Frank-Ito Dennis O, Kimbell Julia S, Laud Purushottam, Garcia Guilherme J M, Rhee John S
Division of Otolaryngology-Head and Neck Surgery, Duke University Medical Center, Durham, North Carolina, USA
Department of Otolaryngology/Head and Neck Surgery, University of North Carolina, Chapel Hill, North Carolina, USA.
Otolaryngol Head Neck Surg. 2014 Nov;151(5):751-9. doi: 10.1177/0194599814547497. Epub 2014 Aug 28.
High failure rates for surgical treatment of nasal airway obstruction (NAO) indicate that better diagnostic tools are needed to improve surgical planning. This study evaluates whether computer models based on a surgeon's edits of presurgery scans can accurately predict results from computer models based on postoperative scans of the same patient using computational fluid dynamics.
Prospective study.
Academic medical center.
Three-dimensional nasal models were reconstructed from computed tomographic scans of 10 patients with NAO presurgery and 5 to 8 months postsurgery. To create transcribed-surgery models, the surgeon digitally modified the preoperative reconstruction in each patient to represent physical changes expected from surgery and healing. Steady-state, laminar, inspiratory airflow was simulated in each model under physiologic, pressure-driven conditions.
Transcribed-surgery and postsurgery model variables were statistically different from presurgery variables at α = 0.05. Unilateral nasal resistance and airflow were not statistically different between transcribed-surgery and postsurgery models, but bilateral resistance was significantly different. Cross-sectional average pressures in transcribed surgery trended with postsurgery. Transcribed-surgery prediction errors of postsurgery bilateral resistance were within 10% to 20% and 20% to 30% in 5 and 4 subjects, respectively. Prediction errors for unilateral resistance were <10%, 10% to 20%, and 20% to 30% in 1, 2, and 4 subjects, respectively.
Computational models with modifications mimicking actual surgery and healing have the potential to predict postoperative outcomes. However, software to effectively translate virtual surgery steps into computational models is lacking. The ability to account for healing factors and the current limited virtual surgery tools are challenges that need to be overcome for greater accuracy.
鼻气道阻塞（NAO）手术治疗的高失败率表明，需要更好的诊断工具来改进手术规划。本研究评估基于外科医生对术前扫描进行编辑的计算机模型，是否能够使用计算流体动力学，根据同一患者的术后扫描准确预测计算机模型的结果。
前瞻性研究。
学术医疗中心。
从10例NAO患者术前及术后5至8个月的计算机断层扫描重建三维鼻腔模型。为创建模拟手术模型，外科医生对每位患者的术前重建模型进行数字修改，以呈现手术和愈合预期的身体变化。在生理压力驱动条件下，对每个模型进行稳态、层流、吸气气流模拟。
在α = 0.05时，模拟手术和术后模型变量与术前变量存在统计学差异。模拟手术和术后模型之间的单侧鼻阻力和气流无统计学差异，但双侧阻力存在显著差异。模拟手术中的横断面平均压力与术后趋势一致。模拟手术对术后双侧阻力的预测误差在5例和4例受试者中分别在10%至20%和20%至30%之间。单侧阻力的预测误差在1例、2例和4例受试者中分别<10%、10%至20%和20%至30%。
模拟实际手术和愈合的计算模型有预测术后结果的潜力。然而，缺乏能有效将虚拟手术步骤转化为计算模型的软件。考虑愈合因素的能力和当前有限的虚拟手术工具是提高准确性需要克服的挑战。
