Screening for Pregnancy Related Heart Failure in Nigeria

Pregnancy Related Clinical Trial

Official title:

Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05438576
• Other study ID #: 22-000539
• Secondary ID: K12HD065987
• Status: Active, not recruiting
• Phase: N/A
• Start date: August 15, 2022
• Est. completion date: May 30, 2024

Study information

• Verified date: May 2024
• Source: Mayo Clinic
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This study will evaluate the effectiveness of an artificial intelligence-enabled ECG (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1200
• Est. completion date: May 30, 2024
• Est. primary completion date: February 29, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years to 49 Years

Eligibility

Inclusion Criteria:

• Currently pregnant or within 12 months postpartum

• Willing and able to provide informed consent

Exclusion Criteria:

• Complex congenital heart disease (single ventricle physiology or significant shunts with cardiac structural changes)

• Significant conduction abnormalities (ventricular pacing on recorded ECG, pacemaker dependence, or severely abnormal/bizarre QRS morphology on ECG tracings)

• Unable or unwilling to provide consent

Related Conditions & MeSH terms

• Cardiomyopathies
• Cardiomyopathy
• Pregnancy Related

Intervention

Other:
• Artificial Intelligence enabled electrocardiogram (AI-ECG)
An artificial intelligence algorithm which analyses ECG data and generates prediction probabilities for a diagnosis of cardiomyopathy.

Locations

Country	Name	City	State
Nigeria	Rasheed Shekoni Specialist Hospital	Dutse	Jigawa
Nigeria	University College Hospital	Ibadan	Oyo
Nigeria	University of Ilorin Teaching Hospital	Ilorin	Kwara
Nigeria	Aminu Kano Teaching Hospital	Kano
Nigeria	Lagos University Teaching Hospital	Lagos
Nigeria	Olabisi Onabanjo University Teaching Hospital	Sagamu	Ogun

Sponsors (3)

Lead Sponsor	Collaborator
Mayo Clinic	Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)

Country where clinical trial is conducted

Nigeria, 

References & Publications (1)

Adedinsewo DA, Morales-Lara AC, Dugan J, Garzon-Siatoya WT, Yao X, Johnson PW, Douglass EJ, Attia ZI, Phillips SD, Yamani MH, Tobah YB, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Noseworthy PA, Carter RE. Screening for peripartum cardiomyopathies using artificial intelligence in Nigeria (SPEC-AI Nigeria): Clinical trial rationale and design. Am Heart J. 2023 Jul;261:64-74. doi: 10.1016/j.ahj.2023.03.008. Epub 2023 Mar 25. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Adverse cardiovascular outcomes	Determine the diagnostic yield of an AI-ECG on cardiovascular outcomes among pregnant and postpartum women.	18 months
Other	Echocardiography utilization	Determine the impact of an AI-ECG on echocardiography utilization	18 months
Other	Effectiveness of AI point of care tools for Cardiomyopathy Detection in the Intervention Arm	Develop and evaluate the diagnostic performance of an AI-enhanced point of care screening tool	18 months
Primary	Left Ventricular Ejection Fraction (LVEF) <50%	Number of participants diagnosed with left ventricular ejection fraction (LVEF) <50% by echocardiography during pregnancy or within 12 months postpartum.	18 months
Secondary	AI-ECG Performance overall and within Subgroups	Determine if an AI-ECG improves the detection of cardiomyopathy among Black pregnant and postpartum women.	18 months
Secondary	Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm	Determine the effectiveness of an AI-ECG for cardiomyopathy detection at different LVEF cut offs (<45%, <40%, = 35%)	18 months