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Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements

Primary Purpose

Congenital Heart Disease

Status
Recruiting
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
SpO2/PIx Measurement and ML Algorithm
Sponsored by
University of California, Davis
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Congenital Heart Disease focused on measuring Machine Learning Algorithm, Pulse Oximetry

Eligibility Criteria

0 Minutes - 21 Days (Child)All SexesAccepts Healthy Volunteers

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.

Sites / Locations

  • UC Davis Medical CenterRecruiting
  • Cohen Children's Medical Center
  • University of Utah Health Care

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

SpO2 and PIx Measurement

Arm Description

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.

Outcomes

Primary Outcome Measures

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.

Secondary Outcome Measures

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.
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.
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.
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.
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.
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.

Full Information

First Posted
November 22, 2022
Last Updated
September 24, 2023
Sponsor
University of California, Davis
Collaborators
National Institutes of Health (NIH)
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1. Study Identification

Unique Protocol Identification Number
NCT05637814
Brief Title
Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
Official Title
Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
Study Type
Interventional

2. Study Status

Record Verification Date
September 2023
Overall Recruitment Status
Recruiting
Study Start Date
August 17, 2023 (Actual)
Primary Completion Date
June 30, 2027 (Anticipated)
Study Completion Date
December 31, 2027 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
University of California, Davis
Collaborators
National Institutes of Health (NIH)

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes

5. 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.
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Congenital Heart Disease
Keywords
Machine Learning Algorithm, Pulse Oximetry

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
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)
Allocation
N/A
Enrollment
240 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
SpO2 and PIx Measurement
Arm Type
Experimental
Arm Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
SpO2/PIx Measurement and ML Algorithm
Intervention Description
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 Information:
Title
Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm.
Description
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.
Time Frame
Through study completion, an average of 4 years
Secondary Outcome Measure Information:
Title
Sensitivity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Specificity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Area under the curve for receiver operating characteristics for critical congenital heart disease using dynamic ML algorithm
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Sensitivity for critical congenital heart disease using dynamic ML algorithm
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Specificity for critical congenital heart disease using dynamic ML model
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Sensitivity for critical coarctation of the aorta using dynamic ML algorithm
Description
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.
Time Frame
Through study completion, an average of 4 years
Other Pre-specified Outcome Measures:
Title
Frequency of repeated inpatient ML measurements
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Feasibility: Number of minutes needed to obtain simultaneous artifact free hand and foot measurements such that all pulse oximetry features can be included.
Description
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.
Time Frame
Through study completion, an average of 4 years
Title
Feasibility: Number of outpatient pulse oximetry measurements obtained
Description
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.
Time Frame
Through study completion, an average of 4 years

10. Eligibility

Sex
All
Minimum Age & Unit of Time
0 Minutes
Maximum Age & Unit of Time
21 Days
Accepts Healthy Volunteers
Accepts Healthy Volunteers
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.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Heather Siefkes, MD, MSCI
Phone
916-713-7697
Email
hsiefkes@ucdavis.edu
First Name & Middle Initial & Last Name or Official Title & Degree
Harshitha Naidu, BS
Phone
916-734-4489
Email
htnaidu@ucdavis.edu
Facility Information:
Facility Name
UC Davis Medical Center
City
Davis
State/Province
California
ZIP/Postal Code
95616
Country
United States
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Heather Siefkes, MD, MSCI
Email
hsiefkes@ucdavis.edu
First Name & Middle Initial & Last Name & Degree
Heather Siefkes, MD, MSCI
Facility Name
Cohen Children's Medical Center
City
Queens
State/Province
New York
ZIP/Postal Code
11040
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Robert Koppel, MD
Email
rkoppel@northwell.edu
First Name & Middle Initial & Last Name & Degree
Robert Koppel, MD
Facility Name
University of Utah Health Care
City
Salt Lake City
State/Province
Utah
ZIP/Postal Code
84102
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Whitnee Hogan, MD
Email
whitnee.hogan@hsc.utah.edu
First Name & Middle Initial & Last Name & Degree
Whitnee Hogan, MD

12. IPD Sharing Statement

Plan to Share IPD
No

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Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements

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