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

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2020 · $192,195

## Abstract

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...

## Key facts

- **NIH application ID:** 10018507
- **Project number:** 5R21HD099239-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Heather M Siefkes
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,195
- **Award type:** 5
- **Project period:** 2019-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018507

## Citation

> US National Institutes of Health, RePORTER application 10018507, Improving Critical Congenital Heart Disease Screening and Detection of "Secondary" Targets (5R21HD099239-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10018507. Licensed CC0.

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