Dataset for: Bayesian estimation of physiological parameters governing a dynamic two-compartment model of exhaled nitric oxide

The fractional concentration of nitric oxide in exhaled breath (FᴇNO) is a biomarker of airway inflammation with applications in clinical asthma management and environmental epidemiology. FᴇNO concentration depends on the expiratory flow rate. Standard FᴇNO is assessed at 50 ml/s, but “extended NO analysis” uses FᴇNO measured at multiple different flow rates to estimate parameters quantifying proximal and distal sources of NO in the lower respiratory tract. Most approaches to modeling multiple flow FᴇNO assume the concentration of NO throughout the airway has achieved a “steady-state”. In practice, this assumption demands that subjects maintain sustained flow rate exhalations, during which both FᴇNO and expiratory flow rate must remain constant, and the FᴇNO maneuver is summarized by the average FᴇNO concentration and average flow during a small interval. In this work, we drop the steady-state assumption in the classic two-compartment model. Instead, we have developed a new parameter estimation approach based on measuring and adjusting for a continuously varying flow rate over the entire FᴇNO maneuver. We have developed a Bayesian inference framework for the parameters of the partial differential equation underlying this model. Based on multiple flow FᴇNO data from the Southern California Children’s Health Study, we use observed and simulated NO concentrations to demonstrate that our approach has reasonable computation time and is consistent with existing steady state approaches, while our inferences consistently offer greater precision than current methods.