Abstract
Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.
Zusammenfassung
Die exspiratorische Flusslimitierung (EFL) ist ein oft unerkannter klinischer Zustand mit einer Vielzahl von negativen Auswirkungen. Es wird ein mathematisches EFL-Modell vorgestellt, um die Flusslimitierung automatisch zu erkennen. Das EFL-Modell ist ein schaltbares Ein-Kompartiment-Lungenmechanikmodell mit einem volumenabhängigen Atemwegswiderstand zur Simulation des dynamischen Verhaltens während der Exspiration. Die EFL-Erkennung basiert auf einer atemzugsweisen Parameteridentifikation und wird anhand klinischer Daten mechanisch beatmeter Patienten validiert. In der Gruppe mit schwerer Flusslimitierung wurden 93.9 % ± 5 % und in der Gruppe ohne Begrenzung wurden 10.2 % ± 13.7 % der Atemzüge als EFL erkannt. Basierend auf der hohen Erkennungsrate der EFL unterstützen diese Ergebnisse die Nützlichkeit der EFL-Erkennung. Dies ist ein erster Schritt in Richtung einer automatischen Erkennung von EFL in klinischen Anwendungen und kann dazu beitragen, die Unterdiagnose von EFL zu verringern.
About the authors
Carlotta Hennigs holds a B.Sc. degree since 2018 and a M.Sc. degree since 2020 in Medical Engineering Science from Universität zu Lübeck. Since 2020 she is employed as research associate at the Institute of Electrical Engineering in Medicine at the Universität zu Lübeck.
Franziska Bilda holds a M.Sc. degree in Electrical Engineering, Information Technology and Computer Engineering from RWTH Aachen University, since 2018. She is employed as research associate at the Institute of Electrical Engineering in Medicine at the Universität zu Lübeck since 2021.
Jan Graßhoff received the B.Sc. and M.Sc. degrees in computer science from Universität zu Lübeck, Germany, in 2014 and 2016, respectively, and is currently pursuing the Ph.D degree. Since 2016, he has been working as a Research Associate with the Institute of Electrical Engineering, Universität zu Lübeck. Since 2020, Jan Graßhoff has been with the Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE, Lübeck, Germany where he is working on novel machine learning techniques for medical devices. His research interests include probabilistic signal processing and parameter/state estimation problems in biomedical applications with a focus on Gaussian processes. In particular, he works on respiratory signal processing and system modeling in the context of mechanical ventilation.
Stephan Walterspacher is a medical doctor, who specialized in respiratory medicine/pneumology and is habilitated for internal medicine at the University of Witten-Herdecke. His main academic interest is in the assessment of respiratory drive and muscle function in health and disease as well as in special conditions such as breath-hold diving. Furthermore, he is engaged in studies of noninvasive mechanical ventilation and works in different guideline committees for the German Respiratory Society (DGP e.V.).
Philipp Rostalski is a professor of Electrical Engineering in Medicine and founding director of the corresponding institute at the University of Lübeck. Since 2020 he is also director at the Fraunhofer Research Institution for Individualized and Cell-based Medical Engineering (IMTE) in Lübeck. He received his Ph.D. degree from ETH Zurich, Switzerland, and served as a Feodor Lynen Scholar at the Department of Mathematics and the Department of Mechanical Engineering, University of California Berkeley, USA. His research activities include model- and data-driven methods in signal processing and control with a particular focus on safety-critical systems. His primary application domains are biomedical and autonomous systems.
Acknowledgments
All patients are gratefully acknowledged for their participation in the study, as well as Dr. Franziska Farquharson/Konstanz who greatly contributed to data acquisition.
-
Research ethics: The used data were collected during a study at the Department of Pneumology, Cardiology, and Intensive Care Medicine at the academic teaching hospital of Konstanz, Germany. The study was officially registered in the German Clinical Trials Register (DRKS00021524).
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The authors state no conflict of interest.
-
Research funding: F. Bilda was supported by “KI-Med Ökosystem” within the AI funding policy of Schleswig Holstein (“KI-Förderrichtlinie”, Project No. 220 21 019). J. Graßhoff and P. Rostalski were supported by the European Union-European Regional Development Fund (ERDF), the Federal Government and Land Schleswig Holstein, Project No. 12420002.
-
Data availability: Not applicable.
References
[1] D. Junhasavasdikul, et al., “Expiratory flow limitation during mechanical ventilation,” Chest, vol. 154, no. 4, pp. 948–962, 2018. https://doi.org/10.1016/j.chest.2018.01.046.Search in Google Scholar PubMed
[2] C. Tantucci, “Expiratory flow limitation definition, mechanisms, methods, and significance,” Pulm. Med., vol. 2013, pp. 749–860, 2013. https://doi.org/10.1155/2013/749860.Search in Google Scholar PubMed PubMed Central
[3] M. S. Lourens, B. V. Berg, H. C. Hoogsteden, and J. M. Bogaard, “Detection of flow limitation in mechanically ventilated patients,” Intensive Care Med., vol. 27, pp. 1312–1320, 2001. https://doi.org/10.1007/s001340101010.Search in Google Scholar PubMed
[4] World Health Organization, Global Strategy on Human Resources for Health: Workforce 2030, Geneva, Switzerland, WHO Document Production Services, 2016.Search in Google Scholar
[5] C. Hennigs, F. Schollemann, J. Graßhoff, T. Hardel, and P. Rostalski, “A simple mathematical lung model of chronic obstructive pulmonary disease (copd),” in Proceedings on Automation in Medical Engineering, vol. 2, 2023.Search in Google Scholar
[6] G. Bellani, Mechanical Ventilation from Pathophysiology to Clinical Evidence, Cham, Springer, 2022.10.1007/978-3-030-93401-9Search in Google Scholar
[7] S. E. Evans and P. D. Scanlon, “Current practice in pulmonary function testing,” Mayo Clin. Proc., vol. 78, no. 6, pp. 758–763, 2003. https://doi.org/10.4065/78.6.758.Search in Google Scholar PubMed
[8] J. Vestbo, et al., “Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary,” Am. J. Respir. Crit. Care Med., vol. 187, no. 4, pp. 347–365, 2007. https://doi.org/10.1164/rccm.201204-0596pp.Search in Google Scholar PubMed
[9] H. K. Reddel, et al., “Global initiative for asthma strategy 2021: executive summary and rationale for key changes,” Am. J. Respir. Crit. Care Med., vol. 205, no. 1, pp. 17–35, 2022. https://doi.org/10.1016/j.jaip.2021.10.001.Search in Google Scholar PubMed
[10] T. M. Dempsey and P. D. Scanlon, “Pulmonary function tests for the generalist: a brief review,” Mayo Clin. Proc., vol. 93, no. 6, pp. 763–771, 2018. https://doi.org/10.1016/j.mayocp.2018.04.009.Search in Google Scholar PubMed
[11] M. Nozoe, et al., “Relationship between spontaneous expiratory flow-volume curve pattern and air-flow obstruction in elderly COPD patients,” Respir. Care, vol. 58, no. 10, pp. 1643–1648, 2013. https://doi.org/10.4187/respcare.02296.Search in Google Scholar PubMed
[12] K. E. Wurst, K. Kelly-Reif, G. A. Bushnell, S. Pascoe, and N. Barnes, “Understanding asthma-chronic obstructive pulmonary disease overlap syndrome,” Respir. Med., vol. 110, pp. 1–11, 2016. https://doi.org/10.1016/j.rmed.2015.10.004.Search in Google Scholar PubMed
[13] J. Schmidt, et al., “Improved lung recruitment and oxygenation during mandatory ventilation with a new expiratory ventilation assistance device: a controlled interventional trial in healthy pigs,” Eur. J. Anaesthesiol., vol. 35, no. 10, pp. 736–744, 2018. https://doi.org/10.1097/eja.0000000000000819.Search in Google Scholar
[14] J. H. T. Bates, Lung Mechanics: An Inverse Modeling Approach, Leiden, Cambridge University Press, 2009.10.1017/CBO9780511627156Search in Google Scholar
[15] D. P. Redmond, K. T. Kim, S. E. Morton, S. L. Howe, Y. S. Chiew, and J. G. Chase, “A variable resistance respiratory mechanics model,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 6660–6665, 2017. https://doi.org/10.1016/j.ifacol.2017.08.1533.Search in Google Scholar
[16] R. Farré, et al.., “Respiratory mechanics in ventilated COPD patients: forced oscillation versus occlusion techniques,” Eur. Respir. J., vol. 12, pp. 170–176, 1998. https://doi.org/10.1183/09031936.98.12010170.Search in Google Scholar PubMed
[17] P. Barbini, G. Cevenini, and G. Avanzolini, “Nonlinear mechanisms determining expiratory flow limitation in mechanical ventilation: a model-based interpretation,” Ann. Biomed. Eng., vol. 31, pp. 908–916, 2003. https://doi.org/10.1114/1.1590665.Search in Google Scholar PubMed
[18] S. Khirani, L. Biot, P. Lavagne, A. Duguet, T. Similowski, and P. Baconnier, “Identification of a non-linear model as a new method to detect expiratory airflow limitation in mechanically ventilated patients,” Acta Biotheor., vol. 52, no. 4, pp. 241–254, 2004. https://doi.org/10.1023/b:acbi.0000046596.43503.36.10.1023/B:ACBI.0000046596.43503.36Search in Google Scholar PubMed
[19] W. Wang, et al., “Can computer simulators accurately represent the pathophysiology of individual COPD patients,” Intensive Care Med. Exp., vol. 2, no. 1, p. 23, 2014. https://doi.org/10.1186/s40635-014-0023-0.Search in Google Scholar PubMed PubMed Central
[20] A. Athanasiades, et al.., “Energy analysis of a nonlinear model of the normal human lung,” J. Biol. Syst., vol. 8, pp. 115–139, 2000. https://doi.org/10.1142/s0218339000000080.Search in Google Scholar
[21] J. Mead, et al.., “Significance of the relationship between lung recoil and maximum expiratory flow,” J. Appl. Physiol., vol. 22, pp. 95–108, 1967. https://doi.org/10.1152/jappl.1967.22.1.95.Search in Google Scholar PubMed
[22] T. Lerios, J. Knopp, L. R. H. Pearson, E. F. S. Guy, and J. Chase, “An identifiable model of lung mechanics to diagnose and monitor COPD,” Comput. Biol. Med., vol. 152, p. 106430, 2022.10.1016/j.compbiomed.2022.106430Search in Google Scholar PubMed
[23] F. M. E. Franssen, et al.., “Personalized medicine for patients with COPD: where are we?” Int. J. Chronic Obstruct. Pulm. Dis., vol. 14, pp. 1465–1484, 2019. https://doi.org/10.2147/copd.s175706.Search in Google Scholar
[24] J. Graßhoff, et al.., “Surface EMG-based quantification of inspiratory effort: a quantitative comparison with Pes,” Crit. Care, vol. 25, no. 1, p. 441, 2021. https://doi.org/10.1186/s13054-021-03833-w.Search in Google Scholar PubMed PubMed Central
[25] M. R. Miller, “Standardisation of spirometry,” Eur. Respir. J., vol. 26, pp. 319–338, 2005. https://doi.org/10.1183/09031936.05.00034805.Search in Google Scholar PubMed
[26] M. Sarkar, I. V. Madabhavi, S. Mehta, and S. Mohanty, “Use of flow volume curve to evaluate large airway obstruction,” Monaldi Arch. Chest Dis., vol. 92, no. 4, p. 1947, 2022. https://doi.org/10.4081/monaldi.2022.1947.Search in Google Scholar PubMed
[27] D. Garcia-Castellote, A. Torres, L. Estrada, L. Sarlabous, and R. Jane, “Evaluation of indirect measures of neural inspiratory time from invasive and noninvasive recordings of respiratory activity,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2017, 2017, pp. 341–344.10.1109/EMBC.2017.8036832Search in Google Scholar PubMed
[28] L. Jarenbäck, J. Ankerst, L. Bjermer, and E. Tufvesson, “Flow-volume parameters in COPD related to extended measurements of lung volume, diffusion, and resistance,” Pulm. Med., vol. 2013, pp. 1–10, 2013. https://doi.org/10.1155/2013/782052.Search in Google Scholar PubMed PubMed Central
[29] C. Guérin, et al., “Lung and chest wall mechanics in patients with acute respiratory distress syndrome, expiratory flow limitation, and airway closure,” J. Appl. Physiol., vol. 128, no. 6, pp. 1594–1603, 2020. https://doi.org/10.1152/japplphysiol.00059.2020.Search in Google Scholar PubMed
[30] G. Natalini, et al.., “Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods,” J. Clin. Monit. Comput., vol. 35, pp. 913–921, 2021. https://doi.org/10.1007/s10877-020-00552-5.Search in Google Scholar PubMed PubMed Central
© 2024 Walter de Gruyter GmbH, Berlin/Boston
