Intensive Care Medicine

, Volume 45, Issue 9, pp 1212–1218 | Cite as

Speckle tracking quantification of lung sliding for the diagnosis of pneumothorax: a multicentric observational study

  • Gary DuclosEmail author
  • Xavier Bobbia
  • Thibaut Markarian
  • Laurent Muller
  • Camille Cheyssac
  • Sarah Castillon
  • Noémie Resseguier
  • Alain Boussuges
  • Giovanni Volpicelli
  • Marc Leone
  • Laurent ZieleskiewiczEmail author



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.


Point-of-care lung ultrasound Pneumothorax Speckle tracking Lung sliding 



Authors warmly thank WINFOCUS-France group for its contribution to this work.

Compliance with ethical standard

Conflicts of interest

XB and LZ declare a competing interest as an ultrasound teacher for GE (GE MEDICAL SYSTEMS ULTRASOUND) customers. ML declares a competing interest with Amomed, Aguettant, MSD, 3 M, Pfizer, Aspen, Orion.

Supplementary material

134_2019_5710_MOESM1_ESM.pdf (95 kb)
Supplementary material 1 (PDF 95 kb). Role of each observers during the study. US ultrasound, Pnx pneumothorax

Supplementary material 2 (AVI 7296 kb). Video clip of region of interest positioning for speckle tracking analysis of the lung sliding. The region of interest presents 3 segments (yellow, blue, green)

Supplementary material 3 (AVI 2103 kb). Video clip of multimodal analysis result of speckle tracking applied on lung sliding. Left upper side (a): video clip showing visual pleural lung sliding tracking with region of interest deformation. Right upper side (b): graphic showing curves traducing strain over time of each segment. White squares correspond to maximal longitudinal strain values. The strain values vary during time due to pleural sliding induced by spontaneous breathing. Left bottom side (c): maximal longitudinal pleural strain value of each segment of the region of interest. Right bottom side (d): color diagram presenting variation of strain values over time for each segment (yellow, blue, green) from up to down. Red symbolize negative values of strain, blue symbolize positive values of strain. Absolute value is coded from pale (low) to dark (high). Colors vary over time in term of side and intensity (from pale to dark)

Supplementary material 4 (AVI 996 kb). Video clip of multimodal analysis result of speckle tracking applied on lung sliding abolition. Left upper side (a): video clip showing no visual pleural lung sliding tracking with no region of interest deformation despite spontaneous breathing and thoracic movements. Right upper side (b): graphic showing curves traducing strain over time of each segment. White squares correspond to maximal longitudinal strain values. Curves remain linear despite spontaneous breathing and thoracic movements traducing low strain values. Left downside (c): maximal longitudinal pleural strain value of each segment of the region of interest. The strain values remain low overtime despite spontaneous breathing and thoracic movements. Right bottom side (d): color diagram presenting variation of strain values over time for each segment (yellow, blue, green) from up to down. Red symbolizes negative values of strain, blue symbolizes positive values of strain. Absolute value is coded from pale (low) to dark (high). Colors do not vary and remain in pale tones

134_2019_5710_MOESM5_ESM.docx (13 kb)
Supplementary material 5 (DOCX 13 kb). Intra-observer(s) agreement analysis. ICC intra-class correlation coefficient, NLS normal lung sliding, PNX pneumothorax
134_2019_5710_MOESM6_ESM.docx (13 kb)
Supplementary material 6 (DOCX 13 kb)


  1. 1.
    Lichtenstein DA (2015) BLUE-protocol and FALLS-protocol: two applications of lung ultrasound in the critically ill. Chest 147:1659–1670CrossRefPubMedGoogle Scholar
  2. 2.
    Charbit J, Millet I, Maury C et al (2015) Prevalence of large and occult pneumothoraces in patients with severe blunt trauma upon hospital admission: experience of 526 cases in a French level 1 trauma center. Am J Emerg Med 33:796–801CrossRefPubMedGoogle Scholar
  3. 3.
    Kirkpatrick AW, Sirois M, Laupland KB et al (2004) Hand-held thoracic sonography for detecting post-traumatic pneumothoraces: the extended focused assessment with sonography for trauma (EFAST). J Trauma 57:288–295CrossRefPubMedGoogle Scholar
  4. 4.
    Volpicelli G, Elbarbary M, Blaivas M et al (2012) International evidence-based recommendations for point-of-care lung ultrasound. Intensive Care Med 38:577–591CrossRefPubMedGoogle Scholar
  5. 5.
    Moore CL, Copel JA (2011) Point-of-care ultrasonography. N Engl J Med 364:749–757CrossRefPubMedGoogle Scholar
  6. 6.
    Lichtenstein D (2014) Lung ultrasound in the critically ill. Curr Opin Crit Care 20:315–322CrossRefPubMedGoogle Scholar
  7. 7.
    Lichtenstein D, Goldstein I, Mourgeon E et al (2004) Comparative diagnostic performances of auscultation, chest radiography, and lung ultrasonography in acute respiratory distress syndrome. Anesthesiology 100:9–15CrossRefPubMedGoogle Scholar
  8. 8.
    Targhetta R, Bourgeois JM, Chavagneux R, Balmes P (1992) Diagnosis of pneumothorax by ultrasound immediately after ultrasonically guided aspiration biopsy. Chest 101:855–856CrossRefPubMedGoogle Scholar
  9. 9.
    Alrajhi K, Woo MY, Vaillancourt C (2012) Test characteristics of ultrasonography for the detection of pneumothorax: a systematic review and meta-analysis. Chest 141:703–708CrossRefPubMedGoogle Scholar
  10. 10.
    Staub LJ, Biscaro RRM, Kaszubowski E, Maurici R (2018) Chest ultrasonography for the emergency diagnosis of traumatic pneumothorax and haemothorax: a systematic review and meta-analysis. Injury 49:457–466CrossRefPubMedGoogle Scholar
  11. 11.
    Hamada SR, Delhaye N, Kerever S et al (2016) Integrating eFAST in the initial management of stable trauma patients: the end of plain film radiography. Ann Intensive Care 6:62CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Zieleskiewicz L, Fresco R, Duclos G et al (2018) Integrating extended focused assessment with sonography for trauma (eFAST) in the initial assessment of severe trauma: impact on the management of 756 patients. Injury 49:1774–1780CrossRefPubMedGoogle Scholar
  13. 13.
    Ding W, Shen Y, Yang J et al (2011) Diagnosis of pneumothorax by radiography and ultrasonography: a meta-analysis. Chest 140:859–866CrossRefPubMedGoogle Scholar
  14. 14.
    Cavaliere F, Zamparelli R, Soave MP et al (2014) Ultrasound artifacts mimicking pleural sliding after pneumonectomy. J Clin Anesth 26:131–135CrossRefPubMedGoogle Scholar
  15. 15.
    Ianniello S, Di Giacomo V, Sessa B, Miele V (2014) First-line sonographic diagnosis of pneumothorax in major trauma: accuracy of e-FAST and comparison with multidetector computed tomography. Radiol Med (Torino) 119(674–680):16Google Scholar
  16. 16.
    Sperandeo M, Maggi M, Catalano D, Trovato G (2014) No sliding, no pneumothorax: thoracic ultrasound is not an all-purpose tool. J Clin Anesth 26:425–426CrossRefPubMedGoogle Scholar
  17. 17.
    Lichtenstein D, Mezière G, Biderman P, Gepner A (1999) The comet-tail artifact: an ultrasound sign ruling out pneumothorax. Intensive Care Med 25:383–388CrossRefPubMedGoogle Scholar
  18. 18.
    Lichtenstein DA, Menu Y (1995) A bedside ultrasound sign ruling out pneumothorax in the critically ill. Lung sliding Chest 108:1345–1348CrossRefPubMedGoogle Scholar
  19. 19.
    Lichtenstein DA, Mezière GA (2008) Relevance of lung ultrasound in the diagnosis of acute respiratory failure: the BLUE protocol. Chest 134:117–125CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Press GM, Miller SK, Hassan IA et al (2014) Prospective evaluation of prehospital trauma ultrasound during aeromedical transport. J Emerg Med 47:638–645CrossRefPubMedGoogle Scholar
  21. 21.
    Richards JR, Awrey JM, Medeiros SE, McGahan JP (2017) Color and power doppler sonography for pneumothorax detection. J Ultrasound Med 36:2143–2147CrossRefPubMedGoogle Scholar
  22. 22.
    Mondillo S, Galderisi M, Mele D et al (2011) Speckle-tracking echocardiography: a new technique for assessing myocardial function. J Ultrasound Med 30:71–83CrossRefPubMedGoogle Scholar
  23. 23.
    Perk G, Tunick PA, Kronzon I (2007) Non-Doppler two-dimensional strain imaging by echocardiography–from technical considerations to clinical applications. J Am Soc Echocardiogr 20:234–243CrossRefPubMedGoogle Scholar
  24. 24.
    Collier P, Phelan D, Klein A (2017) A test in context: myocardial strain measured by speckle-tracking echocardiography. J Am Coll Cardiol 69:1043–1056CrossRefPubMedGoogle Scholar
  25. 25.
    Oppersma E, Hatam N, Doorduin J et al (2017) Functional assessment of the diaphragm by speckle tracking ultrasound during inspiratory loading. J Appl Physiol Bethesda Md 123:1063–1070Google Scholar
  26. 26.
    Orde SR, Boon AJ, Firth DG et al (2016) Diaphragm assessment by two dimensional speckle tracking imaging in normal subjects. BMC Anesthesiol 16:43CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Duclos G, Muller L, Leone M, Zieleskiewicz L (2019) A picture’s worth a thousand words: speckle tracking for quantification and assessment of lung sliding. Intensive Care Med 45:101–102CrossRefPubMedGoogle Scholar
  28. 28.
    Toulouse E, Masseguin C, Lafont B et al (2018) French legal approach to clinical research. Anaesth Crit Care Pain Med 37:607–614CrossRefPubMedGoogle Scholar
  29. 29.
    Gauss T, Balandraud P, Frandon J et al (2019) Strategic proposal for a national trauma system in France. Anaesth Crit Care Pain Med 38:121–130CrossRefPubMedGoogle Scholar
  30. 30.
    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35CrossRefGoogle Scholar
  31. 31.
    Lichtenstein D, Mezière G, Biderman P, Gepner A (2000) The “lung point”: an ultrasound sign specific to pneumothorax. Intensive Care Med 26:1434–1440CrossRefPubMedGoogle Scholar
  32. 32.
    Lichtenstein DA, Lascols N, Prin S, Mezière G (2003) The “lung pulse”: an early ultrasound sign of complete atelectasis. Intensive Care Med 29:2187–2192CrossRefPubMedGoogle Scholar
  33. 33.
    Summers SM, Chin EJ, Long BJ et al (2016) Computerized diagnostic assistant for the automatic detection of pneumothorax on ultrasound: a pilot study. West J Emerg Med 17:209–215CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Dori G, Jakobson DJ (2016) Speckle tracking technology for quantifying lung sliding. Med Hypotheses 91:81–83CrossRefPubMedGoogle Scholar
  35. 35.
    Bobbia X, Muller L, Claret PG et al (2018) A new echocardiographic tool for cardiac output evaluation: an experimental study. Shock. CrossRefGoogle Scholar
  36. 36.
    Hovnanians N, Win T, Makkiya M et al (2017) Validity of automated measurement of left ventricular ejection fraction and volume using the Philips EPIQ system. Echocardiogr Mt Kisco N 34:1575–1583CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Anesthesiology and Intensive Care MedicineAix-Marseille University, Assistance Publique Hôpitaux de Marseille, Hôpital NordMarseilleFrance
  2. 2.Department of Anesthesiology, Emergency and Critical Care Medicine, Intensive Care UnitNîmes University HospitalNîmesFrance
  3. 3.Department of Emergency Medicine and Intensive CareAix-Marseille University, Assistance Publique Hôpitaux de Marseille, Timone University HospitalMarseilleFrance
  4. 4.Support Unit for Clinical Research and Economic EvaluationAssistance Publique-Hôpitaux de MarseilleMarseilleFrance
  5. 5.Service des Explorations fonctionnelles respiratoires, CHU NordPôle thoracique et cardio-vasculaire, Assistance publique des Hôpitaux de MarseilleMarseilleFrance
  6. 6.Center for Cardiovascular and Nutrition Research (C2VN)Aix Marseille Université, INSERM, INRAMarseilleFrance
  7. 7.Department of Emergency MedicineSan Luigi Gonzaga University HospitalTurinItaly

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