Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model

Serdar Abut 1 * , Fatih Doğanay 2, Abdullah Yeşilova 3, Serap Buğa 4
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1 Department of Computer Engineering, Siirt University, Siirt, Turkey
2 Department of Emergency Medicine, Edremit State Hospital, Balıkesir, Turkey
3 Department of Animal Science, Yüzüncü Yıl University, Van, Turkey
4 Primary Care Health Center, Balıkesir Provincial Health Directorate, Balıkesir, Turkey
* Corresponding Author
J CLIN MED KAZ, Volume 18, Issue 3, pp. 23-29. https://doi.org/10.23950/jcmk/10919
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ABSTRACT

Background: FEV1/FVC value is used in the diagnosis of obstructive and restrictive diseases of the lung. It is a parameter reported in the literature that it varies according to lung disease as well as weight, age and gender characteristics. The aim of this study is to investigate the relationship between age, weight, gender and height characteristics and FEV1/FVC value using a heterogeneous population using Gaussian mixture regression method.
Material and Methods: GMR was used to separate the data into components and to make a parameter estimation for each component. The analysis performed on this model revealed that the patients were divided into 5 optimal groups and that these groups showed a regular transition from obstructive pattern to restrictive pattern.
Results: The mean values of the components for FEV1/FVC were found as 50.071 (3.238), 67.034 (1.725), 82.156 (1.329), 93.592 (1.041), 98.466 (0.303), respectively. The effect of the weight on the components in terms of parameter estimation and standard errors of the components was determined as 0.445 (0.129) **, 0.226 (0.053) **, 0.173 (0.053) **, -0.036 (0.026), -0.040 (0.018) *, respectively.
Conclusion: Direct proportional relationship between the patient's weight and the severity of the obstructive pattern, and between the severity of the disease and the age of the patient in both the obstructive and restrictive pattern are expilicitly proved. Furthermore, it has been revealed that data sets containing heterogeneity can be analyzed by dividing them into sub-components using the GMR model.

CITATION

Abut S, Doğanay F, Yeşilova A, Buğa S. Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model. J CLIN MED KAZ. 2021;18(3):23-9. https://doi.org/10.23950/jcmk/10919

REFERENCES

  • Miller, M.R., et al., Standardisation of spirometry. Eur Respir J, 2005. 26(2): p. 319-38. https://doi.org/10.1183/09031936.05.00034805
  • Ferguson, G.T., et al., Office spirometry for lung health assessment in adults: a consensus statement from the National Lung Health Education Program. Chest, 2000. 117(4): p. 1146-1161. https://doi.org/10.1378/chest.117.4.1146
  • Minelli, R., Appunti dalle lezioni di fisiologia umana. La Goliardica Pavese, 1992: p. Neuroimage.
  • Emmett, M., Current Clinical Medicine 2009: Expert Consult Premium Edition by the Cleveland Clinic. Proceedings (Baylor University. Medical Center), 2009. 22(3): p. 291. https://doi.org/10.1080/08998280.2009.11928536
  • Johnson, J.D. and W.M. Theurer, A stepwise approach to the interpretation of pulmonary function tests. Am Fam Physician, 2014. 89(5): p. 359-66.
  • Vestbo, J., et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med, 2013. 187(4): p. 347-65. https://doi.org/10.1164/rccm.201204-0596PP
  • Society, A.T. and M.S.o.t.A.L. Association, Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis, 1991. 144(1202): p. e18.
  • Quanjer, P., et al., Standardized lung function testing. Bull Eur Physiopathol Respir, 1983. 19(Suppl 5): p. 1-95.
  • Pearson, M., et al., BTS guidelines for the management of chronic obstructive pulmonary disease-Foreword. Thorax, 1997. 52: p. S1-S28.
  • Stanojevic, S., et al., Reference ranges for spirometry across all ages: a new approach. American journal of respiratory and critical care medicine, 2008. 177(3): p. 253-260. https://doi.org/10.1164/rccm.200708-1248OC
  • Nunn, A. J., & Gregg, I. (1989). New regression equations for predicting peak expiratory flow in adults. BMJ (Clinical research ed.), 298(6680), 1068–1070. https://doi.org/10.1136/bmj.298.6680.1068
  • Swanney, M.P., et al., Using the lower limit of normal for the FEV1/FVC ratio reduces the misclassification of airway obstruction. Thorax, 2008. 63(12): p. 1046-1051. http://dx.doi.org/10.1136/thx.2008.098483
  • Falaschetti, E., et al., Prediction equations for normal and low lung function from the Health Survey for England. Eur Respir J, 2004. 23(3): p. 456-63. https://doi.org/10.1183/09031936.04.00055204
  • Culver, B.H., Interpretation of spirometry: we can do better than the GOLD standard. 2006, Respiratory Care.
  • Hardie, J.A., et al., Risk of over-diagnosis of COPD in asymptomatic elderly never-smokers. Eur Respir J, 2002. 20(5): p. 1117-22. https://doi.org/10.1183/09031936.02.00023202
  • Wang, P. and M.L. Puterman, Mixed logistic regression models. Journal of Agricultural, Biological, and Environmental Statistics, 1998: p. 175-200. https://doi.org/10.2307/1400650
  • Yesilova, A., et al., Locational classification of walnut (Juglans Regia L.) genotypes collected from Lake Van basin by using mixture modeling. African Journal of Agricultural Research, 2010. 5(12): p. 1509-1514. https://doi.org/10.5897/AJAR.9000471
  • Deng, Y. and A. Li, Structural Health Monitoring for Suspension Bridges. 2019: Springer. https://doi.org/10.1007/978-981-13-3347-7
  • Wang, Y. and Q. Liu, Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships. Fisheries Research, 2006. 77(2): p. 220-225. https://doi.org/10.1016/j.fishres.2005.08.011
  • Penny, W.D., Comparing dynamic causal models using AIC, BIC and free energy. Neuroimage, 2012. 59(1): p. 319-330. https://doi.org/10.1016/j.neuroimage.2011.07.039
  • Xie, W., et al., Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst Biol, 2011. 60(2): p. 150-60. https://doi.org/10.1093/sysbio/syq085
  • McDonald, R.P., An index of goodness-of-fit based on noncentrality. Journal of classification, 1989. 6(1): p. 97-103. https://doi.org/10.1007/BF01908590
  • Liddle, A.R., Information criteria for astrophysical model selection. Monthly Notices of the Royal Astronomical Society: Letters, 2007. 377(1): p. L74-L78. https://doi.org/10.1111/j.1745-3933.2007.00306.x
  • Bozdogan, H. and S.L. Sclove, Multi-sample cluster analysis using Akaike's information criterion. Annals of the Institute of Statistical Mathematics, 1984. 36(1): p. 163-180. https://doi.org/10.1007/BF02481961
  • Sclove, S.L., Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 1987. 52(3): p. 333-343. https://doi.org/10.1007/BF02294360
  • Schwarz, G., Estimating the dimension of a model. The annals of statistics, 1978. 6(2): p. 461-464. https://doi.org/10.1214/aos/1176344136
  • Sahebjami, H. and P.S. Gartside, Pulmonary function in obese subjects with a normal FEV1/FVC ratio. Chest, 1996. 110(6): p. 1425-9. https://doi.org/10.1378/chest.110.6.1425
  • García-Rio, F., Soriano, J. B., Miravitlles, M., Muñoz, L., Duran-Tauleria, E., Sánchez, G., Sobradillo, V., & Ancochea, J. (2014). Impact of obesity on the clinical profile of a population-based sample with chronic obstructive pulmonary disease. PloS one, 9(8), e105220. https://doi.org/10.1371/journal.pone.0105220
  • Mihmanlı, A., & Bozkurt, E. (2020). Effects of Laparoscopic Sleeve Gastrectomy on Respiratory Functions. Bariatric Surgical Practice and Patient Care. https://doi.org/10.1089/bari.2020.0059
  • Köchli, S., et al., Lung function, obesity and physical fitness in young children: The EXAMIN YOUTH study. Respiratory medicine, 2019. 159: p. 105813. https://doi.org/10.1016/j.rmed.2019.105813
  • Çolak, Y., et al., Overweight and Obesity May Lead to Under-diagnosis of Airflow Limitation: Findings from the Copenhagen City Heart Study. COPD: Journal of Chronic Obstructive Pulmonary Disease, 2015. 12(1): p. 5-13. https://doi.org/10.3109/15412555.2014.933955
  • Dixon, A.E. and U. Peters, The effect of obesity on lung function. Expert Rev Respir Med, 2018. 12(9): p. 755-767. https://doi.org/10.1080/17476348.2018.1506331
  • Akinbami, O.J. and X. Liu, Chronic Obstuctive Pulmonary Disease Among Adults Aged 18 and Over in the United States, 1998-2009. 2011: Citeseer.
  • Runarsdottir, S.B., et al., Prevalence of airflow obstruction in nonsmoking older individuals using different spirometric criteria: the AGES Reykjavik Study. COPD: Journal of Chronic Obstructive Pulmonary Disease, 2013. 10(4): p. 493-499. https://doi.org/10.3109/15412555.2013.773303
  • Turner, J.M., J. Mead, and M.E. Wohl, Elasticity of human lungs in relation to age. J Appl Physiol, 1968. 25(6): p. 664-71. https://doi.org/10.1152/jappl.1968.25.6.664
  • Knudson, R.J., et al., Effect of aging alone on mechanical properties of the normal adult human lung. J Appl Physiol Respir Environ Exerc Physiol, 1977. 43(6): p. 1054-62. https://doi.org/10.1152/jappl.1977.43.6.1054
  • Burrows, B., et al., The course and prognosis of different forms of chronic airways obstruction in a sample from the general population. N Engl J Med, 1987. 317(21): p. 1309-14. https://doi.org/10.1056/nejm198711193172103
  • McHugh, J., Duong, M., Ma, J., Dales, R. E., Bassim, C. W., & Verschoor, C. P. (2020). A comprehensive analysis of factors related to lung function in older adults: Cross-sectional findings from the Canadian Longitudinal Study on Aging. Respiratory medicine, 173, 106157. https://doi.org/10.1016/j.rmed.2020.106157