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

• 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
• 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; 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

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.

该项目旨在创建一个经过验证的计算工具，以预测儿科患者的手术结果 阻塞性睡眠呼吸暂停（OSA）。 OSA是一种常见的状况，仅在美国就影响了220万儿童。这是 在睡眠期间被表征为上空气道阻塞，这会导致睡眠干扰并导致 发育延迟，心血管并发症和增长受损。儿童的第一道治疗 使用OSA，是去除扁桃体和腺样体；但是，这些手术并不总是能治愈患者。 另一种治疗方法是，连续气道压力（CPAP）仅由50％的儿童耐受。所以， 许多孩子接受了针对气道周围软组织结构的手术干预措施，例如 扁桃体，舌头和柔软的口感和/或脸部的奖励结构。但是，这些成功率 手术，以减少阻塞性呼吸暂停 - 炎症指数的减少（每小时阻塞性事件 睡眠），令人惊讶的低。因此，显然需要一种工具来提高这些手术的效率 并预测各种手术选择中的哪些将最有效地使每个患者受益。 上呼吸道呼吸道气流的计算流体动力学（CFD）可以提供 预测工具，可以比较各种手术选项的影响，并且最有可能的选项很可能 为了改善患者的状况。以前的CFD模拟无法提供 有关OSA的信息，因为它们是基于刚性几何形状的，或不包括神经肌肉运动，钥匙 OSA中的组件。该项目使用实时磁共振成像（MRI）提供解剖学和 气道向CFD模拟的运动，这意味着首次对确切的体内运动进行了建模。 此外，由于建模基于MRI，这种模式不使用电离辐射，因此是合适的 用于手术前后患者的纵向评估。这些模型的体内验证 通过将基于CFD的气流速度场与生成的气流速度场进行比较，将首次实现 通过遗传过极化129 XE气体的相对比对比度MRI。 辛辛那提儿童医院是儿科肺和睡眠医学领域的世界领导者 放射学，是进行拟议研究和PI开发基本研究的理想环境 成为独立调查员的成功职业的技能。 PI已经确定了这两者的主要导师 技术和临床领域，以及一个进一步的心理团队，以协助该项目的特定方面。之间 他们有MRI，睡眠医学，肺医学，CFD建模，气道手术， 放射学和生物统计学以及通过这种奖励机制的职业发展。他们的知识 优势和理解PI在内部气道和气流的计算建模中的背景。 该项目的成功奖励将使PI有机会成为世界领导者 气道生物力学，并改善OSA儿科患者的生活质量。