Evaluation of posterior airway space after setback surgery by simulation

Evaluation of computational fluid dynamics models for predicting pediatric upper airway airflow characteristics

Computational fluid dynamics (CFD) has the potential for use as a clinical tool to predict the aerodynamics and respiratory function in the upper airway (UA) of children; however, careful selection of validated computational models is necessary. This study constructed a 3D model of the pediatric UA based on cone beam computed tomography (CBCT) imaging. The pediatric UA was 3D printed for pressure and velocity experiments, which were used as reference standards to validate the CFD simulation models. Static wall pressure and velocity distribution inside of the UA under inhale airflow rates from 0 to 266.67 mL/s were studied by CFD simulations based on the large eddy simulation (LES) model and four Reynolds-averaged Navier-Stokes (RANS) models. Our results showed that the LES performed best for pressure prediction; however, it was much more time-consuming than the four RANS models. Among the RANS models, the Low Reynolds number (LRN) SST k-ω model had the best overall performance at a series of airflow rates. Central flow velocity determined by particle image velocimetry was 3.617 m/s, while velocities predicted by the LES, LRN SST k-ω, and k-ω models were 3.681, 3.532, and 3.439 m/s, respectively. All models predicted jet flow in the oropharynx. These results suggest that the above CFD models have acceptable accuracy for predicting pediatric UA aerodynamics and that the LRN SST k-ω model has the most potential for clinical application in pediatric respiratory studies.

Retrospective study on the pharyngeal airspace in the lateral cephalogram - A mathematical model to predict changes due to bimaxillary orthognathic surgery

The aim of this study was the development of a statistical model for reliable prediction of Posterior Airway Space (PAS) changes in lateral cephalograms (LCR) of patients after bimaxillary orthognathic surgery. The LCRs of patients who underwent bimaxillary orthognathic surgery were retrospectively analyzed. The anteroposterior dimension of the PAS was measured at three levels in the pre-operative and postoperative LCR: On the nasopharyngeal (SPAS), oropharyngeal (MAS), and hypopharyngeal level (IAS). The data of 139 patients were collected. The following changes of the PAS were measured: in class II patients SPAS: 0.291 mm (SD = 2.570 mm); MAS: 2.444 mm (SD = 2.986 mm); IAS: 0.750 mm (SD = 3.017 mm); in class III patients SPAS: 1.377 mm (SD 3.212 mm); MAS: 0.962 (SD: = 3.135 mm); IAS: 0.370 mm (SD = 3.468 mm). Linear regression analysis showed for class II patients, a significant influence of mandibular movement on MAS (p = 0.049) and a significant effect of maxillary and mandibular movements on SPAS (p = 0.001) and MAS (p = 0.022) in class III patients. The other jaw displacements had no significant impact on the investigated PAS levels. While the presented method does not permit exact prediction of the dimension of the PAS, it is still an easily accessible method of orientation for the surgeon. The surgeon can initiate three-dimensional examinations to provide exact three-dimensional prediction based on this calculation.

Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study

COVID-19 looks to be the worst pandemic disease in the last decades due to its number of infected people, deaths, and the staggering demand for healthcare services, especially hospitals. The first and most important step is to identify the patient flow through a certain process. For the second step, there is a crucial need for predicting the future patient arrivals for planning especially at the administrative level of a hospital. This study aims to first simulate the patient flow process and then predict the future entry of patients in a hospital as the case study. Also, according to the system status, this study suggests some policies based on different probable scenarios and assesses the outcome of each decision to improve the policies. The simulation model is conducted by Arena.15 software. The seasonal auto-regressive integrated moving average (SARIMA) model is used for patient’s arrival prediction within 30 days. Different scenarios are evaluated through a data envelopment analysis (DEA) method. The simulation model runs for predicted patient’s arrival for the least efficient scenario and the outputs compare the base run scenario. Results show that the system collapses after 14 days according to the predictions and simulation and the bottleneck of the ICU and CCU departments becomes problematic. Hospitals can use simulation and also prediction tools to avoid the crisis to plan for the future in the pandemic.

Computational Fluid Dynamics Analysis of Nasal Airway Changes after Treatment with C-Expander

The use of the C-expander is an effective treatment modality for maxillary skeletal deficiencies which can cause ailments and significantly reduce life expectancy in late adolescents and young adults. However, the morphological and dynamic effects on the nasal airway have not been reported. The main goal of this study was to evaluate the nasal airway changes after the implementation of a C-expander. A sample of nine patients (8 females, 1 male, age range from 15 to 29 years) was included. The morphology parameters and nasal airway ventilation parameters of pretreatment and posttreatment were measured. All study data were normally distributed. A paired t-test was used to evaluate the changes before and after treatment. After expansion, the mean and standard deviation values of intercanine maxillary width (CMW) and intermolar maxillary width (MMW) increased from 35.75 ± 2.48 mm and 54.20 ± 3.17 mm to 37.87 ± 2.26 mm (P < 0.05) and 56.65 ± 3.10 mm (P < 0.05), respectively. The nasal cavity volume increased from 20320.00 ± 3468.25 mm³ to 23134.70 ± 3918.84 mm³ (P < 0.05). The nasal pressure drop decreased from 36.34 ± 3.99 Pa to 30.70 ± 3.17 Pa (P < 0.05), while the value of the maximum velocity decreased from 6.50 ± 0.31 m/s to 5.85 ± 0.37 m/s (P < 0.05). Nasal resistance dropped remarkably from 0.16 ± 0.14 Pa/ml/s to 0.08 ± 0.06 Pa/ml/s (P < 0.05). The use of C-expander can effectively broaden the area and volume of the nasal airway, having a positive effect in the reduction of nasal resistance and improvement of nasal airway ventilation. For patients suffering from maxillary width deficiency and respiratory disorders, a C-expander may be an alternative method to treat the disease.