Late Breaking Abstract - Peak expiratory flow (PEF) time series can differentiate asthma severity in children

Background: Single-time-point-measurements of peak expiratory flow (PEF) and forced expiratory volume in 1s (FEV1) are insufficient to discriminate asthma severity. Variability of PEF in time series however contains more information on asthma phenotype and severity (Nature. 2005; 438:667-70). One such novel mathematical parameter is derived from phase space analysis. 3D volume represents the topological space describing temporal variability of lung function. Hypothesis: Phase space analysis to characterise lung function time series such as 3D volume can better discriminate between mild and problematic severe asthmatics (PSA) than standard measures (FEV1, FeNO and BDR). Methods: 33 patients (64% male; median age 13.1 years) were recruited from a tertiary asthma centre. 25 had problematic severe asthma (PSA, patients with persistent symptoms and/or severe exacerbations despite high dose treatment (ERJ 2014; 43:343-73). 9 patients were mild asthmatics (controlled on an ICS dose of ≤400ug BDP). Daily PEF was performed using a validated electronic PEF meter. PEF time series of a median of 61 time points per patient were analysed. Results: The 3D volume phase space model was the best method to discriminate mild and PSA (p=0.0265). There was no significant difference between the two asthma severity groups in mean FEV1 (p=0.338), FeNO (p=0.090) or BDR (p=0.095). Conclusions: Cheap analysis of PEF data can better discriminate different asthma severities than FEV1 or more expensive lab based testing such as FeNO or BDR and has the potential to be used as a biomarker to identify clinically meaningful subgroups.