A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data

English version

Autoři: Bronwyn K. Brew ^aff001; Flaminia Chiesa ^aff001; Cecilia Lundholm ^aff001; Anne Örtqvist ^aff001; Catarina Almqvist ^aff001
Působiště autorů: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden ^aff001; National Perinatal Epidemiology and Statistics Unit, Centre for Big Data Research in Health and the School of Women and Children’s Health, University of New South Wales, Sydney, Australia ^aff002; IQVIA Nordics, Stockholm, Sweden ^aff003; Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, Stockholm, Sweden ^aff004; Department of Obstetrics and Gynecology, Visby Lasarett, Gotland, Sweden ^aff005; Pediatric Allergy and Pulmonology Unit, Karolinska University Hospital, Stockholm, Sweden ^aff006
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227091

Souhrn

‘Asthma’ is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim of this study was to use data-driven machine learning to identify asthma and wheeze phenotypes in children based on symptom and symptom history data, and, to further characterize these phenotypes. The study population included an asthma-rich population of twins in Sweden aged 9–15 years (n = 752). Latent class analysis using current and historical clinical symptom data generated asthma and wheeze phenotypes. Characterization was then performed with regression analyses using diagnostic data: lung function and immunological biomarkers, parent-reported medication use and risk-factors. The latent class analysis identified four asthma/wheeze phenotypes: early transient wheeze (15%); current wheeze/asthma (5%); mild asthma (9%), moderate asthma (10%) and a healthy phenotype (61%). All wheeze and asthma phenotypes were associated with reduced lung function and risk of hayfever compared to healthy. Children with mild and moderate asthma phenotypes were also more likely to have eczema, allergic sensitization and a family history of asthma. Furthermore, those with moderate asthma phenotype had a higher eosinophil concentration (β 0.21, 95%CI 0.12, 0.30) compared to healthy and used short-term relievers at a higher rate than children with mild asthma phenotype (RR 2.4, 95%CI 1.2–4.9). In conclusion, using a data driven approach we identified four wheeze/asthma phenotypes which were validated with further characterization as unique from one another and which can be adapted for use by the clinician or researcher.

Klíčová slova:

Pediatrics – Children – Allergies – Child health – Pulmonary function – Medical risk factors – Asthma – Twins

Zdroje

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A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data

Souhrn

Klíčová slova:

Zdroje

PLOS One

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