Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children

Asthma Research and Practice

Gerald Cuder¹  Roman Kern²  Mario Lovrić²  Ivana Banić³  Mirjana Turkalj⁴  Matija Rijavec⁵  Peter Korošec⁵ 

[1] Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, AT-8010, Graz, Austria;Know-Center, Infeldgasse 13, AT-8010, Graz, Austria;Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, AT-8010, Graz, Austria;Srebrnjak Children’s Hospital, Srebrnjak 100, 10000, Zagreb, Croatia;Srebrnjak Children’s Hospital, Srebrnjak 100, 10000, Zagreb, Croatia;Faculty of Medicine, J.J, Strossmayer University of Osijek, Josipa Huttlera 4, 31000, Osijek, Croatia;Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia;University Clinic of Respiratory and Allergic Diseases Golnik, Golnik 36, 4204, Golnik, Slovenia;

关键词: Machine learning;    Childhood asthma;    Allergy;    Asthma phenotypes;    Clustering;    Treatment outcome;   

DOI  :  10.1186/s40733-021-00077-x

来源: Springer

【 摘 要 】

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.

【 授权许可】

CC BY