Improving Outcomes in Pediatric Obstructive Sleep Apnea with Computational Fluid Dynamics

利用计算流体动力学改善小儿阻塞性睡眠呼吸暂停的治疗效果

基本信息

批准号：
10543171
负责人：
Alister Bates
金额：
$ 24.9万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10543171
关键词：
3-Dimensional Acceleration Adenoidal structure Affect Air Air Movements Air Pressure Airway Resistance Anatomy Apnea Artificial Intelligence Award Behavioral Biomechanics Biometry Breathing Cardiovascular system Child Childhood Chronic Clinical Cognitive Complement Computer Models Continuous Positive Airway Pressure Data Developmental Delay Disorders Disease Environment Event Face Failure Gases Geometry Goals Growth Heart Abnormalities Hour Image Impairment In Vitro Individual Inhalation Ionizing radiation Knowledge Liquid substance Lung Machine Learning Magnetic Resonance Imaging Maps Measurement Measures Medicine Mentors Metabolic Methods Modality Modeling Morbidity - disease rate Motion Noble Gases Obstructive Sleep Apnea Operative Surgical Procedures Outcome Patients Pattern Pediatric Hospitals Phase Physiology Polysomnography Postoperative Period Pulmonology Quality of life Radiology Specialty Research Research Personnel Resistance Resolution Respiratory Airflow Shapes Sleep Sleep Apnea Syndromes Sleep disturbances Soft Palate Structure Surgeon Techniques Testing Time Tongue Tonsil Validation Wing Xenon airway obstruction candidate validation career career development computerized tools design experience imaging Segmentation improved improved outcome in vivo indexing individual patient neuromuscular non-compliance novel pediatric patients positive airway pressure precision medicine predictive modeling predictive tools pressure reduce symptoms respiratory respiratory surgery simulation skills soft tissue success surgery outcome tool virtual virtual model virtual surgery

项目摘要

This project aims to create a validated computational tool to predict surgical outcomes for pediatric patients with obstructive sleep apnea (OSA). OSA is a common condition, affecting 2.2 million children in the USA alone. It is characterized as upper airway obstruction during sleep, which causes disrupted sleep and leads to developmental delay, cardiovascular complications and impaired growth. The first line of treatment for children with OSA is to remove their tonsils and adenoids; however, these surgeries do not always cure the patient. Another treatment, continuous positive airway pressure (CPAP) is only tolerated by 50% of children. Therefore, many children undergo surgical interventions aimed at soft tissue structures surrounding the airway, such as tonsils, tongue, and soft palate, and/or the bony structures of the face. However, the success rates of these surgeries, measured as a reduction in the obstructive apnea-hypopnea index (obstructive events per hour of sleep), is surprisingly low. Therefore, there is a clear need for a tool to improve the efficacy of these surgeries and predict which of the various surgical options is going to benefit each individual patient most effectively. Computational fluid dynamics (CFD) simulations of respiratory airflow in the upper airways can provide this predictive tool, allowing the effects of various surgical options to be compared virtually and the option most likely to improve the patient’s condition to be chosen. Previous CFD simulations have been unable to provide information about OSA as they were based on rigid geometries, or did not include neuromuscular motion, a key component in OSA. This project uses real-time magnetic resonance imaging (MRI) to provide the anatomy and motion of the airway to the CFD simulation, meaning that the exact in vivo motion is modeled for the first time. Furthermore, since the modeling is based on MRI, a modality which does not use ionizing radiation, it is suitable for longitudinal assessment of patients before and after surgical procedures. In vivo validation of these models will be achieved for the first time through comparison of CFD-based airflow velocity fields with those generated by phase-contrast MRI of inhaled hyperpolarized 129Xe gas. Cincinnati Children’s Hospital is a world leader in the fields of pediatric pulmonary and sleep medicine and radiology, and is the ideal environment to conduct the proposed research and for the PI to develop the essential skills to have a successful career as an independent investigator. The PI has identified primary mentors in both technical and clinical fields, and a further mentoring team to assist with specific aspects of this project. Between them, they have experience in MRI, sleep medicine, pulmonary medicine, CFD modeling, airway surgery, radiology, and biostatistics, as well as career development through this award mechanism. Their knowledge strengthens and complements the PI’s background in computational modeling of the airways and airflow within. The successful award of this project would afford the PI the opportunity to establish himself as a world leader in airway biomechanics and to improve the quality of life for pediatric patients with OSA.

该项目旨在创建一种经过验证的计算工具来预测患有以下疾病的儿科患者的手术结果阻塞性睡眠呼吸暂停 (OSA) 是一种常见疾病，仅在美国就有 220 万儿童受到影响。其特征是睡眠期间上呼吸道阻塞，导致睡眠中断并导致发育迟缓、心血管并发症和生长受损是儿童的一线治疗。 OSA 的主要目的是切除扁桃体和腺样体；然而，这些手术并不总能治愈患者。另一种治疗方法是持续气道正压通气 (CPAP)，只有 50% 的儿童能够耐受。许多儿童接受针对气道周围软组织结构的手术干预，例如然而，扁桃体、舌头和软腭和/或面部的骨骼结构的成功率。手术，以阻塞性呼吸暂停低通气指数（每小时阻塞性事件数）的减少来衡量因此，显然需要一种工具来提高这些手术的功效。并预测各种手术选择中的哪种最有效地使每个患者受益。上呼吸道呼吸气流的计算流体动力学 (CFD) 模拟可以提供这一点预测工具，可以虚拟比较各种手术方案的效果，并选择最有可能的方案以往的CFD模拟已无法提供改善患者病情的选择。有关 OSA 的信息，因为它们基于刚性几何形状，或者不包括关键的神经肌肉运动该项目使用实时磁共振成像 (MRI) 来提供解剖学和将气道运动纳入 CFD 模拟，这意味着首次对精确的体内运动进行建模。此外，由于建模基于 MRI（一种不使用电离辐射的模式），因此适合用于手术前后患者的纵向评估这些模型的体内验证。将首次通过基于 CFD 的气流速度场与生成的气流速度场的比较来实现通过吸入超极化 129Xe 气体的相差 MRI 进行。辛辛那提儿童医院是儿科肺科和睡眠医学领域的世界领先者放射学，是进行拟议研究和 PI 开发必要的基础知识的理想环境 PI 已确定了这两个方面的主要导师。技术和临床领域，以及一个进一步的指导团队来协助该项目的具体方面。他们在 MRI、睡眠医学、肺医学、CFD 建模、气道手术、放射学、生物统计学以及通过这种奖励机制的职业发展。加强和补充 PI 在气道和气流计算建模方面的背景。该项目的成功获奖将为 PI 提供机会，使其成为该领域的世界领导者。气道生物力学并改善 OSA 儿科患者的生活质量。