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Completed

NCT Number: NCT05438576

Screening for Pregnancy Related Heart Failure in Nigeria

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

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Key information

Conditions

Cardiomyopathy Cardiomyopathies Cardiovascular Diseases Heart Diseases Pregnancy Related

Age range

18 year–49 year

Sex eligibility

Female

Study type

Interventional

Phase

Not applicable

Primary location

Rasheed Shekoni Specialist Hospital, Dutse, Jigawa State, Nigeria

Loading trial locations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Digital stethoscope electrocardiogram

Other

Digital stethoscope artificial intelligence enabled electrocardiogram (AI-ECG). An artificial intelligence algorithm which analyses ECG data and generates prediction probabilities for a diagnosis of cardiomyopathy.

Primary outcomes

  1. 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

  1. Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm for Left Ventricular Ejection Fraction (LVEF) ≤ 35%

    Time frame: 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

  2. Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 40%

    Time frame: 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

  3. Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 45%

    Time frame: 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

  4. Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 50%

    Time frame: 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

Other outcomes

  1. Composite Adverse Cardiovascular Events

    Time frame: 18 months

    The number of subjects to experience composite cardiovascular events with include any of the following: diastolic heart failure, gestational hypertension, pre-eclampsia, eclampsia, valvular heart disease, atrial arrhythmias and sustained ventricular arrhythmias.

  2. Echocardiography Utilization

    Time frame: 18 months

    Determine the impact of an AI-ECG on echocardiography utilization

  3. Effectiveness of AI Point of Care Tools for Cardiomyopathy Detection in the Intervention Arm

    Time frame: 18 months

    Develop and evaluate the diagnostic performance of an AI-enhanced point of care screening tool

Sponsors and collaborators

Lead sponsor

Mayo Clinic

Other

Collaborators

  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
  • National Center for Advancing Translational Sciences (NCATS)
  • National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

Registry information

Official study title

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

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Jun 30, 2022
Registry last updated
May 16, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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