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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
Original

Abstract

Purpose

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.

Methods

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.

Results

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).

Conclusion

Speckle tracking technology allows lung sliding quantification and detection of lung sliding abolition in case of pneumothorax on selected ultrasound loops.

Keywords

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

Notes

Acknowledgements

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)

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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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