wheeze; childhood; prediction tool; cough; prognostic modelling; asthma; phenotype
Ramette Alban, Spycher Ben D, Wang Jingying, Goutaki Myrofora, Beardsmore Caroline S, Kuehni Claudia E (2018), Longitudinal Associations between Respiratory Infections and Asthma in Young Children, in American Journal of Epidemiology
, 187(8), kwy053.
Spycher Ben D., Cochrane C, Granell R, Sterne JAC, Silverman M, Pedersen E, Gaillard EA, Henderson J, Kuehni CE (2017), Temporal stability of multitrigger and episodic viral wheeze in early childhood, in European Respiratory Journal
, 50(5), 1700014.
Jurca M, Pescatore AM, Goutaki M, Spycher BD, Beardsmore CS, Kuehni CE (2017), Age-related changes in childhood wheezing characteristics: A whole population study., in Pediatric Pulmonology
, 52(10), 1250-1259.
Wang J, Ramette A, Jurca M, Goutaki M, Beardsmore CS, Kuehni CE (2017), Association between breastfeeding and eczema during childhood and adolescence: A cohort study., in Plos ONE
, 12(9), 0185066.
Wang J, Ramette A, Jurca M, Goutaki M, Beardsmore CS, Kuehni CE (2017), Breastfeeding and respiratory tract infections during the first 2 years of life., in ERJ Open Res
, 3(2), 00143-2016.
Jurca M, Ramette A, Dogaru CM, Goutaki M, Spycher BD, Latzin P, Gaillard EA, Kuehni CE (2017), Prevalence of cough throughout childhood: A cohort study., in Plos ONE
, 12(5), 0177485.
Spycher BD, Kuehni CE (2016), Asthma phenotypes in childhood: conceptual thoughts on stability and transition., in Eur Respir J.
, 47(2), 362-365.
This proposal builds on previous work by the research team on phenotypes and prognostic modelling in childhood asthma. As a novel element, it shifts the previous focus on the general population to the real-world patient population in need of such tools. Background: Wheezing disorders and chronic cough are the most common chronic health problem in childhood. Defining clinical phenotypes and identifying children with high risk of persisting disease remain two of the major challenges in paediatric respiratory medicine. Efforts to identify phenotypes by applying clustering techniques to data from cohort studies are beginning to show a clearer picture of phenotypic variation, but many uncertainties remain. In particular, it is unclear to what extent changes in clinical presentation over time represent the natural course of certain phenotypes or rather phenotype switching. Several asthma prediction tools for childhood asthma have been proposed. Most have focused on a narrow age-range, have not been developed for the clinical population and show moderate predictive performance. We recently developed a simple and non-invasive 10-item tool to Predict Asthma Risk in pre-school Children with wheeze or chronic cough (PARC) for application in 1-3 year old children in primary care. Objectives: The aims of the planned project are to: 1.Progress in phenotype definition of childhood wheeze and cough with a special focus on defining age specific phenotypes (1-3, 4-6, 7-14 years) and the transition between them as children grow older, 2.Develop prediction tools for childhood asthma in clinical care for the age range 1-14 years and begin evaluating their clinical utility 3.Validate findings in external cohortsMethods: We will analyse existing data from population-based cohorts (the Leicester Respiratory Cohorts (LRC; in 1990, N=1,650 and in 1998, N=8,700); the Bern-Basel Infant Lung Development (BILD) cohort (N=837) and the Avon Longitudinal Study on Parents and Children [ALSPAC], N=7,000). In addition, we will set up a new clinical cohort of children seen for wheeze or chronic cough in tertiary care settings in Switzerland (Swiss Paediatric Asthma Cohort, SPAC). Phenotypes and transition patterns between phenotypes will be identified using latent class analysis (SPAC) and latent transition analyses (LRC) and symptoms and physiological measurements at different ages. Using penalized logistic regression we will extend the PARC tool to older age groups, and include repeated symptom assessments and physiological measurements should these be selected as predictors (LRC). For preliminary assessment its clinical utility, risk scores developed prediction tool will be compared to physicians assessments.Output and significance: Ability to predict children at risk for chronic asthma would be of great benefit: for physicians, to adapt management to the most likely clinical course; for parents, to alleviate unfounded concerns and motivate lifestyle changes when indicated; for researchers to recruit high-risk children for intervention studies and gain new insights about mechanisms for disease progression. Using appropriate populations and state-of-the-art methodology, this project will significantly add to the understanding of phenotypic variability in asthma and chronic cough, and to the development and implementation of clinical prediction tools.