Patterns of Respiratory Symptoms and Asthma Diagnosis in School‐Age Children: Three Birth Cohorts

ABSTRACT Background Many studies used information on wheeze presence/absence to determine asthma‐related phenotypes. We investigated whether clinically intuitive asthma subtypes can be identified by applying data‐driven semi‐supervised techniques to information on frequency and triggers of different respiratory symptoms. Methods Partitioning Around Medoids clustering was applied to data on multiple symptoms and their triggers in school‐age children from three birth cohorts: MAAS ( n = 947, age 8 years), SEATON ( n = 763, age 10) and ASHFORD ( n = 584, age 8). ‘Guided’ clustering, incorporating asthma diagnosis, was used to select the optimal number of clusters. Results Five‐cluster solution was optimal. Based on their clinical characteristics, including frequency of asthma diagnosis, we interpreted one cluster as ‘Healthy’. Two clusters were characterised by high asthma prevalence (95.89% and 78.13%). We assigned children with asthma in these two clusters as ‘persistent, multiple‐trigger, more severe’ (PMTS) and ‘persistent, triggered by infection, milder’ (PIM). Children with asthma in the remaining two clusters were assigned as ‘mild‐remitting wheeze’ (MRW) and ‘post‐bronchiolitis resolving asthma’ (PBRA). PBRA was associated with RSV bronchiolitis in infancy. In most children with asthma in this cluster wheezing resolved by age 5–6, and predominant symptoms were shortness of breath and chest tightness. Children in PBRA had the highest hospitalisation rates and wheeze exacerbations in infancy. From age 8 years (cluster derivation) to early adulthood (18–20 years), lung function was significantly lower, and FeNO and airway hyperreactivity significantly higher in PMTS compared to all other clusters. Conclusions Patterns of coexisting symptoms identified by semi‐supervised data‐driven methods may reflect pathophysiological mechanisms of distinct subtypes of childhood wheezing disorders.