Indicators for the correct usage of intranasal medications: A computational fluid dynamics study.

LARYNGOSCOPE(2009)

引用 25|浏览15
暂无评分
摘要
Objectives/Hypothesis: Intranasal medications are commonly used in treating nasal diseases. However, technical details of their correct administration are unclear. Methods: A three-dimensional model of nasal cavity was constructed from the magnetic resonance imaging scans of a healthy human subject. Nasal cavities corresponding to moderate and severe nasal obstruction were derived from the healthy nose by enlarging the inferior turbinate geometrically, which can be documented by approximately one third reduction of the minimum cross-sectional area in the moderate and two thirds in the severe obstruction. The discrete phase model based on computational fluid dynamics was used to study the gas particle flow. The percentage of discrete particles that pass through the minimum cross-sectional area in the nasal valve (NV) region is computed as the percentage of NV penetration rate. Results: The percentage of NV penetration is 10 to 20 times higher when nasal spray is accompanied by an inspiratory airflow than no flow, which can be as low as 4.69% to 8.81% in the healthy model, and 0% in moderate and severe blockage models, In the presence of inspiratory airflow, there is no significant difference in the percentage of NV penetration (80.97%-82.13%) among different head tilt angles (0 degrees-90 degrees). Conclusions: When using an intranasal medication, it is advisable to have an inspiratory flow to improve drug penetration. Various suggested head positions do not improve drug penetration significantly, even in the presence of uniform quiet breathing. Further studies that consider turbulence and unsteady airflow are needed to investigate the deposition distribution of discrete particles inside various nasal cavities.
更多
查看译文
关键词
Three-dimensional computational nose model,inferior turbinate hypertrophy,computational fluid dynamics,nasal airflow,percentage of nasal valve penetration rate
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要