Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements

NCT ID: NCT05637814

Last Updated: 2025-07-16

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 51 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-08-17
• Study Completion Date: 2024-08-02

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.

Detailed Description

The study will externally validate an algorithm that combines non-invasive oxygenation and perfusion measurements as a screening tool for CCHD. In a previous study, the investigators created an algorithm that combines non-invasive measurements of oxygenation and perfusion over at least two measurements using machine learning (ML) techniques. The prior model was created and tested using internal validation (k-fold validation). Thus, the investigators will test the model on an external sample of patients to test generalizability of the model. Additionally, the team will trial a repeated measurement for any "failure" of the screen to assess impact on the false positive rate. Study team will also use repeated pulse oximetry measurements (up to 4 total and including measurements after 48 hours of age, which may be done outpatient) to create a new algorithm that incorporates new data over time. The central hypothesis is that the addition of non-invasive perfusion measurements will be superior to SpO2-alone screening for CCHD detection and a model that incorporates repeated measurements will enhance detection of CCHD while preserving the specificity.

Conditions

Congenital Heart Disease

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

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.

Group Type: EXPERIMENTAL

SpO2/PIx Measurement and ML Algorithm

Intervention Type: DIAGNOSTIC_TEST

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.

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 0 Minutes
• Maximum Eligible Age: 21 Days
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

University of California, Davis

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

UC Davis Medical Center

Davis, California, United States

Cohen Children's Medical Center

Queens, New York, United States

University of Utah Health Care

Salt Lake City, Utah, United States

Countries

United States

Other Identifiers

1933258

Identifier Type: -

Identifier Source: org_study_id