Abstract
| Original language | English |
|---|---|
| Article number | 730857 |
| Journal | Frontiers in physiology |
| Volume | 12 |
| DOIs | |
| Publication status | Published - 14 Sept 2021 |
Keywords
- ARDS
- ICU
- lung ultrasonography
- mechanical ventilation
- phenotype
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In: Frontiers in physiology, Vol. 12, 730857, 14.09.2021.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Lung Ultrasound Assessment of Focal and Non-focal Lung Morphology in Patients With Acute Respiratory Distress Syndrome
AU - Pierrakos, Charalampos
AU - Smit, Marry R.
AU - Pisani, Luigi
AU - Paulus, Frederique
AU - Schultz, Marcus J.
AU - Constantin, Jean-Michel
AU - Chiumello, Davide
AU - Mojoli, Francesco
AU - Mongodi, Silvia
AU - Bos, Lieuwe D. J.
N1 - Funding Information: morphology versus low positive end-expiratory pressure for patients with acute respiratory distress syndrome in France (the LIVE study): a multicentre, single-blind, randomised controlled trial. Lancet Respir. Med. 7, 870–880. doi: 10.1016/ S2213-2600(19)30138-9 Corradi, F., Brusasco, C., Vezzani, A., Santori, G., Manca, T., Ball, L., et al. (2016). Computer-Aided Quantitative Ultrasonography for Detection of Pulmonary Edema in Mechanically Ventilated Cardiac Surgery Patients. Chest 150, 640– 651. doi: 10.1016/j.chest.2016.04.013 Costamagna, A., Pivetta, E., Goffi, A., Steinberg, I., Arina, P., Mazzeo, A. T., et al. (2021). Clinical performance of lung ultrasound in predicting ARDS morphology. Ann. Intensive Care 11:51. doi: 10.1186/s13613-021-00 837-1 Ferguson, N. D., Fan, E., Camporota, L., Antonelli, M., Anzueto, A., Beale, R., et al. (2012). The Berlin definition of ARDS: an expanded rationale, justification, and supplementary material. Intensive Care Med. 38, 1573–1582. doi: 10.1007/ s00134-012-2682-1 Haddam, M., Zieleskiewicz, L., Perbet, S., Baldovini, A., Guervilly, C., Arbelot, C., et al. (2016). Lung ultrasonography for assessment of oxygenation response to prone position ventilation in ARDS. Intensive Care Med. 42, 1546–1556. doi: 10.1007/s00134-016-4411-7 Matthay, M. A., Arabi, Y. M., Siegel, E. R., Ware, L. B., Bos, L. D. J., Sinha, P., et al. (2020). Phenotypes and personalized medicine in the acute respiratory distress syndrome. Intensive Care Med. 46, 2136–2152. doi: 10.1007/s00134-020-06 296-9 Matthay, M. A., Zemans, R. L., Zimmerman, G. A., Arabi, Y. M., Beitler, J. R., Mercat, A., et al. (2019). Acute respiratory distress syndrome. Nat. Rev. Dis. Primers 5:18. doi: 10.1038/s41572-019-0069-0 Mojoli, F., Bouhemad, B., Mongodi, S., and Lichtenstein, D. (2019). Lung Ultrasound for Critically Ill Patients. Am. J. Respir. Crit. Care Med. 199, 701–714. doi: 10.1164/rccm.201802-0236CI Mongodi, S., Bonaiti, S., Stella, A., Colombo, A., Santangelo, E., Vaschetto, R., et al. (2019a). Lung Ultrasound for Daily Monitoring and Management of ARDS Patients. Clin. Pulm. Med. 26, 92–97. doi: 10.1097/CPM.00000000000 00311 Mongodi, S., Santangelo, E., Bouhemad, B., Vaschetto, R., and Mojoli, F. (2019b). Personalised mechanical ventilation in acute respiratory distress syndrome: the right idea with the wrong tools? Lancet Respir. Med. 7:e38. doi: 10.1016/S2213-2600(19)30353-4 Mongodi, S., Bouhemad, B., Orlando, A., Stella, A., Tavazzi, G., Via, G., et al. (2017). Modified Lung Ultrasound Score for Assessing and Monitoring Pulmonary Aeration. Ultraschall Med. 38, 530–537. doi: 10.1055/s-0042-12 0260 Mongodi, S., De Luca, D., Colombo, A., Stella, A., Santangelo, E., Corradi, F., et al. (2021). Quantitative Lung Ultrasound: technical Aspects and Clinical Applications. Anesthesiology 134, 949–965. doi: 10.1097/ALN. 0000000000003757 Pisani, L., De Nicolo, A., Schiavone, M., Adeniji, A. O., De Palma, A., Di Gennaro, F., et al. (2021). Lung Ultrasound for Detection of Pulmonary Complications in Critically Ill Obstetric Patients in a Resource-Limited Setting. Am. J. Trop. Med. Hyg. 104, 478–486. doi: 10.4269/ajtmh.20-0996 Pisani, L., Vercesi, V., van Tongeren, P. S. I., Lagrand, W. K., Leopold, S. J., Huson, M. A. M., et al. (2019). The diagnostic accuracy for ARDS of global versus regional lung ultrasound scores - a post hoc analysis of an observational study in invasively ventilated ICU patients. Intensive Care Med. Exp. 7, 1–11. doi: 10.1186/s40635-019-0241-6 R Development Core Team R. (2011). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. doi: 10.1007/978-3-540-74686-7 Ranieri, V. M., Rubenfeld, G. D., Thompson, B. T., Ferguson, N. D., Caldwell, E., Fan, E., et al. (2012). Acute Respiratory Distress Syndrome. JAMA 307, 2526–2533. doi: 10.1001/jama.2012. 5669 Riviello, E. D., Kiviri, W., Twagirumugabe, T., Mueller, A., Banner-Goodspeed, V. M., Officer, L., et al. (2016). Hospital Incidence and Outcomes of the Acute Respiratory Distress Syndrome Using the Kigali Modification of the Berlin Definition. Am. J. Respir. Crit. Care Med. 193, 52–59. doi: 10.1164/rccm. 201503-0584OC Rouby, J.-J., Arbelot, C., Gao, Y., Zhang, M., Lv, J., An, Y., et al. (2018). Training for Lung Ultrasound Score Measurement in Critically Ill Patients. Am. J. Respir. Crit. Care Med. 198, 398–401. doi: 10.1164/rccm.201802-0227LE Sinha, P., and Calfee, C. S. (2019). Phenotypes in acute respiratory distress syndrome. Curr. Opin. Crit. Care 25, 12–20. doi: 10.1097/MCC. 0000000000000571 Smit, M. R., Pisani, L., de Bock, E. J. E., van der Heijden, F., Paulus, F., Beenen, L. F. M., et al. (2021). Ultrasound versus Computed Tomography Assessment of Focal Lung Aeration in Invasively Ventilated ICU Patients. Ultrasound Med. Biol. 47, 2589–2597. doi: 10.1016/j.ultrasmedbio.2021. 05.019 Vercesi, V., Pisani, L., van Tongeren, P. S. I., Lagrand, W. K., Leopold, S. J., Huson, M. M. A., et al. (2018). External confirmation and exploration of the Kigali modification for diagnosing moderate or severe ARDS. Intensive Care Med. 44, 523–524. doi: 10.1007/s00134-018-5048-5 Conflict of Interest: MJS reports personal fees from Hamilton and Xenios/NovaLung, outside of the submitted work. J-MC reports personal fees and non-financial support from Drager, GE Healthcare, Sedana Medical, Baxter, and Amomed, personal fees from Fisher and Paykel Healthcare, Orion, Philips Medical, and Fresenius Medical Care, and non-financial support from LFB, and Bird Corporation, outside of the present work. FM received fees for lectures from GE Healthcare, Hamilton Medical, SEDA SpA, outside the present work. A research agreement is active between University of Pavia and Hamilton Medical, outside the present work. SM received fees for lectures from GE Healthcare, outside the present work. LDJB reports grants from the Dutch lung foundation (Young investigator grant), grants from the Dutch lung foundation (Public– Private Partnership grant), grants from the Dutch lung foundation (Dirkje Postma Award), grants from IMI and from Amsterdam UMC, outside the submitted work. Publisher Copyright: © Copyright © 2021 Pierrakos, Smit, Pisani, Paulus, Schultz, Constantin, Chiumello, Mojoli, Mongodi and Bos.
PY - 2021/9/14
Y1 - 2021/9/14
N2 - Background: The identification of phenotypes based on lung morphology can be helpful to better target mechanical ventilation of individual patients with acute respiratory distress syndrome (ARDS). We aimed to assess the accuracy of lung ultrasound (LUS) methods for classification of lung morphology in critically ill ARDS patients under mechanical ventilation. Methods: This was a post hoc analysis on two prospective studies that performed LUS and chest computed tomography (CT) scanning at the same time. Expert panels from the two participating centers separately developed two LUS methods for classifying lung morphology based on LUS aeration scores from a 12-region exam (Amsterdam and Lombardy method). Moreover, a previously developed LUS method based on anterior LUS scores was tested (Piedmont method). Sensitivity and specificity of all three LUS methods was assessed in the cohort of the other center(s) by using CT as the gold standard for classification of lung morphology. Results: The Amsterdam and Lombardy cohorts consisted of 32 and 19 ARDS patients, respectively. From these patients, 23 (45%) had focal lung morphology while others had non-focal lung morphology. The Amsterdam method could classify focal lung morphology with a sensitivity of 77% and a specificity of 100%, while the Lombardy method had a sensitivity and specificity of 100 and 61%. The Piedmont method had a sensitivity and specificity of 91 and 75% when tested on both cohorts. With both the Amsterdam and Lombardy method, most patients could be classified based on the anterior regions alone. Conclusion: LUS-based methods can accurately classify lung morphology in invasively ventilated ARDS patients compared to gold standard chest CT. The anterior LUS regions showed to be the most discriminant between focal and non-focal lung morphology, although accuracy increased moderately when lateral and posterior LUS regions were integrated in the method.
AB - Background: The identification of phenotypes based on lung morphology can be helpful to better target mechanical ventilation of individual patients with acute respiratory distress syndrome (ARDS). We aimed to assess the accuracy of lung ultrasound (LUS) methods for classification of lung morphology in critically ill ARDS patients under mechanical ventilation. Methods: This was a post hoc analysis on two prospective studies that performed LUS and chest computed tomography (CT) scanning at the same time. Expert panels from the two participating centers separately developed two LUS methods for classifying lung morphology based on LUS aeration scores from a 12-region exam (Amsterdam and Lombardy method). Moreover, a previously developed LUS method based on anterior LUS scores was tested (Piedmont method). Sensitivity and specificity of all three LUS methods was assessed in the cohort of the other center(s) by using CT as the gold standard for classification of lung morphology. Results: The Amsterdam and Lombardy cohorts consisted of 32 and 19 ARDS patients, respectively. From these patients, 23 (45%) had focal lung morphology while others had non-focal lung morphology. The Amsterdam method could classify focal lung morphology with a sensitivity of 77% and a specificity of 100%, while the Lombardy method had a sensitivity and specificity of 100 and 61%. The Piedmont method had a sensitivity and specificity of 91 and 75% when tested on both cohorts. With both the Amsterdam and Lombardy method, most patients could be classified based on the anterior regions alone. Conclusion: LUS-based methods can accurately classify lung morphology in invasively ventilated ARDS patients compared to gold standard chest CT. The anterior LUS regions showed to be the most discriminant between focal and non-focal lung morphology, although accuracy increased moderately when lateral and posterior LUS regions were integrated in the method.
KW - ARDS
KW - ICU
KW - lung ultrasonography
KW - mechanical ventilation
KW - phenotype
UR - http://www.scopus.com/inward/record.url?scp=85116053093&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fphys.2021.730857
DO - https://doi.org/10.3389/fphys.2021.730857
M3 - Article
C2 - 34594240
SN - 1664-042X
VL - 12
JO - Frontiers in physiology
JF - Frontiers in physiology
M1 - 730857
ER -