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Data processing for steady-state models of multiple flow exhaled nitric oxide

INTRODUCTION: Exhaled nitric oxide (FeNO) is a marker of airway inflammation, typically measured clinically at 50 ml/s expiratory flow (FeNO 50 ). FeNO measured at multiple expiratory flows [“extended NO”] can be partitioned into “NO parameters” quantifying airway wall (J9 aw NO, D aw NO) and alveolar (C A NO) sources using mathematical models. Society recommendations for FeNO 50 recommend identifying a 3 second steady-state plateau concentration from phase III of the exhaled NO/time profile. Such steady-state assumptions may be less valid at lower or higher flows, leading to problems extrapolating data processing for FeNO 50 to extended NO. AIM: Develop, apply, and evaluate an algorithm for processing extended NO data. METHODS: In the Southern California Children9s Health Study, 1704 participants, aged 13-17, performed >15000 extended NO maneuvers (EcoMedics CLD88-SP analyzers). We applied 2 data processing algorithms: (1) a naive extrapolation of society guidelines for FeNO 50 and (2) a novel algorithm accounting for sloped phase III at high and low flows. Using the 2 versions of processed data, we estimated NO parameters by natural-log transform-both-sides nonlinear regression. RESULTS: Phase III was ascending for only 2.2% of low flow maneuvers but descending ( 50% of high flow maneuvers (no steady state plateau). In the 1323 children with complete data and model R 2 >0.95, the new algorithm had little impact on J9 aw NO (paired t-test, p=0.77), decreased mean D aw NO (p A NO (2.1 vs 1.6 ppb, p A NO estimates. CONCLUSIONS: Data processing markedly impacts NO parameter estimation. Our work informs better standardization of extended NO.