Esteban-Ortega Francisco, Rosique-López Lina, Ochoa-Ríos Jaime A, Rodríguez-Romero Rafael, Burgos-Olmos Manuel A
Department of Surgery, School of Medicine, University of Seville, Seville, Spain.
Department of Otolaryngology, Hospital Universitario Virgen del Rocío, Seville, Spain.
Eur Arch Otorhinolaryngol. 2025 Mar;282(3):1319-1326. doi: 10.1007/s00405-024-09122-w. Epub 2024 Dec 11.
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.
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.
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.
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 4 [1].
空鼻综合征（ENS）是一种使人衰弱的疾病，通常在激进的鼻甲切除术之后出现。然而，目前缺乏有助于诊断该疾病的客观测试方法。准确诊断ENS患者对于有效诊断和治疗至关重要。本文的目的是利用计算流体动力学（CFD）分析疑似ENS患者的鼻气流阻力和对称性，根据CFD数据将他们分为不同的组，并证明CFD分析在完善ENS诊断和指导个体化治疗策略方面的潜力。
本研究纳入了48例被诊断为ENS的患者。然而，我们仅考虑那些有鼻甲手术记录（最终可能还包括鼻中隔手术）、CT扫描有既往手术迹象且有符合ENS Q6中症状的ENS病史的患者。我们采用计算流体动力学（CFD）来分析鼻气流阻力和对称性。根据CFD数据将患者分为三组：低阻力且对称正常、明显不对称以及CFD参数正常。
一半的患者（48例中的24例）被归入低阻力且对称正常组，表明为“典型”ENS。较小的一组（8例）表现出明显不对称，提示单侧ENS或既往手术失败。最后，16例CFD参数在正常气流和阻力范围内的患者被归入正常呼吸组。
我们的研究结果凸显了CFD分析在根据气流特征对ENS患者进行分类方面的价值，因为CFD分析似乎有助于完善ENS的诊断。这种分类系统可能有助于制定个体化治疗策略并改善患者预后。有必要进行进一步研究以验证这些结果并探索不同ENS亚组的临床意义。
4级[1]。
