Empty nose syndrome: new insights from a CFD approach

Area of Science:

• Otolaryngology
• Biomedical Engineering
• Medical Imaging

Background:

• Empty Nose Syndrome (ENS) is a challenging condition often resulting from turbinate reduction surgery.
• Objective diagnostic tools for ENS are currently lacking, hindering accurate diagnosis and treatment.
• Accurate diagnosis is crucial for effective management and improving patient quality of life.

Purpose of the Study:

• To apply computational fluid dynamics (CFD) for analyzing nasal airflow resistance and symmetry in suspected ENS patients.
• To classify ENS patients into distinct groups using CFD data.
• To explore the potential of CFD in refining ENS diagnosis and guiding individualized treatment.

Main Methods:

• 48 patients with documented prior turbinate surgery and ENS symptoms were included.
• Computational fluid dynamics (CFD) was used to assess nasal airflow resistance and symmetry.
• Patients were categorized into three groups based on CFD parameters: low resistance/normal symmetry, evident asymmetry, and normal CFD.

Main Results:

• 24 patients (50%) presented with low resistance and normal symmetry, characteristic of typical ENS.
• 8 patients (16.7%) showed evident asymmetry, suggesting unilateral ENS or surgical failure.
• 16 patients (33.3%) had normal CFD parameters, indicating a 'normal breathing' group.

Conclusions:

• CFD analysis offers a valuable method for objectively classifying ENS patients based on airflow characteristics.
• This objective classification can significantly aid in refining ENS diagnosis.
• The findings suggest CFD can guide personalized treatment strategies, potentially improving patient outcomes.