Goldsmith Andrew J, Al Saud Ahad, Duggan Nicole M, Ma Irene W, Huang Calvin K, Eke Onyinyechi, Kapur Tina, Kharasch Sigmund, Liteplo Andrew, Shokoohi Hamid
Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Cureus. 2022 Jan 11;14(1):e21116. doi: 10.7759/cureus.21116. eCollection 2022 Jan.
Background and objectives Patients infected with influenza and COVID-19 exhibit similar clinical presentations; thus, a point-of-care test to differentiate between the diseases is needed. Here, we sought to identify features of point-of-care lung ultrasound (LUS) that can discriminate between influenza and COVID-19. Methods In this prospective, cross-sectional study, LUS clips of patients presenting to the emergency department (ED) with viral-like symptoms were collected via a 10-zone scanning protocol. Deidentified clips were interpreted by emergency ultrasound fellows blinded to patients' clinical context and influenza or COVID-19 diagnosis. Modified Soldati scores were calculated for each lung zone. Logistic regression identified the association of pulmonary pathologies with each disease. Results Ultrasound fellows reviewed LUS clips from 165 patients, of which 30.9% (51/165) had confirmed influenza, 33.9% (56/165) had confirmed COVID-19, and 35.1% (58/165) had neither disease. Patients with COVID-19 were more likely to have irregular pleura and B-lines in all lung zones (p<0.01). The median-modified Soldati score for influenza patients was 0/20 (IQR 0-2), 9/20 (IQR 2.5-15.5) for COVID-19 patients, and 2/20 (IQR 0-8) for patients with neither disease (p<0.0001). In multivariate regression analysis adjusted for age, sex, and congestive heart failure (CHF), the presence of B-lines (OR = 1.29, 95% CI 1.09-1.53) was independently associated with COVID-19 diagnosis. The presence of pleural effusion was inversely associated with COVID-19 (OR = 0.09, 95% CI 0.01-0.65). Conclusions LUS may help providers preferentially identify the presence of influenza versus COVID-19 infection both visually and by calculating a modified Soldati score. Further studies assessing the utility of LUS in differentiating viral illnesses in patients with variable illness patterns and those with variable illness severity are warranted.
感染流感和新冠病毒的患者临床表现相似;因此,需要一种即时检验方法来区分这两种疾病。在此,我们试图确定即时肺部超声(LUS)能够区分流感和新冠病毒的特征。方法:在这项前瞻性横断面研究中,通过10区扫描方案收集了到急诊科就诊且有类似病毒症状患者的LUS影像片段。经过去识别处理的影像片段由对患者临床情况以及流感或新冠病毒诊断不知情的急诊超声研究员进行解读。计算每个肺区的改良索尔达蒂评分。逻辑回归分析确定肺部病变与每种疾病的关联。结果:超声研究员审查了165例患者的LUS影像片段,其中30.9%(51/165)确诊为流感,33.9%(56/165)确诊为新冠病毒感染,35.1%(58/165)未感染这两种疾病。新冠病毒感染患者在所有肺区更易出现不规则胸膜和B线(p<0.01)。流感患者的改良索尔达蒂评分中位数为0/20(四分位间距0 - 2),新冠病毒感染患者为9/20(四分位间距2.5 - 15.5),未感染这两种疾病的患者为2/20(四分位间距0 - 8)(p<0.0001)。在对年龄、性别和充血性心力衰竭(CHF)进行校正的多因素回归分析中,B线的出现(比值比=1.29,95%置信区间1.09 - 1.53)与新冠病毒感染诊断独立相关。胸腔积液的出现与新冠病毒感染呈负相关(比值比=0.09,95%置信区间0.01 - 0.65)。结论:LUS可能有助于医护人员通过视觉观察以及计算改良索尔达蒂评分来优先识别流感与新冠病毒感染的存在情况。有必要开展进一步研究,评估LUS在区分病情模式多样和病情严重程度不同患者的病毒感染方面的效用。