Improving Critical Congenital Heart Disease Screening and Detection of "Secondary" Targets

改善危重先天性心脏病筛查和“次要”目标检测

• 批准号: 10018507
• 负责人: Heather M Siefkes
• 金额: $ 19.22万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018507
• 关键词: Affect; Algorithms; American Heart Association; Aortic coarctation; Automatic Data Processing; Blood; Blood Circulation; Blood Pressure; Blood flow; California; Cardiac; Caring; Cessation of life; Characteristics; Childhood; Clinical; Collaborations; Congenital Abnormality; Critical Congenital Heart Defects; Critical Illness; Data; Defect; Detection; Diagnosis; Early Diagnosis; Engineering; Equipment; Evaluation; Funding; Future; Goals; Individual; Infant; Infrastructure; Interruption; Intervention; Lesion; Lower Extremity; Machine Learning; Measurement; Measures; Methods; Morbidity - disease rate; Multicenter Studies; National Heart, Lung, and Blood Institute; National Institute of Child Health and Human Development; Neonatal Screening; New York; Newborn Infant; Obstruction; Oxygen; Perfusion; Physiologic pulse; Population; Process; Pulsatile Flow; Pulse Oximetry; Research; Savings; Screening Result; Screening procedure; Sensitivity and Specificity; Specificity; Techniques; Testing; Time; Ultrasonography; United States; Upper Extremity; Validation; aortic arch; automated algorithm; base; clinical application; cohort; computer science; congenital heart disorder; high reward; high risk; human error; improved; indexing; infant death; innovation; interdisciplinary approach; member; mortality; multidisciplinary; neonatal period; novel; prenatal; prevent; screening; supervised learning

PROJECT SUMMARY/ABSTRACT We propose to develop an automated critical congenital heart disease (CCHD) screening algorithm using machine learning techniques to combine non-invasive measurements of perfusion and oxygenation. Oxygen saturation (SpO2)-based screening is the current standard for CCHD screening, however it fails to detect up to 50% of asymptomatic newborns with CCHD or nearly 900 newborns in the United States annually. The majority of newborns missed by SpO2 screening have defects with aortic obstruction, such as coarctation of the aorta (CoA), that do not result in deoxygenated blood entering circulation. Non-invasive measurements of perfusion such as perfusion index (PIx) and pulse oximetry waveform analysis is expected to improve the detection of newborns with defects such as CoA, which is currently the most commonly missed CCHD by SpO2 screening. Both PIx and pulse oximetry waveforms can be measured non-invasively and with the same equipment used for SpO2 screening. Members of our team recently showed that the addition of PIx, a non-invasive measurement of pulsatile blood flow, has the potential to improve CCHD detection otherwise missed by SpO2 screening. However, variability of PIx over brief time periods (seconds) and human error in its interpretation limit its clinical capabilities. Additionally, human error in interpretation of the current SpO2 screening algorithm leads to missed diagnoses and inappropriate testing in healthy newborns. Therefore, an automated SpO2-PIx screening algorithm is needed to both simplify the screening process, and improve detection of defects that are missed with SpO2 screening. In order to achieve that, we will identify the optimal PIx waveforms to create a metric that discriminates between newborns with and without CCHD. We will perform pulse oximetry waveform analysis to identify other non-invasive components with discriminatory capacity for newborns with CCHD. Additionally, we will apply supervised machine learning techniques to automate the algorithm interpretation. The proposed research is significant because an automated SpO2-PIx screening algorithm could save the lives of hundreds of newborns with CCHD that are not diagnosed by SpO2 screening. Additionally, this is innovative as it will be the first automatic interpretation of PIx measurement among newborns with CCHD and merging of automated PIx and SpO2, which will allow for easy implementation at later steps. Through collaboration with four pediatric cardiac centers, we will establish the infrastructure and necessary multidisciplinary relationships to conduct future multicenter studies to evaluate this novel combined SpO2-PIx algorithm on a large scale involving thousands of newborns. Improving the detection of CCHD will require a multidisciplinary approach among all the individuals involved in the care and screening of newborns with CCHD. Additionally, collaboration with engineering and computer sciences will be necessary to automate the SpO2-PIx CCHD screening algorithm.

项目摘要/摘要 我们建议开发自动关键先天性心脏病（CCHD）筛查算法 使用机器学习技术结合灌注和氧合的非侵入性测量。 基于氧饱和（SPO2）的筛选是CCHD筛选的当前标准，但是它未能 每年在美国发现多达50％的无症状新生儿或近900名新生儿。 SPO2筛查错过的大多数新生儿都患有主动脉阻塞的缺陷，例如缩短 主动脉（COA），不会导致血液进入循环。非侵入性测量 预计灌注索引（PIX）和脉搏血氧仪波形分析将改善 检测具有COA等缺陷的新生儿，这是SPO2最常见的CCHD 筛选。 Pix和Pulse Oximetry波形都可以非侵入性地测量，并且相同 用于SPO2筛选的设备。 我们团队的成员最近表明，添加pix是脉冲的非侵入性测量 血流有可能改善SPO2筛选遗漏的CCHD检测。然而， pix在短时间内（秒）和解释中的人为错误的变异性限制了其临床 功能。另外，当前SPO2筛选算法解释中的人为错误导致错过 健康新生儿的诊断和不适当的测试。因此，自动SPO2-PIX筛选 需要算法来简化筛选过程，并改善遗漏的缺陷检测 使用SPO2筛选。为了实现这一目标，我们将确定最佳pix波形，以创建一个指标 区分有和没有CCHD的新生儿。我们将执行脉搏血氧仪波形分析 通过CCHD确定具有新生儿歧视能力的其他非侵入性成分。另外，我们 将应用监督的机器学习技术来自动化算法解释。 拟议的研究很重要，因为自动化的SPO2-PIX筛选算法可以节省 尚未通过SPO2筛查诊断的数百名具有CCHD的新生儿的生活。另外，这是 创新性，因为它将是对新生儿中CCHD和CCHD和 合并自动PIX和SPO2，这将允许在以后的步骤中轻松实现。通过 与四个儿科心脏中心合作，我们将建立基础设施和必要 多学科关系以进行未来的多中心研究来评估这一新型SPO2幼犬 大规模的算法涉及数千名新生儿。改进CCHD的检测将需要 所有参与新生儿护理和筛查的人之间的多学科方法 CCHD。此外，必须与工程和计算机科学合作以自动化 SPO2-PIX CCHD筛选算法。