Recruiting

Heart Screening

Sponsor:

UCLA

Code:

NCT05637814

Conditions

Congenital Heart Disease

Eligibility Criteria

Sex: All

Age: 0

Healthy Volunteers: Accepted

Interventions

SpO2/PIx Measurement and ML Algorithm

Study Details

Brief summary:

The purpose of this study is to implement and externally validate an inpatient ML algorithm that combines pulse oximetry features for critical congenital heart disease (CCHD) screening.

Conditions

Congenital Heart Disease

Study ID

NCT05637814

Start date

Aug 17, 2023

Status verified date

Jun, 2026

Completion date

Dec 31, 2027

Anticipated

Primary completion date

Jun 30, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 0

Healthy Volunteers: Accepted

Inclusion Criteria:

  • Age < 22 days
  • Fetuses suspected to have congenital heart disease
  • Newborns with suspected/confirmed critical congenital heart disease
  • Asymptomatic newborn undergoing SpO2 screening for CCHD

Exclusion Criteria:

  • Echocardiogram completed prior to enrollment as the newborn would then no longer be considered "asymptomatic undergoing SpO2 screening for CCHD"
  • For Newborns with confirmed/suspected congenital heart disease (CHD): a) Patent ductus arteriosus and/or atrial septal defect/patent foramen ovale without other defects, b) Corrective cardiac surgical or catheter intervention performed before enrollment or c) Current infusions of vasoactive medications other than prostaglandin therapy.

Study Design

Enrollment

320 participants

Anticipated

Intervention Model

Single group

Primary purpose

Diagnostic

Interventions and Outcome Measures

Arms

experimental: SpO2 and PIx Measurement

Non-invasive measurements of oxygenation (SpO2) and perfusion (PIx) will be measured with pulse oximeters and a ML CCHD screening algorithm will be assigning a prediction every minute.

Interventions

SpO2/PIx Measurement and ML Algorithm

Right upper and any lower extremity oxygen saturation (SpO2) and perfusion index (PIx) will be measured and an online ML inference model will be used to classify a newborn as healthy versus CCHD as new pulse oximetry data is collected.

Primary outcome measure

  • Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm. [ Time Frame: Through study completion, an average of 4 years ]

Central Contacts and Locations

Central contacts

Heather Siefkes, MD, MSCI

916-713-7697hsiefkes@ucdavis.edu

Elva Horath, IMG

916-713-7697ethorath@ucdavis.edu

Locations

UC Davis Medical Center

Recruiting

Davis, California, United States, 95616

Contacts

Heather Siefkes, MD, MSCI

hsiefkes@ucdavis.edu

Principal Investigator:

Heather Siefkes, MD, MSCI

More Information

Sponsor

University of California, Davis

Last update posted

Jun 18, 2026

Last verified

Jun, 2026

Keywords

  • Machine Learning Algorithm
  • Pulse Oximetry

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by University of California, Davis on 2026-06-18.