Imaging biomarkers of severe respiratory infections in premature infants Phase II

早产儿严重呼吸道感染的影像生物标志物 II 期

• 批准号: 10491039
• 负责人: Andinet Asmamaw Enquobahrie
• 金额: $ 82.61万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491039
• 关键词: Address; Area; Bronchopulmonary Dysplasia; Businesses; Cause of Death; Child; Clinical; Clinical Management; Clinical Markers; Clinical Research; Collaborations; Complement; Complication; Computer software; Data; Data Set; Development; Disease Progression; Early Intervention; Early identification; Electronic Health Record; Evidence Based Medicine; Exposure to; Fibrosis; Goals; Health; Health Technology; Health system; Healthcare; Hospitalization; Image; Individual; Infant Mortality; Ingestion; Intervention; Life; Lower Respiratory Tract Infection; Lung; Lung diseases; Machine Learning; Mechanical ventilation; Methodology; Methods; Modeling; Morbidity - disease rate; Neonatal Intensive Care Units; Network-based; Outcome; Patients; Phase; Premature Infant; Process; Prospective cohort; ROC Curve; Radiology Specialty; Respiratory Disease; Respiratory Tract Infections; Risk; Risk Factors; Roentgen Rays; Severities; Severity of illness; Small Business Technology Transfer Research; Software Engineering; Specialist; Technology; Therapy Clinical Trials; Thoracic Radiography; Time; Vulnerable Populations; X-Ray Medical Imaging; algorithm development; base; clinical practice; clinical risk; cohort; commercialization; cost; disorder risk; graphical user interface; high risk; imaging biomarker; imaging software; improved; improved outcome; infant monitoring; infection risk; innovation; interstitial; longitudinal analysis; lung basal segment; lung imaging; machine learning algorithm; meetings; mortality; multidisciplinary; neural network; novel; patient health information; pediatric patients; performance tests; predictive modeling; predictive tools; premature; premature lungs; prevent; programs; prospective; quantitative imaging; research clinical testing; respiratory; risk prediction; risk stratification; standard of care; statistics; tool; usability

ABSTRACT Prematurity is the largest single cause of death in children under five in the world and lower respiratory tract infections (LRTI) are the top cause of hospitalization and mortality in premature infants. Clinical tools to predict the risk and assess the severity of LRTI in premature babies are critically needed to allow early interventions to decrease the high morbidity and mortality in this patient group. Our goal is to improve clinical practice by developing an objective framework to predict the risk and assess the severity of respiratory disease in premature babies using non-invasive low-radiation X-ray imaging biomarkers and clinical parameters. In the Phase I of this project, our multidisciplinary team of pulmonologists, neonatologists and imaging and machine learning specialists developed an imaging software technology called Lung Aeration and Irregular opacities Radiological analyzer (LungAIR). Our accomplishments include: 1) establishing a curated ground truth of focal findings in chest X-Ray (CXR) of premature babies; 2) developing a machine learning algorithm to automatically localize and quantify CXR-based prematurity lung disease signatures (fibrosis/interstitial opacities, cystic changes and hyperinflation); 3) creating a graphical user interface for clinical deployment; and 4) evaluating our imaging software technology in an independent cohort. We also demonstrated that the imaging biomarkers obtained by LungAIR correlate strongly with the severity of bronchopulmonary dysplasia (BPD)—the most common respiratory complication of prematurity-- and the cumulative exposure to supplemental O2 and mechanical ventilation in the neonatal intensive care unit (NICU) (p<0.001). Importantly, our preliminary results indicated that the combination of imaging and clinical markers (BPD severity) provide an accurate predictive model for LRTI-related complications in the first year of life (AUC=74, p<0.01). This Phase II project builds on the findings and methodology developed in Phase I. In Specific Aim 1, we will incorporate a model of lung disease risk factors in LungAIR platform. Our software will ingest respiratory support information daily during NICU hospitalization and integrate the data with CXR analysis. In Specific Aim 2, we will extend LungAIR to perform longitudinal analyses during hospitalization with the potential to accelerate the prediction of health risks. We will also integrate our results with the electronic health record of the patient for improve the clinical workflow. In Specific Aim 3 we will conduct a clinical study to prospectively evaluate the LungAIR clinical platform functionality. The proposal includes the business model and a path to commercializing LungAIR. The early identification of premature babies at high risk for BPD and severe LRTI should improve their outcome, reduce hospitalization times and inherent clinical costs, and decrease infant mortality. In addition, the ability to objectively quantify and track lung imaging biomarkers will also guide therapy and clinical trials, as well as improve the longitudinal monitoring of infants.

抽象的 早产是全球五岁以下儿童死亡的最大单一原因，下呼吸道疾病 感染（LRTI）是早产儿住院和死亡的首要原因。临床工具可用于预测。 迫切需要了解早产儿 LRTI 的风险并评估其严重程度，以便尽早采取干预措施 我们的目标是通过以下方式改善临床实践：降低该患者群体的高发病率和死亡率。 制定客观框架来预测早产儿呼吸道疾病的风险并评估其严重程度 使用非侵入性低辐射 X 射线成像对婴儿进行生物标志物和临床参数。 在该项目的第一阶段，我们的多学科团队由肺科医生、新生儿科医生和影像学专家组成 机器学习专家开发了一种名为“肺通气和不规则”的成像软件技术 不透明度放射分析仪 (LungAIR)。我们的成就包括：1) 建立了精心策划的地面事实。 早产儿胸部 X 光 (CXR) 的焦点发现 2) 开发机器学习算法 自动定位和量化基于 CXR 的早产儿肺部疾病特征（纤维化/间质混浊、 囊性变化和恶性通货膨胀）；3）创建用于临床部署的图形用户界面；4） 在一个独立的队列中评估我们的成像软件技术我们还证明了成像。 LungAIR 获得的生物标志物与支气管肺发育不良 (BPD) 的严重程度密切相关 早产儿最常见的呼吸系统并发症——以及累积暴露于补充氧气和 新生儿重症监护病房 (NICU) 的机械通气 (p<0.001) 重要的是，我们的初步结果。 表明影像学和临床标志物（BPD 严重程度）的结合提供了准确的预测 出生后第一年 LRTI 相关并发症模型（AUC=74，p<0.01）。 该第二阶段项目建立在第一阶段开发的研究结果和方法的基础上。在具体目标 1 中，我们将 我们的软件将肺部疾病风险因素模型纳入 LungAIR 平台中。 在 NICU 住院期间的日常信息，并将数据与 CXR 分析相结合，我们将在具体目标 2 中。 扩展 LungAIR 在住院期间进行纵向分析，有可能加速 我们还将把我们的结果与患者的电子健康记录结合起来。 改善临床工作流程 在具体目标 3 中，我们将进行一项临床研究来前瞻性评估 该提案包括商业模式和商业化路径。 LungAIR。早期识别患有 BPD 和严重 LRTI 的高风险早产儿应该可以改善他们的情况。 成果，减少住院时间和固有的临床费用，并降低婴儿死亡率。 客观量化和跟踪肺部成像生物标志物的能力也将指导治疗和临床试验 改善婴儿的纵向监测。