Screening for Pregnancy Related Heart Failure in Nigeria

Not Applicable

Completed

• Conditions: Pregnancy Related; Cardiomyopathy
• Interventions: Other: Artificial Intelligence enabled electrocardiogram (AI-ECG)

• Registration Number: NCT05438576
• Lead Sponsor: Mayo Clinic

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2022-06-24
• Study First Submitted QC: 2022-06-28
• Study First Posted: 2022-06-30
• Study Start: 2022-08-15
• Primary Completion: 2024-05-15
• Study Completion: 2024-05-15
• Results First Submitted: 2025-02-05
• Status Verified: 2025-04
• Results First Submitted QC: 2025-04-30
• Last Update Submitted: 2025-04-30
• Results First Posted: 2025-05-16
• Last Update Posted: 2025-05-16

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 1232

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Intervention	Artificial Intelligence enabled electrocardiogram (AI-ECG)	Participants will have ECGs analyzed with artificial intelligence for cardiomyopathy detection.

Primary Outcome Measures

Name	Time	Method
Left Ventricular Ejection Fraction (LVEF) <50%	18 months	Number of participants diagnosed with left ventricular ejection fraction (LVEF) \<50% by echocardiography during pregnancy or within 12 months postpartum.

Secondary Outcome Measures

Name	Time	Method
Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 40%	18 months	This is defined as a positive point-of-care AI prediction for LVEF \< 40% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography
Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 45%	18 months	This is defined as a positive point-of-care AI prediction for LVEF \<45% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography
Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 50%	18 months	This is defined as a positive point-of-care AI prediction for LVEF \<50% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography
Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm for Left Ventricular Ejection Fraction (LVEF) ≤ 35%	18 months	This is defined as a positive point-of-care AI prediction for LVEF ≤ 35% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography

Trial Locations

Locations (6)

University of Ilorin Teaching Hospital; 🇳🇬 Ilorin, Kwara, Nigeria

Olabisi Onabanjo University Teaching Hospital; 🇳🇬 Sagamu, Ogun, Nigeria

Lagos University Teaching Hospital; 🇳🇬 Lagos, Nigeria

Rasheed Shekoni Specialist Hospital; 🇳🇬 Dutse, Jigawa, Nigeria

Aminu Kano Teaching Hospital; 🇳🇬 Kano, Nigeria

University College Hospital; 🇳🇬 Ibadan, Oyo, Nigeria