Congenital Heart DiseaseNCT05637814
Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
A study testing SpO2/PIx Measurement and ML Algorithm in people with Congenital Heart Disease, run by University of California, Davis at 3 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 minutes to 21 daysHealthy volunteers can take part
- Conditions the study is for
- Congenital Heart Disease
- What is being tested?
- SpO2/PIx Measurement and ML Algorithm
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 320 participants
- Where
- 3 sitesUnited States
- Study dates
- 17 Aug 2023to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 minutes to 21 days
- All sexes
- Healthy volunteers accepted
- Child
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States3 sites, 3 recruiting
- Recruiting
UC Davis Medical Center
Davis, California, 95616
Heather Siefkes, MD, MSCI, hsiefkes@ucdavis.edu
Heather Siefkes, MD, MSCI
- Not yet recruiting
Cohen Children's Medical Center
Queens, New York, 11040
Robert Koppel, MD, rkoppel@northwell.edu
Robert Koppel, MD
- Not yet recruiting
University of Utah Health Care
Salt Lake City, Utah, 84102
Whitnee Hogan, MD, whitnee.hogan@hsc.utah.edu
Whitnee Hogan, MD
About this study
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.
Read the 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
- Being tested
- SpO2/PIx Measurement and ML Algorithm
- Keywords
- Machine Learning Algorithm, Pulse Oximetry
- Medical subject headings
- Heart Defects, Congenital
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SpO2/PIx Measurement and ML AlgorithmDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| SpO2 and PIx Measurement | Experimental | 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. Receives: Diagnostic Test: SpO2/PIx Measurement and ML Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm.
Receiver operating characteristics reflect a combination of sensitivity and specificity of a test. The investigators will identify the true positive and true negative rates for CCHD by confirming health status to a minimum of 2 months of age. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Secondary outcomes (6)
Sensitivity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
The investigators will identify the true positive rate for CCHD by confirming health status to a minimum of 2 months of age. CCHD will be defined based on echocardiogram or parent report if echocardiogram not present. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Specificity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
The investigators will identify the true negative rate by confirming health status to a minimum of 2 months of age. CCHD will be defined based on echocardiogram or parent report if echocardiogram not present. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Area under the curve for receiver operating characteristics for critical congenital heart disease using dynamic ML algorithm
Receiver operating characteristics reflect a combination of sensitivity and specificity of a test. The investigators will identify the true positive and true negative rates for CCHD by confirming health status to a minimum of 2 months of age. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Sensitivity for critical congenital heart disease using dynamic ML algorithm
The investigators will identify the true positive rate for CCHD by confirming health status to a minimum of 2 months of age. CCHD will be defined based on echocardiogram or parent report if echocardiogram not present. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Specificity for critical congenital heart disease using dynamic ML model
The investigators will identify the true negative rate by confirming health status to a minimum of 2 months of age. CCHD will be defined based on echocardiogram or parent report if echocardiogram not present. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Sensitivity for critical coarctation of the aorta using dynamic ML algorithm
Critical coarctation of the aorta is the most commonly missed CCHD. The investigators will identify the true positive rate by confirming health status to a minimum of 2 months of age. The investigators will also utilize birth defect and death registries for missing infants.
Measured: Through study completion, an average of 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Dec 2022
- Study start17 Aug 2023
- Primary completion30 Jun 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Congenital Heart DiseaseStudies of the same condition, open and under way.
- More trials run by University of California, DavisThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Non-invasive measurements of oxygenation and perfusion will be measured with pulse oximeters and a machine learning algorithm to improve sensitivity of CCHD screening.
- Masking
- None (open label)
- Enrolment
- 320 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, Davis
- Sponsor type
- University, hospital or other
- Collaborators
- National Institutes of Health (NIH)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05637814
- Sponsor's protocol ID
- 1933258
- Status last verified
- Jun 2026
- First submitted
- 22 Nov 2022
- Last updated
- 18 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
- ClinicalTrials.gov ID
- NCT05637814
- Acronym / sponsor ID
- 1933258
- Conditions
- Congenital Heart Disease
- Keywords
- Machine Learning Algorithm, Pulse Oximetry
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 320 participants (estimated)
- Lead sponsor
- University of California, DavisUniversity, hospital or other
- Collaborators
- National Institutes of Health (NIH)
- Study start
- 17 Aug 2023 (actual)
- Primary completion
- 30 Jun 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 5 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Non-invasive measurements of oxygenation and perfusion will be measured with pulse oximeters and a machine learning algorithm to improve sensitivity of CCHD screening.
| Arm | Type | Receives |
|---|---|---|
| SpO2 and PIx Measurement | Experimental | Diagnostic Test: SpO2/PIx Measurement and ML AlgorithmNon-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. |
Who may qualify
- Ages
- 0 minutes to 21 days
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SpO2/PIx Measurement and ML AlgorithmRight 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. | Diagnostic test | SpO2 and PIx Measurement |
Primary outcome
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
Receiver operating characteristics reflect a combination of sensitivity and specificity of a test. The investigators will identify the true positive and true negative rates for CCHD by confirming health status to a minimum of 2 months of age. The investigators will also utilize birth defect and death registries for missing infants.
6 secondary outcomes
Sensitivity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
Time frame: Through study completion, an average of 4 years
Specificity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
Time frame: Through study completion, an average of 4 years
Area under the curve for receiver operating characteristics for critical congenital heart disease using dynamic ML algorithm
Time frame: Through study completion, an average of 4 years
Sensitivity for critical congenital heart disease using dynamic ML algorithm
Time frame: Through study completion, an average of 4 years
Specificity for critical congenital heart disease using dynamic ML model
Time frame: Through study completion, an average of 4 years
Sensitivity for critical coarctation of the aorta using dynamic ML algorithm
Time frame: Through study completion, an average of 4 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Jun 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing SpO2/PIx Measurement and ML Algorithm in people with Congenital Heart Disease, run by University of California, Davis at 3 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 18 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Heather Siefkes, MD, MSCI (916-713-7697, hsiefkes@ucdavis.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Jun 2026.