each area, 78 then, a numeric score from 0 to 3 is assigned depending on the severity of findings (described 79 below). No score is assigned to areas which are not accessible to examination (for example, posterior 80 areas in patients unable to maintain sitting position or area number 13 when cardiac window masked 81 lung parenchyma). 82

All the LUS assessments were performed by authors, who are all part of the Emergency 83 Department staff, with at least 5 years of experience in point-of-care ultrasound, not radiologists, 84 during the first clinical examination in the ED, as point-of-care ultrasound examination. LUS was 85 performed with a pocket device using a wireless 6 MHz convex probe (ATL s.r.l., Milano, Italy), 86 collected in single use plastic covers, as previously described , in order to 87 reduce the risk of contamination and to make sterilization procedures easy. For each LUS we 88 reported the patient's position (sitting/lying) and the ventilation type used during the exam. Each 89 exam was recorded, and scores awarded by each examiner underwent confirmation by at least two of 90 the other authors. Overall, there was good concordance among the authors, particularly for extreme 91 scores. Disagreement on single scores to be awarded were resolved through collective discussion. 92

The inter-rater reliability was established by calculation of Cohen's k coefficient. The levels of 93 interobserver agreement were high (values of k coefficient above 0.8) for scores 0 and 1 and good 94 (values of k coefficient between 0.6 and 0.8) for score 2 and 3. 95 A total of fourteen areas (3 posterior, 2 lateral, and 2 anterior for each lung) were scanned per 96 patient, registering a video of 10 seconds in each area. Scans were intercostal to cover the widest 97 surface possible with a single scan, since COVID-19 pneumonia can be bilateral and involve any 98 lung area. A standard sequence of evaluations was used, as described by Soldati et al (Soldati et al. 99 2020c) . In cases of clinical impossibility to evaluate the posterior lung areas of the patient, the 100 operator started the exam from landmark number 7 to fourteen (therefore lateral and anterior surfaces 101 were scanned). 102

We used the scoring system proposed by Soldati (Soldati et al. 2020c ) in order to classify the 103 severity of the lung involvement. In particular, for each area, we defined the following degrees of 104 severity (figure 1E-H): 105  Score 0: normal LUS examination; 106  Score 1: the pleural line is regular or irregular, non-confluent vertical artifacts are visible; 107  Score 2: the pleural line is irregular, multiple confluent vertical artifacts are present, and/or 108 well subpleural consolidations; 109  Score 3: dense and largely extended areas of white lung with or without larger consolidations. 110

For each area, score was assigned by the author performing the exam, with subsequent 111 confirmation by at least two other authors reviewing the recorded clip. Disagreement was resolved 112 through discussion among all authors. 113

Moreover, for each LUS we also registered the possible presence of single/multiple 114 consolidations and/or pleural effusion. 115

For each patient, we collected the total number of lung areas with pathological patterns, the 116 mean score of the total examined areas, and also the percentage of pathological areas over the total 117 areas examined (we included this last parameters since some patients were not examined on the 118 posterior areas in the ED due to impossibility of mobilization secondary to compromised clinical 119 conditions). 120

Outcomes 121

The study aimed to investigate the usefulness of LUS to assess the severity of COVID-19 122 pneumonia, and to identify potential correlation between LUS patterns and clinical outcome of the 123 patient. 124

The primary outcome was the correlation between LUS patterns and patients' mortality. The 125 following LUS parameters were compared among survived and dead patients in order to identify 126 predictors of mortality: mean LUS score, total number of pathological areas at LUS examination, and 127 percentage of pathological areas at LUS examination. Patients who were discharged home from the 128 ED were considered discharged unless a subsequent admission to our hospital was recorded. When 129 this happened, we considered the LUS evaluation at the time of the first ED contact and the outcome 130 from the last admission. 131

The secondary outcomes were: to describe the LUS patterns of adult patients with COVID-19 132 evaluated in the ED, to correlate LUS parameters (score, total number and percentage of pathological 133 areas) with the need for intensive care unit (ICU) admission and invasive ventilation and to evaluate 134 the concordance between ultrasound examination and standard chest X-ray. 135

Outcome of the hospital stay was considered as a dichotomous variable for which only two 137 values were possible (either death or home discharge). ICU admission was considered a dichotomous 138 variable. 139

For each patient we calculated, as said above, the following measures: average score as the 140 mean of the scores in the examined areas, absolute number of areas with a score equal to or higher 141 than 0, percentage of examined areas with a score of or higher than 0. These values were considered 142 continuous variables. Normality was assessed by visual inspection of the resulting distribution of 143 these variables. Therefore, patients were divided according to final outcome of the hospital stay 144 (death/discharge), need for ICU admission (Yes/No), need for invasive ventilation (Yes/No) and the 145 above-described measures were compared between each pair of groups with the Mann-Whitney U 146 test for non-normal parameters and Student's t-test for normal parameters. As none of these variables 147 was normally distributed, comparisons were made only by Mann-Whitney U test. Significance level 148 was put at 0.05, two-sided. 149

Concordance between chest X-ray and LUS for the detection of overt COVID19 pneumonia by 150 calculating the Cohen's kappa for different score and percentage of involved areas cutoffs. LUS was performed in a total of 41 patients (a total of 494 lung areas examined). In 12 patients 169 (29.3%), due to the clinical conditions of the patients that did not allow the complete evaluation of 170 all 14 areas, the posterior regions (areas 1 to 6) were not evaluated. In 8 patients, area 13 was not 171 evaluated because of superimposed cardiac window. Additionally, 12 areas overall were not 172 examined due to technical reasons (e.g.: the physician had to stop the exam due to emergent clinical 173 situations, access to a certain area obstructed by medical devices, patient's discomfort). LUS was 174 normal in 3 patients (7.3%, all with negative chest X-Ray as well) while 38 patients (92.7%) had at 175 least a pathological pattern in one lung area. Score 0 was detected in 194 areas, score 1 in 147 areas, 176 score 2 in 110 areas, score 3 in 31. Figure 3a shows the distribution of the pathological patterns 177 (score 1 to 3) in the 14 lung areas evaluated. Score 1 and 2 are those most represented. All lung areas 178 were involved by pathological patterns, although the lateral lung areas (areas 7 to 10) were those 179 more involved by pathological areas (Figure 3b) . 180

Patients in our sample had a median of 6 lung areas with pathological findings (IQR 6, range 183 0-14), defined as a score different from 0. The median rate of lung areas involved was 71% (IQR 184 64%, range 0-100) while the median average score was 1.14 (IQR 0.93, range 0-3). There was a 185 significant difference for both average score and rate of involved lung areas between subjects who 186 died during index hospitalization and those who were discharged home. Patients who subsequently 187 died had a median rate of involved areas of 100% (IQR 81.5-100%, range 71-100%) while those 188 discharged had a median rate of involved areas of 50% (IQR 27-81.5%, range 0-100%), with an 189 estimated difference of 40.5% (95% CI 4-68%, p .01), see figure 4. 190 Patients who subsequently died had a median average score of 1.43 (IQR 1.31-1.69, range 191 1.14-3) compared with those who were discharged, who had a median average score of 1 (IQR 0.27-192 1.4, range 0-1.86), with an estimated difference of 0.47 (95% CI 0.06 -0.93, p .02), see figure 5 . 193 There was no significant difference among the two groups regarding the absolute number of areas 194 with pathological findings, see figure 6 . 195

There was a higher rate of involved lung areas among patients admitted to ICU anytime during 197 hospital stay than among those who did not require ICU admission ( figure 4) . Respectively, ICU 198 admitted patients had a median of 93% involved areas (IQR 71-100%, range 0-100%), while patients 199 who did not require ICU admission had a median rate of 20% involved areas (IQR 0-42.5%, range 0-200 50%), with an estimated difference of 29% (95% CI 8-50%, p .008). Average score was also higher 201 among patients who required ICU admission (figure 5). Patients who required a subsequent ICU 202 admission had a median average score of 1.36 (IQR 1.2-1.58, range 0-3), while those who did not 203 require ICU admission had a median average score of 1 (IQR 0.39-1.38, range 0-1.86), with an 204 estimated difference of 0.47 (95% CI 0.05-0.93, p .02). 205

Absolute number of involved lung areas was also significantly different between patients who 206 required ICU admission and those who did not (estimated difference 4, 95% CI 1-7, p .016), see 207 figure 6. 208

Conversely, differences in average score, rate of involved areas or absolute number of involved 210 areas did not reach statistical significance when compared between patients who required invasive 211 ventilation and those who did not. 212

Given the absence of a strict definition of COVID19 pneumonia at LUS examination, we 214 evaluated concordance for various cutoff values, both for the average score and for the rate of 215 involved areas (figure 7). 216

The best concordance was observed for a cutoff of 0.4 for score (Cohen's kappa 0.72, 95% CI 217 0.4-1) and of 20% for rate of involved areas (Cohen's kappa 0.53, 95% CI 0.14-0.93). 218

In the last decades, lung ultrasonography has emerged as an accurate tool for the point-of-care 220 diagnosis of many chest conditions, particularly pneumonia, pulmonary edema and pleural effusion 221 in several settings, including emergency and critical care. In last years, technological advances have 222 led to smaller devices, up to the pocket size, with good image quality, particularly fit to be deployed 223 in resource-poor setting and outside of hospital environment. Moreover, compared to traditional 224 radiological techniques, LUS is particularly convenient for populations for whom exposure to 225 ionizing radiations is a concern (for example children and pregnant women). 226

In this study, we prospectively evaluated the predictive role of LUS performed during the first 227 evaluation in the ED of patients with COVID-19. Moreover, we prospectively assessed the 228 correlation between LUS findings and severity of disease in COVID19 patients evaluated in a 229 referral ED of a large COVID-19 university hospital. We found a significant correlation between 230 ultrasound findings and severity of the disease, assessed as mortality and need for ICU admission. To 231 our knowledge, this is the first study describing the predictive role of LUS in patients with COVID-232

First, we showed that LUS is able to detect COVID-19 pneumonia in the ED. 92.7% of patients 234 included in the analysis had at least a pathological pattern in one lung area. Importantly, we showed 235 that all lung areas (posterior, lateral and anterior) can be involved by different degrees of 236 pathological patterns, highlighting the importance of always assess all lung areas if the clinical 237 conditions of the patient allow it. 238

Secondly, we assessed the predictive role of LUS in terms of mortality/survival and need for 239 ICU admission. In order to quantify the LUS severity, we used the score of Soldati et al (Soldati et al. 240 2020c). We chose this score for several reasons: it is the first score proposed and already used by 241 several Institutions in Italy and in the world, receiving a huge impact assessed by altimetrics data. 242 Also, the score was validated by an Italian task force of LUS independent experts working in 243 different settings, using a large virtual database that so far collected a total of 45,560 and 13,364 244 frames, from different countries (Roy et al. 2020) . Moreover, this score is based on simple LUS 245 patterns already used in several lung conditions (pleural line characteristics, vertical artifacts, 246 consolidations, white lung). 247

Interestingly, we found that the percentage of pathological lung areas on LUS and the LUS 248 score were both able to significantly predict the final clinical outcome (death/survival) and need for 249 ICU admission. Of note, in our study none of the patients with less than 70% pathological areas or 250 with an average score of less than 1.1 died. Conversely, the number of pathological areas in each 251 patient were not significantly associated with a specific outcome. However, this finding is probably 252 due to the impossibility in analyzing all lung areas in several patients with impossibility to have 253 posterior areas analyzed, due to clinical reasons. For this reason, we included in our study the 254 percentage of pathological areas for each patient. 255

While the role of LUS as a pre-triage tool or to optimize resources has been hypothesized (11, 256 25), it has not been documented before. In fact, several authors during the last weeks have assessed 257 the ability of LUS in detecting COVID-19 pneumonia, but not assessed its predictive role (Buonsenso 258 et al. 2020c; Huang et al. 2020; Lu et al. 2020b; Peng et al. 2020; Volpicelli and Gargani 2020) . To 259 those finding, we add that the score proposed by Soldati and colleagues, which uses well known and 260 characterized LUS patterns and which has been analyzed on a large virtual and multicenter 261 database (Roy et al. 2020) , has also a predictive ability and is associated with the need for ICU 262 admission and outcome. Given the shortage of trained health-care personnel and equipment faced by 263 all involved countries during the COVID19 pandemics, this finding may have clinical and public 264 health implications. The use of a relatively simple diagnostic procedure such as LUS (Buonsenso et 265 al. 2020a; De Rose et al. 2020; Moro et al. 2020) , and the possibility of using it in outpatient settings 266 or even at home with pocket devices, can help health authorities for appropriate resources allocation, 267 early identification of patients at higher risk, identification of patients that could start early 268 treatments at home, home/out-patient follow-up with early recognition of those with worsening LUS 269 patterns that might benefit of hospitalization, particularly in resource-poor settings. 270

Third, although it was not the aim of the study, we assessed concordance of LUS with Chest X-271

Ray. The concordance between LUS and CT scan was not methodologically possible since only 17 272 patients (41.5%) performed CT scan and all were pathological. This finding may derive from the fact 273 that, at the peak of COVID19 incidence, with high number of patients hospitalized compared to 274 available resources, CT scan was reserved only for most severe cases. Anyway, all patients with 275 positive CT scan also had a positive LUS. Regarding the concordance between LUS and Chest X-276 Ray, using a cutoff of LUS score of 0.4 and 20% pathological areas as a positive LUS, the 277 concordances were k 0.72 (good) and 0.53 (moderate) between the two methodologies. 278

There are several limitations in our paper that should be kept in mind by the reader. First, our 279 study was performed only on patients with microbiologically confirmed COVID19 disease, thus we 280 did not evaluate the diagnostic performance of LUS in a mixed population. However, the stated goal 281 of our study was to evaluate LUS not as a diagnostic tool for COVID19, but as tool to identify more 282 severe disease and patients with a worse prognosis. Secondly, we had a relatively small sample, 283 which did not allow us to evaluate whether other variables were more predictive of the outcome, thus 284 potentially reducing the usefulness of LUS. Third, our study was performed in a referral center for 285 COVID19 during the peak incidence of SARS-CoV-2 infection and, thus both selection and attention 286 bias may have unpredictably affected our results, so that generalizing our results to other settings or 287 other time periods should be done with caution. 288

On the other hand, strengths of our study include prospective design, standardized ultrasound 289 examination, simple and standardized scoring system and the fact that our study was conducted at the 290 early stage of hospital admission, before therapeutic interventions or subsequent worsening of the 291 clinical picture. 292

In conclusion, our study shows that LUS, performed in the ED by emergency physicians, is 293 able to predict at the first evaluation the overall prognosis of COVID19 patients, recognizing those 294 needing ICU admission and those at higher risk of death. Further studies are needed to evaluate 295 whether LUS findings may be safely used to prioritize hospital admissions, or to guide early ICU 296 admissions or secondary level treatments (including new treatments or ventilatory supports). 297

Subsequent studies should also evaluate the usefulness of LUS in the outpatient setting. 298 Soldati et al (13) . Figures 1a to 1d show the localization of the 14 areas evaluated with Lung

Ultrasound. Figure 1e shows a Lung ultrasound score 0 (normal pattern), with clear A-lines (horizontal artifacts, white arrows). Figure 1f shows a Lung ultrasound score 1, with pleural line irregularity (white, thick arrow) and a single vertical artifact (B-line, white arrow). Figure 1g shows a Lung ultrasound score 2, with pleural line irregularity (white, thick arrow) and a multiple but not confluent vertical artifacts (B-lines, white arrow). Figure 1h shows a Lung ultrasound score 3, with a subpleural consolidation (black arrow) and a large area of white lung (double-head black arrow). 

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We wish to express our gratitude to Riccardo Inchingolo, MD, and to Andrea Smargiassi, MD who, as members of the Italian Academy of Thoracic Ultrasound (Accademia Italiana di Ecografia Toracica, ADET), have provided training on chest ultrasound findings in COVID19 to all healthcare personnel of our hospital involved in the management of COVID19 patients.We would also like to thank all the healthcare personnel of our hospital, and particularly of the Emergency Department, for their invaluable support and for their bravery and commitment during the COVID19 pandemics. This study would not have been possible without them.