NCT05438576
Completed
Not Applicable
Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Cardiomyopathy
- Sponsor
- Mayo Clinic
- Enrollment
- 1232
- Locations
- 6
- Primary Endpoint
- Left Ventricular Ejection Fraction (LVEF) <50%
- Status
- Completed
- Last Updated
- 11 months ago
Overview
Brief Summary
This study will evaluate the effectiveness of an artificial intelligence-enabled ECG (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.
Investigators
Demilade A. Adedinsewo
Principal Investigator
Mayo Clinic
Eligibility Criteria
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
Outcomes
Primary Outcomes
Left Ventricular Ejection Fraction (LVEF) <50%
Time Frame: 18 months
Number of participants diagnosed with left ventricular ejection fraction (LVEF) \<50% by echocardiography during pregnancy or within 12 months postpartum.
Secondary Outcomes
- Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm for Left Ventricular Ejection Fraction (LVEF) ≤ 35%(18 months)
- Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 40%(18 months)
- Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 45%(18 months)
- Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 50%(18 months)
Study Sites (6)
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