Screening for Pregnancy Related Heart Failure in Nigeria

April 30, 2025 updated by: Demilade A. Adedinsewo, Mayo Clinic

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

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Actual)

1232

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Nigeria
      • Kano, Nigeria
        • Aminu Kano Teaching Hospital
      • Lagos, Nigeria
        • Lagos University Teaching Hospital
    • Jigawa
      • Dutse, Jigawa, Nigeria
        • Rasheed Shekoni Specialist Hospital
    • Kwara
      • Ilorin, Kwara, Nigeria
        • University of Ilorin Teaching Hospital
    • Ogun
      • Sagamu, Ogun, Nigeria
        • Olabisi Onabanjo University Teaching Hospital
    • Oyo
      • Ibadan, Oyo, Nigeria
        • University College Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 49 years (Adult)

Accepts Healthy Volunteers

Yes

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Screening
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Intervention
Participants will have ECGs analyzed with artificial intelligence for cardiomyopathy detection.
Other: Digital stethoscope electrocardiogram
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.
No Intervention: Control
Participants will have standard clinical ECGs acquired.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
18 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
18 months
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
18 months
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
18 months
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
18 months

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
18 months
Echocardiography Utilization
Time Frame: 18 months
Determine the impact of an AI-ECG on echocardiography utilization
18 months
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
18 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mayo Clinic

Collaborators

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

Investigators

  • Principal Investigator: Demilade Adedinsewo, MD, MPH, Mayo Clinic

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 15, 2022

Primary Completion (Actual)

May 15, 2024

Study Completion (Actual)

May 15, 2024

Study Registration Dates

First Submitted

June 24, 2022

First Submitted That Met QC Criteria

June 28, 2022

First Posted (Actual)

June 30, 2022

Study Record Updates

Last Update Posted (Actual)

May 16, 2025

Last Update Submitted That Met QC Criteria

April 30, 2025

Last Verified

April 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 22-000539
  • UL1TR002377 (U.S. NIH Grant/Contract)
  • K12AR084222 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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