Clinical trial NCT05637814: 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
Overall status: RECRUITING
Phase: NA
Conditions: Congenital Heart Disease
Keywords: Machine Learning Algorithm
Pulse Oximetry
Lead sponsor: University of California, Davis
Sponsor class: OTHER
Collaborators: name: National Institutes of Health (NIH)
First posted: 2022-12-05
Enrollment: 240
Sex: ALL
Minimum age (years): 0
Maximum age (years): 21
Standard ages: CHILD
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
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.
Primary purpose: DIAGNOSTIC

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.

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.

Primary outcomes:
measure: Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm.
timeFrame: Through study completion, an average of 4 years
Secondary outcomes:
measure: Sensitivity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
timeFrame: Through study completion, an average of 4 years
measure: Specificity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)
timeFrame: Through study completion, an average of 4 years
measure: Area under the curve for receiver operating characteristics for critical congenital heart disease using dynamic ML algorithm
timeFrame: Through study completion, an average of 4 years
measure: Sensitivity for critical congenital heart disease using dynamic ML algorithm
timeFrame: Through study completion, an average of 4 years
measure: Specificity for critical congenital heart disease using dynamic ML model
timeFrame: Through study completion, an average of 4 years
measure: Sensitivity for critical coarctation of the aorta using dynamic ML algorithm
timeFrame: Through study completion, an average of 4 years
Other outcomes:
measure: Frequency of repeated inpatient ML measurements
timeFrame: Through study completion, an average of 4 years
measure: Feasibility: Number of minutes needed to obtain simultaneous artifact free hand and foot measurements such that all pulse oximetry features can be included.
timeFrame: Through study completion, an average of 4 years
measure: Feasibility: Number of outpatient pulse oximetry measurements obtained
timeFrame: Through study completion, an average of 4 years
Locations:
city: Davis
country: United States
facility: UC Davis Medical Center
state: California
city: Queens
country: United States
facility: Cohen Children's Medical Center
state: New York
city: Salt Lake City
country: United States
facility: University of Utah Health Care
state: Utah
Study officials:
References: