Improving Critical Congenital Heart Disease Screening With Addition of Perfusion Measurements

The purpose of this research is to evaluate and create a new clinical prediction model for CCHD screening that combines non-invasive measurements of oxygenation and perfusion.

This study will create and test an enhanced critical congenital heart defect (CCHD) screen that combines non-invasive measurements of oxygenation and perfusion in two ways. The first will use current equipment without automated interpretation and the second will require additional software/device but will automate the interpretation. To work towards development of those two algorithms in this stage of the study, the study team will download oxygen saturation (SpO2) and perfusion index (PIx) measurements from newborns with and without congenital heart disease (CHD) and apply statistical tests to the data afterwards to identify parameters to include in the newly developed screening algorithms. The overall objective is to create an automated screening algorithm to improve acyanotic CCHD detection, preventing morbidity and mortality associated with diagnoses missed by SpO2 screening. The central hypothesis is that the addition of non-invasive perfusion measurements to SpO2 will be superior to SpO2-alone screening for detection of acyanotic CCHD. The data collected during this study will be used to develop the two algorithms and the algorithms will be compared to the standard of care using statistical tests on the data after the fact (k-fold validation). The newly developed algorithms will not be tested in-situ on patients during this study.