An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

Measurement of Nasal Obstruction: Rhinomanometry as Basic Method in Functional Rhinology

Measuring nasal obstruction should be mandatory before every nasal surgery to determine the necessary surgical steps and to separate mucosal congestion from skeletal deformity. Rhinomanometry is considered the "golden standard" for measuring nasal airway resistance. An extension of the diagnostic power was achieved by the introduction of the four-phase rhinomanometry (4PR), which provides additional information about the influence of the elasticity of the nasal wall. Also, a high correlation to the subjective sensing of obstruction could be achieved by the introduction of logarithmic parameters. The parameters of 4PR could be classified by evaluating 36,500 measurements. The International Standardization Committee on the Objective Assessment of the Upper Airway (ISCOANA) determined it as the new standard. Adding a visual analog scale (VAS) to every measurement is a documentation of the feeling of obstruction at the moment of measurement. Practical hints for 4PR are given. The determination of obstruction for both nasal sides or the calculation of the total nasal resistance should be followed by a second analysis of the detailed obstruction anatomy. In the future, pressure losses or temperature gradients can be determined by computational fluid dynamics (CFD). A reliable way to simulate the success of single surgical steps with a high didactical meaning is the three-dimensional (3D) print of sliced nose models and to measure the effect of stepwise mechanical corrections.

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

Comparison of rhinomanometric and computational fluid dynamic assessment of nasal resistance with respect to measurement accuracy

PURPOSE

Computational fluid dynamics (CFD)-based calculation of intranasal airflow became an important method in rhinologic research. Current evidence shows weak to moderate correlation as well as a systematic underprediction of nasal resistance by numerical simulations. In this study, we investigate whether these differences can be explained by measurement uncertainties caused by rhinomanometric devices and procedures. Furthermore, preliminary findings regarding the impact of tissue movements are reported.

METHODS

A retrospective sample of 17 patients, who reported impaired nasal breathing and for which rhinomanometric (RMM) measurements using two different devices as well as computed tomography scans were available, was investigated in this study. Three patients also exhibited a marked collapse of the nasal valve. Agreement between both rhinomanometric measurements as well as between rhinomanometry and CFD-based calculations was assessed using linear correlation and Bland-Altman analyses. These analyses were performed for the volume flow rates measured at trans-nasal pressure differences of 75 and 150 Pa during inspiration and expiration.

RESULTS

The correlation between volume flow rates measured using both RMM devices was good (R² > 0.72 for all breathing states), and no relevant differences in measured flow rates was observed (21.6 ml/s and 14.8 ml/s for 75 and 150 Pa, respectively). In contrast, correlation between RMM and CFD was poor (R² < 0.5) and CFD systematically overpredicted RMM-based flow rate measurements (231.8 ml/s and 328.3 ml/s). No differences between patients with and without nasal valve collapse nor between inspiration and expiration were observed.

CONCLUSION

Biases introduced during RMM measurements, by either the chosen device, the operator or other aspects as for example the nasal cycle, are not strong enough to explain the gross differences commonly reported between RMM- and CFD-based measurement of nasal resistance. Additionally, tissue movement during breathing is most likely also no sufficient explanation for these differences.