Machine Learning for CCHD Screening using Dynamic Data

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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.