Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements

• ClinicalTrials.gov Identifier: NCT05637814
• Recruitment Status: Not yet recruiting
• First Posted: December 5, 2022
• Last Update Posted: December 9, 2022
• Sponsor: University of California, Davis
• Collaborator: National Institutes of Health (NIH)
• Information provided by (Responsible Party): Heather Siefkes, University of California, Davis

Study Description

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.

Condition or disease	Intervention/treatment	Phase
Congenital Heart Disease	Diagnostic Test: SpO2/PIx Measurement and ML Algorithm	Not Applicable

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

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 240 participants
• Allocation: N/A
• Intervention Model: Single Group Assignment
• Intervention 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)
• Primary Purpose: Diagnostic
• Official Title: Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
• Estimated Study Start Date: February 1, 2023
• Estimated Primary Completion Date: June 30, 2027
• Estimated Study Completion Date: December 31, 2027

Arms and Interventions

Arm	Intervention/treatment
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.	Diagnostic Test: 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.

Outcome Measures

Primary Outcome Measures :

1. 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.

Secondary Outcome Measures :

1. 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 ]
   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.
2. 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 ]
   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.
3. 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 ]
   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.
4. Sensitivity for critical congenital heart disease using dynamic ML algorithm [ Time Frame: Through study completion, an average of 4 years ]
   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.
5. Specificity for critical congenital heart disease using dynamic ML model [ Time Frame: Through study completion, an average of 4 years ]
   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.
6. Sensitivity for critical coarctation of the aorta using dynamic ML algorithm [ Time Frame: Through study completion, an average of 4 years ]
   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.

Other Outcome Measures:

1. Frequency of repeated inpatient ML measurements [ Time Frame: Through study completion, an average of 4 years ]
   If a newborn has an initial "fail" during the inpatient ML screening algorithm, then 1 repeated measurement will occur within 3 hours after waiting at least 30 minutes. If the next repeated measurement is a "fail" then the final classification assigned will be a "fail." If the repeat measurement is a "pass" the final classification will be a "pass." To gauge impact on nursing time for repeated measurements, The investigators will quantify how often these repeated measurements occur.
2. Feasibility: Number of minutes needed to obtain simultaneous artifact free hand and foot measurements such that all pulse oximetry features can be included. [ Time Frame: Through study completion, an average of 4 years ]
   In order to incorporate the radiofemoral delay component of the pulse oximetry features, the hand and foot waveforms need to be artifact free simultaneously. The pulse oximetry device will give a result every minute to give the investigators an idea on how long it may take to reach simultaneously artifact free waveforms.
3. Feasibility: Number of outpatient pulse oximetry measurements obtained [ Time Frame: Through study completion, an average of 4 years ]
   Pulse oximetry measurements are not currently conducted in the outpatient setting. Thus, the investigators will assess feasibility for future trials based on how many outpatient measurements are obtained versus missed in the study protocol.

Eligibility Criteria

• Ages Eligible for Study: 0 Minutes to 21 Days (Child)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes

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.

Contacts and Locations

Contacts

Contact: Heather Siefkes, MD, MSCI; 916-713-7697; hsiefkes@ucdavis.edu

Contact: Harshitha Naidu, BS; 916-734-4489; htnaidu@ucdavis.edu

Locations

United States, California

UC Davis Medical Center

Davis, California, United States, 95616

Contact: Heather Siefkes, MD, MSCI hsiefkes@ucdavis.edu

Principal Investigator: Heather Siefkes, MD, MSCI

United States, New York

Cohen Children's Medical Center

Queens, New York, United States, 11040

Contact: Robert Koppel, MD rkoppel@northwell.edu

Principal Investigator: Robert Koppel, MD

United States, Utah

University of Utah Health Care

Salt Lake City, Utah, United States, 84102

Contact: Whitnee Hogan, MD whitnee.hogan@hsc.utah.edu

Principal Investigator: Whitnee Hogan, MD

Sponsors and Collaborators

University of California, Davis

National Institutes of Health (NIH)

More Information

• Responsible Party: Heather Siefkes, Associate Professor, University of California, Davis
• ClinicalTrials.gov Identifier: NCT05637814
• Other Study ID Numbers: 1933258
• First Posted: December 5, 2022
• Last Update Posted: December 9, 2022
• Last Verified: December 2022

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Heather Siefkes, University of California, Davis:

Machine Learning Algorithm; Pulse Oximetry

Additional relevant MeSH terms:

Heart Diseases; Heart Defects, Congenital; Cardiovascular Diseases; Cardiovascular Abnormalities; Congenital Abnormalities