Machine Learning for CCHD Screening using Dynamic Data

使用动态数据进行 CCHD 筛查的机器学习

• 批准号: 10588951
• 负责人: Heather M Siefkes
• 金额: $ 16.35万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-10 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588951
• 关键词: Affect; Age; Algorithms; Aortic coarctation; Artificial Intelligence; Blood flow; Cessation of life; Characteristics; Classification; Clinical; Cluster randomized trial; Congenital Abnormality; Congenital Heart Defects; Critical Congenital Heart Defects; Data; Defect; Detection; Device or Instrument Development; Devices; Diagnosis; Diagnostic; Ductus Arteriosus; Hand; Heart Rate; Hospitalization; Hospitals; Hour; Hypoxemia; Infant; Inpatients; Intervention; Knowledge; Label; Leadership; Machine Learning; Measurement; Measures; Modeling; Morbidity - disease rate; Morphologic artifacts; National Institute of Child Health and Human Development; Neonatal; Neonatal Screening; Newborn Infant; Non-Invasive Detection; Nurseries; Obstetric pharmacology; Obstruction; Outcome; Outpatients; Output; Oxygen; Patients; Perfusion; Perinatal; Perinatology; Pregnancy; Prospective cohort; Pulse Oximetry; Randomized; Regression Analysis; Regulation; Rest; Scanning; Series; Site; Specificity; Techniques; Testing; Therapeutic; Time; Training; United States; Voting; Work; algorithmic bias; blood perfusion; career; cohort; congenital heart disorder; efficacy evaluation; follow-up; foot; heart disease risk; implementation evaluation; implementation process; implementation science; improved; indexing; innovation; machine learning algorithm; machine learning classifier; machine learning model; mortality; neonatal period; outcome prediction; pediatric pharmacology; point of care; preservation; prognostication; routine screening; screening; skills; tertiary care; Affect; Age; Algorithms; Aortic coarctation; Artificial Intelligence; Blood flow; Cessation of life; Characteristics; Classification; Clinical; Cluster randomized trial; Congenital Abnormality; Congenital Heart Defects; Critical Congenital Heart Defects; Data; Defect; Detection; Device or Instrument Development; Devices; Diagnosis; Diagnostic; Ductus Arteriosus; Hand; Heart Rate; Hospitalization; Hospitals; Hour; Hypoxemia; Infant; Inpatients; Intervention; Knowledge; Label; Leadership; Machine Learning; Measurement; Measures; Modeling; Morbidity - disease rate; Morphologic artifacts; National Institute of Child Health and Human Development; Neonatal; Neonatal Screening; Newborn Infant; Non-Invasive Detection; Nurseries; Obstetric pharmacology; Obstruction; Outcome; Outpatients; Output; Oxygen; Patients; Perfusion; Perinatal; Perinatology; Pregnancy; Prospective cohort; Pulse Oximetry; Randomized; Regression Analysis; Regulation; Rest; Scanning; Series; Site; Specificity; Techniques; Testing; Therapeutic; Time; Training; United States; Voting; Work; algorithmic bias; blood perfusion; career; cohort; congenital heart disorder; efficacy evaluation; follow-up; foot; heart disease risk; implementation evaluation; implementation process; implementation science; improved; indexing; innovation; machine learning algorithm; machine learning classifier; machine learning model; mortality; neonatal period; outcome prediction; pediatric pharmacology; point of care; preservation; prognostication; routine screening; screening; skills; tertiary care

PROJECT SUMMARY/ABSTRACT I propose to develop and test a machine learning (ML) algorithm that uses dynamic data from pulse oximetry for critical congenital heart disease (CCHD) screening. Oxygen saturation (SpO2)-based screening is the current standard for CCHD screening; however, it fails to detect 50% of asymptomatic newborns with CCHD or nearly 900 newborns in the United States annually. Most newborns missed by SpO2 screening have defects with systemic obstruction, such as coarctation of the aorta (CoA), that do not cause hypoxemia. Pulse oximetry can also measure non-invasive measurements such as perfusion such as perfusion index (PIx), radiofemoral delay, heart rate, and other waveform characteristics. Introduction of other pulse oximetry features is expected to improve CCHD and CoA detection. My recent work revealed improved CCHD detection using ML algorithms that combined pulse oximetry features. The algorithms improved CCHD detection to at least 93%, including improved detection of CoA, while maintaining high specificity. However, the model depended on two separate measurements including simultaneously artifact free waveforms in both the right hand and a foot. Having a model with dynamic prognostication that allows for an infant’s predicted outcome to change as new data is incorporated could be better. Additionally, the amount of time to obtain two waveforms that are artifact free in a possibly moving baby needs to be understood for implementation. Therefore, I will develop and test a ML algorithm that combines pulse oximetry features and incorporates dynamic data from repeated measurements allowing a newborn’s predicted classification (CCHD vs no-CCHD) to change as new data is incorporated. I will do this in two ways. The first will utilize only inpatient measurements and will externally validate our recently developed ML algorithm. This first approach will also test a “repeat” screen for any initial “fails,” an approach that mimics the current SpO2 standard screen and is expected to keep the false positive rate below 1%. The second approach will incorporate measurements after 48 hours of age (including from the outpatient setting). Outpatient CCHD screening has not been studied. Most newborns are seen for routine follow up outpatient around the age at which CoA becomes more clinically apparent, and thus, more likely to be detected by non-invasive perfusion assessments. This study is significant because a dynamic screening model that includes perfusion data could save the lives of hundreds of newborns with CCHD that are not diagnosed by SpO2 screening annually. Additionally, it is innovative because it makes use of readily available non-invasive pulse oximetry data and will use dynamic data (inpatient and outpatient) that allows for a newborn’s prognostication to change as new data is incorporated. From this study and career plan, I will gain skills in machine learning with emphasis in dynamic approaches, and implementation science. I will use the results and skills from this proposal to then study a cluster randomized trial of our algorithm and assess implementation processes.

项目摘要/摘要 我建议开发和测试机器学习（ML）算法，该算法使用脉冲中的动态数据 关键先天性心脏病（CCHD）筛查的血氧仪。基于氧饱和（SPO2）的筛选为 当前的CCHD筛选标准；但是，它无法检测到50％的无症状新生儿 美国年度CCHD或近900名新生儿。 SPO2筛查错过的大多数新生儿有 具有全身异议的缺陷，例如主动脉（COA）的协调，不会引起低氧血症。脉冲 血氧仪还可以测量非侵入性测量值，例如灌注指数（pix）， 放射线延迟，心率和其他波形特征。引入其他脉搏血氧饱和度 预计功能将改善CCHD和COA检测。我最近的工作显示CCHD检测有所改善 使用将脉搏血氧饱和度特征组合在一起的ML算法。该算法将CCHD检测提高到AT 至少93％，包括改善COA的检测，同时保持高特异性。但是，模型 取决于两个单独的测量值，包括仅在右边 手和脚。拥有具有动态编程的模型，该模型允许婴儿的预测结果 随着新数据的合并可能会更好。此外，获得两个波形的时间 在可能的移动婴儿中不含伪像的人需要了解实施。 因此，我将开发并测试一种结合脉搏血氧仪特征和的ML算法 从重复测量中结合了动态数据，允许新生儿的预测分类（CCHD） 与No-CCHD）更改，随着新数据的合并。我将以两种方式进行。第一个仅利用 住院测量值，并将在外部验证我们最近开发的ML算法。第一种方法 还将测试任何初始“失败”的“重复”屏幕，这是一种模拟当前SPO2标准屏幕的方法 并有望将假阳性率保持在1％以下。第二种方法将包含测量 48小时后（包括从门诊环境中）。门诊CCHD筛查尚未研究。 大多数新生儿都可以在COA变得更加多的年龄左右进行常规后续门诊 临床上很明显，因此更有可能通过非侵入性灌注评估来检测。 这项研究很重要，因为包含灌注数据的动态筛选模型可以节省 数百名患有CCHD的新生儿每年未通过SPO2筛查来诊断。另外，它 具有创新性，因为它可以利用易于可用的非侵入性脉搏血氧仪数据，并将使用动态 数据（住院和门诊），允许新生儿的提示更改，因为新数据是 并入。从这项研究和职业计划中，我将获得机器学习的技能，重点是动态 方法和实施科学。我将利用该提案中的结果和技能来研究 聚类我们算法和评估实施过程的随机试验。