| Title: | Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children |
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| Authors: | ID Banić, Ivana (Author)
ID Lovrić, Mario (Author)
ID Cuder, Gerald (Author)
ID Kern, Roman (Author)
ID Rijavec, Matija, Klinika Golnik, Biotehniška fakulteta UL (Author)
ID Korošec, Peter, Klinika Golnik (Author)
ID Kljajić-Turkalj, Mirjana (Author) |
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| Files: |  PDF - Presentation file, download (1,32 MB)
MD5: 6A3DE0C5C7A9EE5E9241A72D15B1875F
 URL - Source URL, visit https://asthmarp.biomedcentral.com/track/pdf/10.1186/s40733-021-00077-x.pdf
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  UKPBAG - University Clinic of Respiratory and Allergic Diseases Golnik
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| Abstract: | 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. |
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| Keywords: | asthma, allergy and immunology, pediatrics, machine learning, treatment outcome, phenotypes, childhood asthma, clustering |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Place of publishing: | Velika Britanija |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2021 |
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| Number of pages: | str. 1-11 |
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| Numbering: | Vol. 7 |
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| PID: | 20.500.12556/DiRROS-14264  |
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| UDC: | 616.2 |
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| ISSN on article: | 2054-7064 |
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| DOI: | 10.1186/s40733-021-00077-x |
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| COBISS.SI-ID: | 73007875 |
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| Copyright: | © The Author(s) 2021 |
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| Note: | Nasl. z nasl. zaslona;
Soavtorja iz Slovenije: Matija Rijavec, Peter Korošec;
Opis vira z dne 13. 8. 2021;
Št. članka: 11;
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| Publication date in DiRROS: | 16.08.2021 |
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| Views: | 1284 |
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| Downloads: | 868 |
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