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.

摘要 早产是世界上五岁以下儿童和下呼吸道死亡的最大单一原因 感染(LRTI)是早产儿住院和死亡的首要原因。用于预测的临床工具 评估早产儿下呼吸道感染的风险和严重程度是允许早期干预的关键。 降低这一人群的高发病率和死亡率。我们的目标是通过以下方式改善临床实践 建立一个客观的框架来预测早产儿呼吸道疾病的风险和评估其严重性 婴儿采用无创低辐射X射线成像生物标志物和临床参数。 在这个项目的第一阶段，我们的多学科团队由肺科医生、新生儿专家和成像和 机器学习专家开发了一种名为肺充气和不规则的成像软件技术 不透光放射分析仪(LUNGAIR)。我们的成就包括：1)建立了精心策划的地面真理 早产儿胸部X光(CXR)的局灶性发现；2)开发机器学习算法以 自动定位和量化基于CXR的早产儿肺部疾病征象(纤维化/间质浑浊， 囊性变化和恶性充气)；3)创建用于临床部署的图形用户界面；以及4) 在一个独立的队列中评估我们的成像软件技术。我们还演示了成像 LUNGAIR获得的生物标记物与BPD的严重程度密切相关-- 早产儿最常见的呼吸道并发症--以及累积暴露于补充氧气和 新生儿重症监护病房中的机械通风(p&lt；0.001)。重要的是，我们的初步结果 表明结合影像和临床标记物(BPD严重程度)可以提供准确的预测。 第一年LRTI相关并发症的模型(AUC=74，p&lt；0.01)。 此第二阶段项目建立在第一阶段制定的结果和方法的基础上。在具体目标1中，我们将 在LongAIR平台中纳入肺部疾病危险因素模型。我们的软件将摄取呼吸支持 NICU住院期间的日常信息，并将数据与CXR分析相结合。在具体目标2中，我们将 扩展LongAIR以在住院期间执行纵向分析，有可能加快 健康风险预测。我们还将把我们的结果与患者的电子健康记录相结合，以便 完善临床工作流程。在具体目标3中，我们将进行一项临床研究，以前瞻性地评估 龙腾AIR临床平台功能。该提案包括商业模式和商业化途径 龙艾尔。早期识别BPD的高危早产儿和严重的LRTI应该改善他们的 结果，减少住院时间和固有的临床成本，并降低婴儿死亡率。此外， 客观量化和跟踪肺成像生物标记物的能力也将指导治疗和临床试验 AS改善了对婴儿的纵向监测。