Pulse Oximetry Screening for Congenital Heart Defects

Recruiting at 2 trial locations

Overseen By Elva Horath, IMG

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: University of California, Davis

Must not be taking: Vasoactive medications

Disqualifiers: Echocardiogram, Cardiac surgery, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

240 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

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.

Do I need to stop my current medications to join the trial?

The trial protocol does not specify if you need to stop taking your current medications. However, if you are on vasoactive medications other than prostaglandin therapy, you may not be eligible to participate.

What safety data exists for pulse oximetry screening for congenital heart defects?

The research indicates that pulse oximetry screening, especially when enhanced with machine learning algorithms, improves the detection of critical congenital heart disease (CCHD) with minimal impact on false positive rates. The addition of features like perfusion index, heart rate, and pulse delay to the standard SpO2 measurement enhances sensitivity by approximately 10 percentage points. This suggests that the enhanced screening method is safe and effective, with little risk of increasing false positives. ^{1 2 3 4 5}

Is the treatment using a machine learning algorithm for screening congenital heart defects promising?

Yes, the treatment using a machine learning algorithm for screening congenital heart defects is promising because it improves the detection of these heart problems in newborns by about 10% compared to the current method, without increasing false alarms. ^{1 2 3 6 7}

What data supports the idea that Pulse Oximetry Screening for Congenital Heart Defects is an effective treatment?

The available research shows that using pulse oximetry screening, especially when combined with machine learning algorithms, improves the detection of critical congenital heart defects (CCHD) in newborns. One study found that adding machine learning to the standard pulse oximetry method increased the detection rate by about 10 percentage points without increasing false alarms. Another study involving nearly 40,000 newborns in Sweden demonstrated that pulse oximetry is effective in identifying life-threatening heart conditions early. This suggests that pulse oximetry, particularly when enhanced with additional features and technology, is a reliable method for screening CCHD compared to traditional methods. ^{1 4 6 8 9}

Are You a Good Fit for This Trial?

This trial is for asymptomatic newborns under 22 days old being screened for critical congenital heart disease (CCHD), including those suspected of having CCHD or with a prenatal suspicion of heart defects. Newborns who've had certain cardiac interventions, are on specific medications, or have had an echocardiogram prior to enrollment are excluded.

Inclusion Criteria

I am less than 22 days old.

My newborn is healthy but needs a heart defect screening.

My newborn is suspected or confirmed to have a serious heart condition.

Exclusion Criteria

Newborns with certain types of heart defects or who have had heart surgery or current use of specific heart medications may not participate.

An echocardiogram needs to be done before enrolling the newborn.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Inpatient Screening

Non-invasive measurements of oxygenation (SpO2) and perfusion (PIx) are taken using pulse oximeters and a machine learning CCHD screening algorithm assigns a prediction every minute.

0-48 hours

Continuous monitoring

Outpatient Follow-up

Repeated pulse oximetry measurements are conducted after 48 hours of age, potentially in an outpatient setting, to enhance detection of CCHD.

Up to 2 months

Follow-up

Participants are monitored for safety and effectiveness after screening, with health status confirmed to a minimum of 2 months of age.

4 years

What Are the Treatments Tested in This Trial?

Interventions

• ML Algorithm
• SpO2/PIx Measurement

Trial Overview The study tests a machine learning algorithm that uses pulse oximetry data to screen for CCHD in infants. It aims to validate the effectiveness of combining oxygen saturation and perfusion index measurements from standard screenings with this new technology.

How Is the Trial Designed?

Group I: SpO2 and PIx Measurement Experimental Treatment 1 Intervention

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.

ML Algorithm is already approved in United States, European Union, Canada, United Kingdom for the following indications:

Approved in United States as AI/ML Algorithm for CCHD Screening for:

• Critical Congenital Heart Disease Screening

Approved in European Union as AI/ML Algorithm for CCHD Screening for:

• Critical Congenital Heart Disease Screening

Approved in Canada as AI/ML Algorithm for CCHD Screening for:

• Critical Congenital Heart Disease Screening

Approved in United Kingdom as AI/ML Algorithm for CCHD Screening for:

• Critical Congenital Heart Disease Screening

Who Is Running the Clinical Trial?

University of California, Davis

Lead Sponsor

Trials

958

Recruited

4,816,000+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Published Research Related to This Trial

Current screening for Critical Congenital Heart Disease (CCHD) using only oxygen saturation (SpO2) misses about 900 newborns in the US each year, highlighting the need for improved detection methods.

By integrating machine learning algorithms that analyze additional pulse oximetry features, the proposed screening system increased sensitivity by approximately 10 percentage points without significantly affecting the false positive rate, demonstrating a promising advancement in CCHD detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms. Lai, Z., Vadlaputi, P., Tancredi, DJ., et al.[2022]

In a study of 200 newborns with cyanotic congenital heart disease (CCHD), significant differences in pulse oxygen saturation (SpO2) levels were observed, with the lowest levels found in those with transposition of the great arteries (TGA).

Respiratory interventions were most frequently required for TGA infants, with 66% receiving continuous positive airway pressure, 33% requiring positive pressure ventilation, and 53% needing intubation, highlighting the critical need for tailored respiratory support in these high-risk newborns.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Delivery room oxygen physiology and respiratory interventions for newborns with cyanotic congenital heart disease. Thomas, AR., Ma, AL., Weinberg, DD., et al.[2023]

Pulse oximetry screening before discharge significantly improved the detection rate of duct dependent circulation in newborns, achieving a sensitivity of 82.8% and detecting 100% of cases, compared to a much lower detection rate in regions without this screening.

The introduction of pulse oximetry led to a substantial reduction in the risk of leaving the hospital undiagnosed, with only 8% of babies in West Götaland having undetected duct dependent circulation compared to 28% in other regions, ultimately preventing deaths from undiagnosed conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of pulse oximetry screening on the detection of duct dependent congenital heart disease: a Swedish prospective screening study in 39,821 newborns. de-Wahl Granelli, A., Wennergren, M., Sandberg, K., et al.[2022]

Machine learning algorithms demonstrated high accuracy in categorizing adult congenital heart disease (ACHD) patients by diagnosis, disease complexity, and functional class, achieving accuracies of 91.1%, 97.0%, and 90.6% respectively, based on a dataset of over 44,000 medical records from 10,019 patients.

The algorithms also effectively predicted the need for multidisciplinary team discussions and were linked to patient survival outcomes, indicating their potential to guide therapy and improve prognosis in ACHD patients over a median follow-up of 8 years.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Diller, GP., Kempny, A., Babu-Narayan, SV., et al.[2022]

In a study of 6,329 newborns, pulse-oximetry was found to be a safe and effective screening tool for detecting critical congenital heart disease (CCHD), with a sensitivity of 87.5% and specificity of 99.8%.

The use of pulse-oximetry identified 4 out of 7 cases of CCHD that would have been missed by clinical examination alone, highlighting its importance in routine newborn checks.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effectiveness of pulse-oximetry in addition to routine neonatal examination in detection of congenital heart disease in asymptomatic newborns. Oakley, JL., Soni, NB., Wilson, D., et al.[2015]

In a study involving 102 healthcare providers, the accuracy of interpreting pulse oximetry results for screening critical congenital heart disease was significantly higher when using a computer-based tool (98.3%) compared to a manual algorithm (81.6%).

The computer-based tool particularly improved accuracy for 'fail' scenarios (96.7% vs. 65.4%) and 'retest' scenarios (98.7% vs. 80.7%), highlighting its potential to reduce human error in critical health screenings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quality improvement in screening for critical congenital heart disease. Oster, ME., Kuo, KW., Mahle, WT.[2013]

In a study of 5487 newborns screened for congenital heart disease (CHD), only 19% sensitivity was found for detecting all CHDs and 20% for major CHDs using clinical evaluation and pulse oximetry, indicating a need for improved detection methods.

Despite the low sensitivity, the combination of clinical signs and pulse oximetry showed high specificity (88%), suggesting that while many cases may be missed, those identified as abnormal should receive prompt echocardiography for further evaluation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical screening for Congenital heart disease at birth: a prospective study in a community hospital in Kerala. Vaidyanathan, B., Sathish, G., Mohanan, ST., et al.[2022]

Pulse oximetry screening for critical congenital cardiovascular malformations (CCVM) in asymptomatic newborns showed a sensitivity of 60% and a specificity of 99.95%, indicating it is a reliable method for detecting serious heart conditions in newborns.

The study screened 11,281 newborns and detected 3 cases of CCVM, demonstrating that while the test is effective, there is a risk of false negatives for certain heart defects that may not be detectable through oximetry alone.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effectiveness of pulse oximetry screening for congenital heart disease in asymptomatic newborns. Koppel, RI., Druschel, CM., Carter, T., et al.[2022]

In a study of 963 newborns in Quito, pulse oximetry screening did not identify any critical congenital heart diseases (CHD), but it successfully detected other heart conditions like atrial septal defect (ASD) and patent ductus arteriosus (PDA).

The findings suggest that the current saturation cutoff points for identifying critical cardiac anomalies may need to be adjusted for high-altitude environments, as the mean preductal and postductal saturations were lower than typical values.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Is pulse oximetry helpful for the early detection of critical congenital heart disease at high altitude? González-Andrade, F., Echeverría, D., López, V., et al.[2019]

Citations

1. United States pubmed.ncbi.nlm.nih.gov

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms. [2022]

2. United States pubmed.ncbi.nlm.nih.gov

Delivery room oxygen physiology and respiratory interventions for newborns with cyanotic congenital heart disease. [2023]

3. England pubmed.ncbi.nlm.nih.gov

Impact of pulse oximetry screening on the detection of duct dependent congenital heart disease: a Swedish prospective screening study in 39,821 newborns. [2022]

4. England pubmed.ncbi.nlm.nih.gov

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. [2022]

5. England pubmed.ncbi.nlm.nih.gov

Effectiveness of pulse-oximetry in addition to routine neonatal examination in detection of congenital heart disease in asymptomatic newborns. [2015]

6. United States pubmed.ncbi.nlm.nih.gov

Quality improvement in screening for critical congenital heart disease. [2013]

7. India pubmed.ncbi.nlm.nih.gov

Clinical screening for Congenital heart disease at birth: a prospective study in a community hospital in Kerala. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Effectiveness of pulse oximetry screening for congenital heart disease in asymptomatic newborns. [2022]

9. United States pubmed.ncbi.nlm.nih.gov

Is pulse oximetry helpful for the early detection of critical congenital heart disease at high altitude? [2019]