Background Severe refractory asthma is a heterogeneous disease. We sought to determine statistical clusters from the British Thoracic Society Severe refractory Asthma Registry and to examine cluster-specific outcomes and stability. Methods Factor analysis and statistical cluster modelling was undertaken to determine the number of clusters and their membership (N = 349). Cluster-specific outcomes were assessed after a median follow-up of 3 years. A classifier was programmed to determine cluster stability and was validated in an independent cohort of new patients recruited to the registry (n = 245). Findings Five clusters were identified. Cluster 1 (34%) were atopic with early onset disease, cluster 2 (21%) were obese with late onset disease, cluster 3 (15%) had the least severe disease, cluster 4 (15%) were the eosinophilic with late onset disease and cluster 5 (15%) had significant fixed airflow obstruction. At follow-up, the proportion of subjects treated with oral corticosteroids increased in all groups with an increase in body mass index. Exacerbation frequency decreased significantly in clusters 1, 2 and 4 and was associated with a significant fall in the peripheral blood eosinophil count in clusters 2 and 4. Stability of cluster membership at follow-up was 52% for the whole group with stability being best in cluster 2 (71%) and worst in cluster 4 (25%). In an independent validation cohort, the classifier identified the same 5 clusters with similar patient distribution and characteristics. Interpretation Statistical cluster analysis can identify distinct phenotypes with specific outcomes. Cluster membership can be determined using a classifier, but when treatment is optimised, cluster stability is poor.
References
[1]
Wenzel S (2012) Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med 18(5): 716–25. doi: 10.1038/nm.2678
[2]
Bousquet J, Mantzouranis E, Cruz A, A？t-Khaled N, Baena-Cagnani C, et al. (2010) Uniform definition of asthma severity, control, and exacerbations: document presented for the World Health Organisation Consultation on Severe Asthma. J Allergy ClinImmunol 126(5): 926–38. doi: 10.1016/j.jaci.2010.07.019
[3]
Blakey J, Wardlaw A (2012) What is severe asthma? Clin Exp Allergy 42(5): 617–62. doi: 10.1111/j.1365-2222.2012.03962.x
[4]
Brightling CE, Gupta S, Gonem S, Siddiqui S (2012) Lung damage and airway remodelling in severe asthma. Clin Exp Allergy 42(5): 638–49. doi: 10.1111/j.1365-2222.2011.03917.x
[5]
Haldar P, Pavord ID, Shaw DE, Berry MA, Thomas M, et al. (2008) Cluster analysis and clinical asthma phenotypes. Am J Resp Crit Care Med 178(3): 218–224. doi: 10.1164/rccm.200711-1754oc
[6]
Moore WC, Meyers DA, Wenzel SE, Teague WG, Li H, et al. (2010) Identification of asthma phenotypes using cluster analysis in the severe asthma research program. Am J RespCrit Care Med 181(4): 315–323. doi: 10.1164/rccm.200906-0896oc
[7]
Gupta S, Siddiqui S, Haldar P, Entwisle JJ, Mawby D, et al. (2010) Quantitative analysis of high-resolution computed tomography scans in severe asthma subphenotypes. Thorax 65(9): 775–781. doi: 10.1136/thx.2010.136374
[8]
Jang AS, Kwon HS, Cho YS, Bae YJ, Kim TB (2012) Identification of subtypes of refractory asthma in Korean patients by cluster analysis. Lung 191(1): 87–93. doi: 10.1007/s00408-012-9430-8
[9]
Siroux V, Basagana X, Boudier A, Pin I, Garcia-Aymerich J, et al. (2011) Identifying adult asthma phenotypes using a clustering approach. EurRespir J 38(2): 310–7. doi: 10.1183/09031936.00120810
[10]
Sutherland ER, Goleva E, King TS, Lehman E, Stevens AD, et al. (2012) Cluster Analysis of Obesity and Asthma Phenotypes. PLoS One 7(5): e36631. doi: 10.1371/journal.pone.0036631
[11]
Amelink M de Nijs SB, De Groot JC, Van Tilburg PM, Van Spiegel PI, et al. (2013) Three phenotypes of adult-onset asthma. Allergy 68(5): 674–80. doi: 10.1111/all.12136
[12]
Fitzpatrick AM, Teague WG, Meyers DA, Peters SP, Li X, et al. (2011) J Allergy Clin Immunol. 127(2): 382–389. doi: 10.1016/j.jaci.2010.11.015
[13]
Wu XD, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. (2008) Top 10 algorithms in data mining. Knowledge and Information Systems 14(1): 1–37. doi: 10.1007/s10115-007-0114-2
[14]
Melnykov V, Ranjan AM (2010) Finite mixture models and model-based clustering. Statistics Surveys 4: 80–116. doi: 10.1214/09-ss053
[15]
He Y, Pan W, Lin JZ (2006) Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Computational Statistics & Data Analysis 51(2): 641–658. doi: 10.1016/j.csda.2006.02.012
[16]
West M (1997) Hierarchical mixture models in neurological transmission analysis. Journal of the American Statistical Association 92(438): 587–606. doi: 10.1080/01621459.1997.10474011
[17]
Woolrich MW (2005) Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data. Ieee Transactions on Medical Imaging 24(1): 1–11. doi: 10.1109/tmi.2004.836545
[18]
Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6): 716–723. doi: 10.1109/tac.1974.1100705
[19]
Schwarz GE (1978) Estimating the dimension of a model. Annals of Statistics 6(2): 461–464. doi: 10.1214/aos/1176344136
[20]
Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer Journal 41(8): 578–588. doi: 10.1093/comjnl/41.8.578
[21]
Heaney LG, Brightling CE, Menzies-Gow A, Stevenson M, Niven RM (2010) British Thoracic Society Severe refractory Asthma Network. Refractory asthma in the UK: cross-sectional findings from a UK multicentre registry. Thorax 65(9): 787–794. doi: 10.1136/thx.2010.137414
[22]
Sweeney J, Brightling CE, Menzies-Gow A, Niven R, Patterson CC, et al. (2012) British Thoracic Society Severe refractory Asthma Network. Clinical management and outcome of refractory asthma in the UK from the British Thoracic Society Severe refractory Asthma Registry Thorax 67(8): 754–6. doi: 10.1136/thoraxjnl-2012-201869
[23]
Little RJA, Rubin DB (2002) Statistical Analysis with Missing Data USA. John Wiley & Sons, Inc. 11.
[24]
Gibeon D, Batuwita R, Osmond M, Heaney LG, Brightling CE, et al. (2013) Obesity associated severe asthma represents a distinct clinical phenotype- analysis of the British Thoracic Society Severe refractory Asthma Registry patient cohort according to body mass index. Chest 143(2): 406–14. doi: 10.1378/chest.12-0872
[25]
Thomson NC, Chaudhuri R, Heaney LG, Bucknall C, Niven RM, et al. (2013) C.Clinical outcomes and inflammatory biomarkers in current smokers and ex-smokers with severe asthma. J Allergy ClinImmunol 131 (4): 1008–1016. doi: 10.1016/j.jaci.2012.12.1574
[26]
Rubin DB, Schenker N (1991) Multiple imputation in healthcare databases- an overview and some applications. Statistics in Medicine 10(4): 585–598. doi: 10.1002/sim.4780100410
[27]
Zhang TR, Ramakrishnon, Livny M (1996) BIRCH: An efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data. Montreal, Canada: ACM.
[28]
Chiu T, Fang D, Chen J, Wang Y, Jeris C (2001) A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco, CA: ACM.
Haldar P, Brightling CE, Hargadon B, Gupta S, Monteiro W, et al. (2009) Mepolizumab and exacerbations of refractory eosinophilic asthma. N Engl J Med 360(10): 973–84. doi: 10.1056/nejmoa0808991
[31]
Pavord ID, Korn S, Howarth P, Bleecker ER, Buhl R, et al. (2012) Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial. The Lancet 380(9842): 651–9. doi: 10.1016/s0140-6736(12)60988-x
[32]
Green RH, Brightling CE, McKenna S, Hargadon B, Parker D, et al. (2002) Asthma exacerbations and sputum eosinophil counts: a randomised controlled trial. The Lancet 360(9347): 1715–21. doi: 10.1016/s0140-6736(02)11679-5
[33]
Gonem S, Desai D, Siddiqui S, Brightling CE (2011) Evidence for phenotype-driven treatment in asthmatic patients. CurrOpin Allergy Clin Immunol 11(4): 381–5. doi: 10.1097/aci.0b013e328348a8f9
[34]
Gamble J, Stevenson M, McClean E, Heaney LG (2009) The prevalence of non-adherence in difficult asthma. Am J RespirCrit Care Med 180(9): 817–22. doi: 10.1164/rccm.200902-0166oc
