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Naslov:Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children
Avtorji:ID Banić, Ivana (Avtor)
ID Lovrić, Mario (Avtor)
ID Cuder, Gerald (Avtor)
ID Kern, Roman (Avtor)
ID Rijavec, Matija, Klinika Golnik, Biotehniška fakulteta UL (Avtor)
ID Korošec, Peter, Klinika Golnik (Avtor)
ID Kljajić-Turkalj, Mirjana (Avtor)
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (1,32 MB)
MD5: 6A3DE0C5C7A9EE5E9241A72D15B1875F
 
URL URL - Izvorni URL, za dostop obiščite https://asthmarp.biomedcentral.com/track/pdf/10.1186/s40733-021-00077-x.pdf
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo UKPBAG - Univerzitetna klinika za pljučne bolezni in alergijo Golnik
Povzetek: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.
Ključne besede:asthma, allergy and immunology, pediatrics, machine learning, treatment outcome, phenotypes, childhood asthma, clustering
Leto izida:2021
Status objave pri reviji:Objavljeno
Verzija članka:Založnikova različica članka
Založnik:Springer Nature
Št. strani:str. 1-11
Številčenje:Vol. 7
Izvor:Velika Britanija
UDK:616.2
ISSN pri članku:2054-7064
COBISS.SI-ID:73007875 Novo okno
DOI:10.1186/s40733-021-00077-x Novo okno
Avtorske pravice:© The Author(s) 2021
Opomba:Nasl. z nasl. zaslona; Soavtorja iz Slovenije: Matija Rijavec, Peter Korošec; Opis vira z dne 13. 8. 2021; Št. članka: 11;
Datum objave v DiRROS:16.08.2021
Število ogledov:454
Število prenosov:271
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Asthma research and practice
Skrajšan naslov:Asthma res. pract.
Založnik:BioMed Central
ISSN:2054-7064
COBISS.SI-ID:525466393 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:03.08.2021

Sekundarni jezik

Jezik:Ni določen
Ključne besede:astma, alergija in imunologija, pediatrija. strojno učenje, izid zdravljenja, fenotipi, astma pri otrocih, razvrščanje po skupinah


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