疾患鼻気道における空気流と粒子堆積の計算流体力学的研究

Understanding the properties of airflow in the nasal cavity is very important in determining the nasal physiology and in diagnosis of various anomalies associated with the nose. The complex anatomy of the nasal cavity has proven to be a significant obstacle in the understanding of nasal obstructive disorders. Due to their non-invasiveness, Computational Fluid Dynamics (CFD) has now been utilized to assess the effects of surgical interventions on nasal morphological changes as well as local breathing airflow characteristics through the upper airway of individual patients. Furthermore, nasal inhalation is a major route of entry into body for airborne pollutions. Therefore, the function of the upper airway to filter out the inhaled toxic particles is considered important. The determination of the total particle filtering efficiency and the precise location of the induced lesion in the upper airway is the first step in understanding the critical factors involved in the pathogenesis of the upper airway injury. The present work involved development of three-dimensional diseased upper airway models from Computed Tomographic (CT) scan images derived from a nasal airway without any nasal diseased and an upper airway which was diagnosed with chronic nasal obstruction and obstructive sleep apnea. Numerical simulation of airflow and transport and deposition of inhaled pollutant through chronic diseased nasal airway, constricted pharyngeal representing Obstructive Sleep Apnea (OSA) and diseased upper airway with OSA for pre- and post-operative cases have been studied. Detailed flow pattern and characteristics for inspiratory airflow for various breathing rates (7.5-40 L/min) were evaluated. Simulation of the particle transport and deposition of micro-sized particles with particle diameter ranging from 1-40 ?m were also investigated. In the first part of this study, the surgical treatment performed in the nasal cavity which include septoplasty, inferior turbinate reduction and partial concha bullosa resection substantially increased nasal volume, which influenced flow partitioning and decreases the pressure drop and flow resistance of the nasal passage. The removal of the obstruction in the nasal airway significantly improve the breathing quality. However, the nasal airway experienced approximately about a 50 % decrease in total particle filtering efficiency after surgery. Therefore, careful consideration should be given to this matter before nasal operation especially for a patient with breathing allergic history. In the second part of this study, the morphology of the constricted pharyngeal representing OSA was found to significantly affect the airflow pattern and the deposition fraction of microparticles. The morphology of the upper airway, the size of the inhaled particle and breathing rate was found significantly affect the total particle deposition efficiency and local deposition fraction in the upper airway. The presented regional deposition fraction may be used in specifying the site of highest possibility for respiratory lesions according to the breathing rate and the size of the inhaled toxic particles. Results obtained from this study can be also used to estimate the location of airway obstruction in upper airway of patient with sleep apnea symptom. In the third part of this study, the surgical conducted procedure has cleared out the obstructions in the nasal airway hence improve the airflow distribution through the upper airway during inhalation process. This study shows that the nasal surgery alone can help improve the breathing quality in the upper airway with OSA. The reduction of the airflow resistance in the nasal cavity affect the pressure distribution in the lower part of the upper airway. Obstruction in the nasal passage and sudden airway expansion in the upper airway increased number of particles trap, recirculated and finally deposited in the airway. Finally, the experimental data obtained from the experimental study utilizing the developed pharyngeal airway further validate the result obtained from the numerical study.九州工業大学博士学位論文 学位記番号：生工博甲第315号 学位授与年月日：平成30年3月23日1: INTRODUCTION|2: LITERATURE REVIEW|3: MODELLING THE HUMAN UPPER AIRWAY|4: NUMERICAL SIMULATION METHODOLOGY|5: NUMERICAL INVESTIGATION ON AIRFLOW CHARACTERISTICS IN NASAL CAVITY HAVING TURBINATE HYPERTROPHY, CONCHA BULLOSA, AND SEPTUM DEVIATION WITH OSA: PRE- AND POST SURGERY|6: COMPUTATIONAL FLUID DYNAMICS STUDY OF AIRFLOW AND MICROPARTICLE DEPOSITION IN A CONSTRICTED PHARYNGEAL SECTION REPRESENTING OBSTRUCTIVE SLEEP APNEA DISEASE|7: NUMERICAL SIMULATION OF AIRFLOW AND AEROSOL DEPOSITION IN REALISTIC HUMAN UPPER AIRWAY WITH OBSTRUCTIVE SLEEP APNEA AND CHRONIC NASAL OBSTRUCTION: PRE- AND POST-SURGERY|8: EXPERIMENTAL INVESTIGATION|9: CONCLUSIONS AND FUTURE RECOMMENDATIONS九州工業大学平成29年

Experimental and Numerical Modeling of Fluid Flow

This Special Issue provides an overview of the applied experimental and numerical flow, models, which are used to investigate fluid flow in complex situations. The investigated problems are related to fundamental processes or new applications. As demonstrated, the field of the application of experimental and numerical flow models is constantly expanding

To verify four 5-year-old mathematical models to predict the outcome of ICU patients

The aim of this study is to verify calibration and discrimination after 5 years in the case mix of patients admitted to the Intensive Care Unit (ICU) during the year 2000. In this way we want to perform a quality control of our ICU in order to justify the increased amount of money spent for intensive care.A prospective study has been made on the 357 patients admitted to the ICU during the year 2000. The Apache II score was calculated within the first 24 hours and, depending on the length of stay in the ICU, on the 5(th), 10(th) and 15(th) day after ICU admission. On the basis of the 4 mathematical models death risk has been calculated for each of the 4 times. The Hosmer-Lemeshow test was performed for calibration and ROC curves for discrimination, always for each of the 4 mathematical models.The 1(st) model, at 24 hours from ICU admission, showed a bad calibration (p=0.000088), while the ROC curve was 0.744+/-0.32. Also the 2(nd) model, at the 5(th) day from admission, showed a bad calibration (p=0.000588), with ROC curve of 0.827+/-0.04. The 3(rd) model (10(th) day), was well calibrated (p=0.112247) and discriminating (ROC=0.888 +/-0.04). Finally the models at 15 days showed again a bad calibration (p=0.001422) but a very good discrimination (area=0.906+/-0.06).Developing mathematical models to predict mortality within ICUs can be useful to assess quality of care, even if these models should not be the only ICU quality controls, but must be accompanied by other indicators, looking at quality of life of the patients after ICU discharge