医学
慢性阻塞性肺病
肺炎
肺超声
放射科
金标准(测试)
前瞻性队列研究
肺癌
肺
接收机工作特性
内科学
作者
Luca Rinaldi,S Milione,Maria Chiara Fascione,Pia Clara Pafundi,Concetta Altruda,Mafalda Di Caterino,Lucio Monaco,Alfonso Reginelli,Fabio Perrotta,Giovanni Porta,Mario Venafro,Carlo Acierno,Davide Mastrocinque,Mauro Giordano,Andrea Bianco,Ferdinando Carlo Sasso,Luigi Elio Adinolfi
出处
期刊:Respirology
[Wiley]
日期:2019-08-02
卷期号:25 (5): 535-542
被引量:31
摘要
ABSTRACT Background and objective The aim of this study was to assess the role of lung ultrasound (LUS) in a diagnostic algorithm of respiratory diseases, and to establish the accuracy of LUS compared with chest radiography (CXR). Methods Over a period of 2 years, 509 consecutive patients admitted for respiratory‐related symptoms to both emergency and general medicine wards were enrolled and evaluated using LUS and CXR. LUS was conducted by expert operators who were blinded to the medical history and laboratory data. Computed tomography (CT) of the chest was performed in case of discordance between the CXR and LUS, suspected lung cancer and an inconclusive diagnosis. Diagnosis made by CT was considered the gold standard. Results The difference in sensitivity and specificity between LUS and CXR as demonstrated by ROC curve analyses (LUS‐AUROC: 0.853; specificity: 81.6%; sensitivity: 93.9% vs CXR‐AUROC: 0.763; specificity: 57.4%; sensitivity: 96.3%) was significant ( P = 0.001). Final diagnosis included 240 cases (47.2%) of pneumonia, 44 patients with cancer (8.6%), 20 patients with chronic obstructive pulmonary disease (COPD, 3.9%), 24 patients with heart failure (4.7%) and others (6.1%). In 108 patients (21.2%) with any lung pathology, a CT scan was performed with a positive diagnosis in 96 cases (88.9%); we found that CXR and LUS detected no abnormality in 24 (25%) and 5 (5.2%) cases, respectively. LUS was concordant with the final diagnosis ( P < 0.0001), and in healthy patients, there was a low percentage of false positives (5.9%). Conclusion The results support the routine use of LUS in the clinical context.
科研通智能强力驱动
Strongly Powered by AbleSci AI