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

项目总结/摘要 我们建议开发一种自动化的危重先天性心脏病（CCHD）筛查算法 使用机器学习技术来联合收割机灌注和氧合的非侵入性测量。 基于血氧饱和度（SpO 2）的筛查是目前CCHD筛查的标准，但它未能 在美国，每年可检测到高达50%的无症状新生儿CCHD或近900名新生儿。 SpO 2筛查漏诊的大多数新生儿存在主动脉阻塞缺陷，如缩窄 主动脉（CoA），不会导致脱氧血液进入循环。非侵入性测量 灌注指数（PIx）和脉搏血氧饱和度波形分析等灌注技术，有望改善 检测有缺陷的新生儿，如CoA，这是目前SpO 2最常遗漏的CCHD 筛选Pix和脉搏血氧饱和度波形都可以无创测量， 用于SpO 2筛查的设备。 我们团队的成员最近表明，增加Pix，一种非侵入性的测量脉动的方法， 血流，有可能改善CCHD检测，否则会被SpO 2筛查遗漏。然而，在这方面， Pix在短时间段（秒）内的变异性及其解释中的人为错误限制了其临床应用。 能力的此外，当前SpO 2筛选算法解释中的人为错误导致遗漏 健康新生儿的诊断和不适当的测试。因此，自动SpO 2-PIx筛查 需要一种算法来简化筛选过程，并改进对遗漏缺陷的检测 SpO 2筛查为了实现这一目标，我们将确定最佳Pix波形，以创建一个度量， 区分患有和不患有CCHD的新生儿。我们将进行脉搏血氧仪波形分析， 确定其他对新生儿CCHD具有鉴别能力的非侵入性成分。另外我们 将应用监督机器学习技术来自动化算法解释。 所提出的研究是有意义的，因为自动SpO 2-PIx筛选算法可以节省 数百名未通过SpO 2筛查确诊的CCHD新生儿的生命。此外，这是 创新，因为它将是第一个自动解释Pix测量新生儿与CCHD和 合并自动Pix和SpO 2，这将允许在后续步骤中轻松实施。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的 多学科关系，以进行未来的多中心研究，以评价这种新型SpO 2-PIx组合 一个涉及数千名新生儿的大规模算法。改善CCHD的检测将需要 在所有参与新生儿护理和筛查的个人中采用多学科方法， CCHD。此外，与工程和计算机科学的合作将是必要的自动化， SpO 2-PIx CCHD筛查算法。

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.