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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的早产是世界5岁以下儿童和下呼吸道的最大单一死亡原因感染（LRTI）是早产婴儿住院和死亡率的主要原因。预测临床工具迫切需要在早产婴儿中LRTI的严重程度至关重要，以允许早期干预措施降低该患者组的高发病率和死亡率。我们的目标是通过开发一个客观的框架来预测早产中呼吸道疾病的严重程度使用非侵入性低辐射X射线成像生物标志物和临床参数的婴儿。在该项目的第一阶段，我们的肺科医生，新生儿学家和成像的多学科团队以及机器学习专家开发了一种名为肺气和不规则的成像软件技术卵子性放射学分析仪（Lungair）。我们的成就包括：1）建立一个精心策划的地面真相早产婴儿胸部X射线（CXR）的焦点； 2）开发机器学习算法自动定位并量化基于CXR的早产肺疾病特征（纤维化/间质性OCITE，囊性变化和过度充气）； 3）创建用于临床部署的图形用户界面；和4）在独立队列中评估我们的成像软件技术。我们还证明了成像通过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 - 早产的最常见呼吸并发症 - 累积暴露于补充O2和新生儿重症监护室（NICU）中的机械通气（p <0.001）。重要的是，我们的初步结果表明成像和临床标记（BPD严重程度）的组合提供了准确的预测 LRTI相关并发症的模型在生命的第一年（AUC = 74，p <0.01）。该第二阶段项目建立在第一阶段中开发的发现和方法的基础上。在特定目标1中，我们将将肺部疾病危险因素的模型纳入肺平台。我们的软件将摄取呼吸支持 NICU住院期间每天提供信息，并将数据与CXR分析相结合。在特定的目标2中，我们将扩展肺肺部以进行住院期间进行纵向分析，并有可能加速预测健康风险。我们还将将结果与患者的电子健康记录相结合在特定目的3中，我们将进行一项临床研究，以预期评估肺临床平台功能。该提案包括商业模式和商业化的途径肺。早期对BPD和严重LRTI高风险的早产婴儿的早期识别应改善其结果，减少住院时间并继承临床成本，并降低婴儿死亡率。另外，能够客观地量化和跟踪肺成像生物标志物也将指导治疗和临床试验随着改善婴儿的纵向监测。