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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.

项目总结/文摘