Abstract
Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases and a leading cause of morbidity and mortality. It is characterized by irreversible airflow limitations. We aimed to explore whether the dynamics of expiration could serve as a descriptor of airflow limitations. Additionally, we explored the relationship between dynamic components and the presence of COPD. A data-based model was developed using data from 474 subjects. Significant difference (p < 0.0001) was found comparing a group of diseased patients with healthy for each dynamic component (namely the two poles, the steady state gain (SSG) and the time constant). Moreover difference was observed for each severity stage of disease. When ranking all components, SSG and pole1 are highlighted as the best COPD descriptors. We concluded that more detailed analysis of the forced expiration can be used to expand the understanding of COPD. Furthermore, the obtained parameters may improve current COPD assessment.
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Rabe, K.F., Hurd, S., Anzueto, A., Barnes, P.J., Buist, S.A., Calverley, P., Fukuchi, Y., Jenkins, C., Rodriguez-Roisin, R., van Weel, C., Zielinski, J.: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am. J. Respir. Crit. Care Med. 176(6), 532–555 (2007)
Rennard, S.I., Vestbo, J.: Natural histories of chronic obstructive pulmonary disease. Proc. Am. Thorac. Soc. 5(9), 878–883 (2008)
Decramer, M., Janssens, W., Miravitlles, M.: Chronic obstructive pulmonary disease. Lancet 379(9823), 1341–1351 (2012)
Mathers, C.D., Loncar, D.: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006)
Murray, C.J., Lopez, A.D.: Alternative projections of mortality and disability by cause 1990-2020: global burden of disease study. Lancet 349(9064), 1498–1504 (1997)
WHO, World Health Statistic, EN_WHS08_Full.pdf. 2012 (2008). http://www.who.int/whosis/whostat/
Mannino, D.M., Buist, A.S.: Global burden of COPD: risk factors, prevalence, and future trends. Lancet 370(9589), 765–773 (2007)
Agusti, A., Calverley, P.M., Celli, B., Coxson, H.O., Edwards, L.D., Lomas, D.A., MacNee, W., Miller, B.E., Rennard, S., Silverman, E.K., Tal-Singer, R., Wouters, E., Yates, J.C., Vestbo, J.: Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir. Res. 11, 122 (2010)
Miravitlles, M., Soler-Cataluna, J.J., Calle, M., Soriano, J.B.: Treatment of COPD by clinical phenotypes: putting old evidence into clinical practice. Eur. Respir. J. 41(6), 1252–1256 (2013)
Garcia-Rio, F., Soriano, J.B., Miravitlles, M., Munoz, L., Duran-Tauleria, E., Sanchez, G., Sobradillo, V., Ancochea, J.: Overdiagnosing subjects with COPD using the 0.7 fixed ratio: correlation with a poor health-related quality of life. Chest 139(5), 1072–1080 (2011)
Bodduluri, S., Newell, Jr., J.D., Hoffman, E.A., Reinhardt, J.M.: Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework. Acad. Radiol. 20(5), 527–536 (2013)
Sorensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H., de Bruijne, M.: Texture-based analysis of COPD: a data-driven approach. IEEE Trans. Med. Imaging 31(1), 70–78 (2012)
Fens, N., Zwinderman, A.H., van der Schee, M.P., de Nijs, S.B., Dijkers, E., Roldaan, A.C., Cheung, D., Bel, E.H., Sterk, P.J.: Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am. J. Respir. Crit. Care Med. 180(11), 1076–1082 (2009)
Phillips, C.O., Syed, Y., Parthalain, N.M., Zwiggelaar, R., Claypole, T.C., Lewis, K.E.: Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls. J. Breath. Res. 6(3), 036003 (2012)
Amaral, J.L., Lopes, A.J., Jansen, J.M., Faria, A.C., Melo, P.L.: Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Comput. Methods Programs Biomed. 105(3), 183–193 (2012)
Dellaca, R.L., Santus, P., Aliverti, A., Stevenson, N., Centanni, S., Macklem, P.T., Pedotti, A., Calverley, P.M.: Detection of expiratory flow limitation in COPD using the forced oscillation technique. Eur. Respir. J. 23(2), 232–240 (2004)
Lambrechts, D., Buysschaert, I., Zanen, P., Coolen, J., Lays, N., Cuppens, H., Groen, H.J., Dewever, W., van Klaveren, R.J., Verschakelen, J., Wijmenga, C., Postma, D.S., Decramer, M., Janssens, W.: The 15q24/25 susceptibility variant for lung cancer and chronic obstructive pulmonary disease is associated with emphysema. Am. J. Respir. Crit. Care Med. 181(5), 486–493 (2010)
Topalovic, M., Exadaktylos, V., Peeters, A., Coolen, J., Dewever, W., Hemeryck, M., Slagmolen, P., Janssens, K., Berckmans, D., Decramer, M., Janssens, W.: Computer quantification of airway collapse on forced expiration to predict the presence of emphysema. Respir. Res. 14, 131 (2013)
Miller, M.R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C.P., Gustafsson, P., Jensen, R., Johnson, D.C., MacIntyre, N., McKay, R., Navajas, D., Pedersen, O.F., Pellegrino, R., Viegi, G., Wanger, J.: Standardisation of spirometry. Eur. Respir. J. 26(2), 319–338 (2005)
Quanjer, P.H., Tammeling, G.J., Cotes, J.E., Pedersen, O.F., Peslin, R., Yernault, J.C.: Lung volumes and forced ventilatory flows. work group on standardization of respiratory function tests. european community for coal and steel. official position of the european respiratory society. Rev. Mal. Respir. 11(Suppl 3), 5–40 (1994)
Taylor, C.J., Pedregal, D.J., Young, P.C., Tych, W.: Environmental time series analysis and forecasting with the Captain toolbox. Environ. Model Softw. 22(6), 797–814 (2007)
Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1987)
Young, P.C.: Recursive Estimation and Time-Series Analysis: An Introduction. Springer, Heidelberg (1984)
Young, P.: Parameter-estimation for continuous-time models - a survey. Automatica 17(1), 23–39 (1981)
Yang, Y., Pedersen, J.P.: A comparative study on feature selection in text categorization. In: Proceedings of the International Conference on Machine Learning (ICML 1997), pp. 412–420 (1997)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Healy, F., Wilson, A.F., Fairshter, R.D.: Physiologic correlates of airway collapse in chronic airflow obstruction. Chest 85(4), 476–481 (1984)
Koulouris, N.G., Hardavella, G.: Physiological techniques for detecting expiratory flow limitation during tidal breathing. Eur. Respir. Rev. 20(121), 147–155 (2011)
Papandrinopoulou, D., Tzouda, V., Tsoukalas, G.: Lung compliance and chronic obstructive pulmonary disease. Pulm. Med. 2012, 542769 (2012)
Bass, H.: The flow volume loop: normal standards and abnormalities in chronic obstructive pulmonary disease. Chest 63(2), 171–176 (1973)
Jayamanne, D.S., Epstein, H., Goldring, R.M.: Flow-volume curve contour in COPD: correlation with pulmonary mechanics. Chest 77(6), 749–757 (1980)
Acknowledgements
The authors would like to thank Geert Celis and co-workers (Respiratory Division, University Hospital Leuven, Belgium) for helping in collection of patient data and their technical support in extracting data from the Masterlab.
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Topalovic, M. et al. (2015). Exploring Expiratory Flow Dynamics to Understand Chronic Obstructive Pulmonary Disease. In: Plantier, G., Schultz, T., Fred, A., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2014. Communications in Computer and Information Science, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-319-26129-4_15
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DOI: https://doi.org/10.1007/978-3-319-26129-4_15
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