Department of Anesthesiology and Intensive Care Medicine, Aix-Marseille University, Assistance Publique Hôpitaux de Marseille, Hôpital Nord, Marseille, France.
Department of Anesthesiology, Emergency and Critical Care Medicine, Intensive Care Unit, Nîmes University Hospital, 30029, Nîmes, France.
Intensive Care Med. 2019 Sep;45(9):1212-1218. doi: 10.1007/s00134-019-05710-1. Epub 2019 Jul 29.
Lung ultrasound is used for the diagnosis of pneumothorax, based on lung sliding abolition which is a qualitative and operator-dependent assessment. Speckle tracking allows the quantification of structure deformation over time by analysing acoustic markers. We aimed to test the ability of speckle tracking technology to quantify lung sliding in a selected cohort of patients and to observe how the technology may help the process of pneumothorax diagnosis.
We performed retrospectively a pleural speckle tracking analysis on ultrasound loops from patients with pneumothorax. We compared the values measured by two observers from pneumothorax side with contralateral normal lung side. The receiver operating characteristic (ROC) curve was constructed to evaluate the performance of maximal pleural strain to detect the lung sliding abolition. Diagnosis performance and time to diagnosis between B-Mode and speckle tracking technology were compared from a third blinded observer.
We analysed 104 ultrasound loops from 52 patients. The area under the ROC curve of the maximal pleural strain value to identify lung sliding abolition was 1.00 [95%CI 1.00; 1.00]. Specificity was 100% [95%CI 93%; 100%] and sensitivity was 100% [95%CI 93%; 100%] with the best cut-off of 4%. Over 104 ultrasound loops, the blinded observer made two errors with B-Mode and none with speckle tracking. The median diagnosis time was 3 [2-5] seconds for B-Mode versus 2 [1-2] seconds for speckle tracking (p = 0.001).
Speckle tracking technology allows lung sliding quantification and detection of lung sliding abolition in case of pneumothorax on selected ultrasound loops.
基于定性且依赖操作者的肺滑动缺失评估,肺部超声用于气胸的诊断。声斑点追踪技术可通过分析声标记物来定量随时间的结构变形。我们旨在测试声斑点追踪技术定量评估选定气胸患者组中肺滑动的能力,并观察该技术如何有助于气胸的诊断过程。
我们对气胸患者的超声图像进行了胸膜斑点追踪分析。我们将两位观察者测量的气胸侧和对侧正常肺侧的值进行了比较。构建了接收者操作特征(ROC)曲线,以评估最大胸膜应变值检测肺滑动缺失的性能。第三位盲法观察者比较了 B 模式和斑点追踪技术的诊断性能和诊断时间。
我们分析了 52 例患者的 104 个超声图像。最大胸膜应变值识别肺滑动缺失的 ROC 曲线下面积为 1.00 [95%CI 1.00;1.00]。特异性为 100% [95%CI 93%;100%],敏感性为 100% [95%CI 93%;100%],最佳截断值为 4%。在 104 个超声图像中,盲法观察者使用 B 模式出现了两次错误,而使用斑点追踪技术则没有。B 模式的中位诊断时间为 3 [2-5] 秒,斑点追踪技术为 2 [1-2] 秒(p = 0.001)。
在选定的超声图像中,斑点追踪技术可定量评估肺滑动并检测气胸时的肺滑动缺失。