Dosimetry Sensitivity in a Lower Dimensional Model of Patient-Specific Asthma Subjects.

Abstract

Experimental uncertainty will impact in silico model calculations of aerosol delivery and deposition. Patient-specific dosimetry models are often parameterized based on medical imaging data, which contain inherent experimental variability. Here, we created and parameterized 1D models of three subject-specific asthmatic subjects and randomly assigned perturbations of up to 15 % on airway diameter, segmental volume, and defected volume. Sensitivity of imaging data experimental variability on dosimetry metrics were quantified. Lobar particle delivery primarily depended on the distal segmental volumes; 15 % range of noise resulted in delivery to the upper right lobe to vary at most from 15.2 and 18.2 % for one of the severe subjects. Particle deposition was most sensitive to airway diameter; 95 % confidence intervals spanned from 8 to 10.6 % in the mild/moderate subject for 15 % variation on input metrics for 5 [Formula: see text] diameter particles. While these results provide possible ranges of dosimetry calculations for a specific subject, the perturbations were not sufficient to model the large observed inter-subject variability (8.9, 19, and 14.5 % deposition, subjects 1--3, respectively, 5 [Formula: see text] diameter particles). This study highlights that in silico model predictions are robust in the presence of experimental uncertainty and that it continues to be necessary to perform subject-specific simulations, especially within the presence of heterogeneous airway disease. Sensitivity analysis provides confidence in calculating deposition in the airways of asthmatic subjects within the presence of experimental uncertainty.