Abstract
OBJECTIVES: Empty Nose Syndrome (ENS) is a debilitating condition which usually arises after aggressive turbinate reduction. However, objective tests to help in the diagnosis of this condition are lacking. Accurate diagnosis of ENS patients is critical for effective diagnosis and treatment. The article's objectives are to utilize computational fluid dynamics (CFD) to analyze nasal airflow resistance and symmetry in suspected ENS patients, classify them into distinct groups based on CFD data, and demonstrate the potential of CFD analysis in refining ENS diagnosis and guiding individualized treatment strategies. METHODS: This study involved 48 patients diagnosed of ENS. However, we only considered those patients with documented prior turbinate surgery (eventually plus septal surgery), CT scan with signs of prior surgery, and a history of ENS with symptoms included in the ENS Q6. We employed computational fluid dynamics (CFD) to analyze nasal airflow resistance and symmetry. Patients were classified into three groups based on their CFD data: low resistance and normal symmetry, evident asymmetry, and normal CFD parameters. RESULTS: Half of patients (24 out of 48) were found in the low resistance and normal symmetry group, indicating 'typical' ENS. A smaller group (8) exhibited evident asymmetry, suggesting unilateral ENS or failure of previous surgery. Finally, 16 patients whose CFD parameters are inside the normal range of flow and resistance were classified in the normal breathing group. CONCLUSIONS: Our findings highlight the value of CFD analysis in classifying ENS patients based on airflow characteristics, as CFD analysis seems helpful in refining the diagnosis of ENS. This classification system can potentially aid in tailoring individual treatment strategies and improving patient outcomes. Further research is necessary to validate these results and explore the clinical implications of different ENS subgroups. LEVEL OF EVIDENCE: Level 4 [1].