Computationally Guided Simulation-Based Surgical Correction of Nasal Airway Obstruction in Patients With Cleft Lip Nasal Deformity.

ObjectiveTo evaluate different virtual-surgical interventions for the correction of unilateral cleft lip nasal deformity (uCLND)-induced nasal airway obstruction (NAO) using computational fluid dynamics (CFD) modeling.DesignPreliminary pilot study involving virtual-surgical interventions based on pre-surgery computed tomography (CT) imaging with CFD analysis.SettingAcademic medical center.Patients, ParticipantsFive subjects aged ≥15 years with uCLND who underwent preoperative CT imaging.InterventionsVirtual-surgical procedures involving varying combinations of inferior turbinate reduction (ITR), anterior (cartilaginous) septoplasty (AS), and posterior (osseous) septoplasty (PS).Main Outcome Measure(s)CFD-identified greatest obstructive site, changes in nasal resistance and airflow distribution calculated from airflow simulations in preoperative and virtual-surgery nasal models, correlation of resistance changes with the minimal clinically important difference (MCID) for patient-reported Nasal Obstruction Symptom Evaluation (NOSE) scores, and evaluation of efficacious virtual procedures balancing surgical complexity.ResultsPreoperatively, all subjects had greater resistance on the cleft side. Isolated septoplasty reduced bilateral resistance by <10%. Cleft-side ITR ± AS and/or PS reduced bilateral resistance by 32% to 54%. Bilateral ITR ± AS/PS achieved the largest improvements, with median reductions of 50% to 66% in bilateral resistance. Non-cleft ITR reduced resistance on the non-cleft side but often worsened airflow asymmetry. The number of procedures resulting in resistance improvements past the MCID threshold varied among subjects.ConclusionsCFD-based virtual-surgical procedures indicated that ITR alone or with septoplasty provided the most effective improvement in nasal resistance and airflow for uCLND. Patient-specific anatomy strongly influenced efficacy, supporting the rationale for the use of computational modeling to assist in personalized surgical planning.