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

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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.