Comparison of feasibility and estimates of central and peripheral nitric oxide parameters by different mathematical...

Assessment of the central and peripheral nitric oxide (NO) dynamics of the lung provides information on the severity and anatomical site of pulmonary inflammation. Several mathematical methods for calculating alveolar and bronchial NO parameters have been introduced. Our aim was to compare these methods.

The study included 69 healthy adults, 66 healthy children, 73 asbestos-exposed subjects and 72 subjects with chronic obstructive pulmonary disease (COPD). Exhaled NO was measured at multiple flow rates and we used five mathematical methods (Tsoukias and George, Pietropaoli, Condorelli, Högman and Meriläinen, and Silkoff) to estimate alveolar and bronchial NO parameters.

The Högman and Meriläinen method was less frequently feasible than the other methods but it had the highest degree of agreement with the measured data. The methods were most often feasible in healthy or asbestos-exposed adults but distinctly more infrequently in children and adults with COPD, suggesting difficulties in NO measurements in these groups. The linear methods (Tsoukias and George, Pietropaoli) yielded higher alveolar NO concentration and lower bronchial NO flux than the two non-linear methods (Högman and Meriläinen, Silkoff) and a linear method with correction for axial back-diffusion of NO (Condorelli).

In differentiating central and peripheral NO sources we recommend using linear methods, as low flow rates are not needed and the feasibility of the methods is good. If bronchial wall NO concentration (C _awNO) and diffusing capacity (D _awNO) are of interest, non-linear methods are needed, and we recommend using the Högman and Meriläinen method as only three flow rates are needed. However, the agreement between the model and measured data needs to be checked in real time to ensure feasibility. If the subject has difficulties with the extremely low or high flow rates, we then recommend using the Silkoff method to improve feasibility, but more flow rates and measurements are then needed and the agreement between the model and the measured data may be poorer.