Machine Learning for CCHD Screening using Dynamic Data

使用动态数据进行 CCHD 筛查的机器学习

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要我建议开发和测试一种使用脉冲动态数据的机器学习 (ML) 算法用于危重先天性心脏病 (CCHD) 筛查的血氧饱和度 (SpO2) 筛查。然而，目前的 CCHD 筛查标准未能检测出 50% 的无症状新生儿；美国每年有近 900 名新生儿被 SpO2 筛查漏诊。伴有全身性梗阻的缺陷，例如主动脉缩窄（CoA），但不会导致脉搏低氧血症。血氧测定法还可以测量非侵入性测量，例如灌注指数 (PIx)、股动脉延迟、心率和其他波形特征介绍其他脉搏血氧测定法。我最近的工作揭示了改进的 CCHD 检测。使用结合脉搏血氧测定功能的 ML 算法，该算法将 CCHD 检测改进到至少 100 倍。至少 93%，包括改进 CoA 检测，同时保持模型的高特异性。取决于两个单独的测量，包括右侧同时无伪影的波形拥有一个具有动态预后的模型，可以预测婴儿的结果随着新数据的加入而改变，获得两个波形的时间可能会更好。需要理解在可能移动的婴儿中没有伪影的因素才能实施。因此，我将开发并测试一种结合脉搏血氧测定功能和结合了重复测量的动态数据，允许新生儿进行预测分类 (CCHD vs no-CCHD）随着新数据的合并而改变。我将通过两种方式来实现。住院患者测量，并将在外部验证我们最近开发的 ML 算法。还将测试“重复”屏幕是否有任何初始“失败”，这是一种模仿当前 SpO2 标准屏幕的方法第二种方法将结合测量，预计误报率将保持在 1% 以下。 48 小时后（包括门诊患者）的 CCHD 筛查尚未进行研究。大多数新生儿在 CoA 变得更严重的年龄左右接受常规门诊随访临床上明显，因此更有可能通过非侵入性灌注评估来检测。这项研究意义重大，因为包含灌注数据的动态筛查模型可以节省每年，数百名患有 CCHD 且未通过 SpO2 筛查诊断出来的新生儿的生命受到影响。之所以具有创新性，是因为它利用了现成的非侵入性脉搏血氧饱和度数据，并将使用动态数据（住院病人和门诊病人）允许新生儿的预后随着新数据的变化而改变通过这次学习和职业计划，我将获得机器学习技能，重点是动态学习。我将利用该提案的结果和技能来研究一个方法。对我们的算法进行集群随机试验并评估实施过程。