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Screening for Pregnancy Related Heart Failure in Nigeria

Primary Purpose

Cardiomyopathy, Pregnancy Related

Status
Active
Phase
Not Applicable
Locations
Nigeria
Study Type
Interventional
Intervention
Artificial Intelligence enabled electrocardiogram (AI-ECG)
Sponsored by
Mayo Clinic
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional screening trial for Cardiomyopathy focused on measuring Cardiomyopathy, Artificial Intelligence, Women's Health, Digital Health

Eligibility Criteria

18 Years - 49 Years (Adult)FemaleAccepts Healthy Volunteers

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

Sites / Locations

  • Rasheed Shekoni Specialist Hospital
  • University of Ilorin Teaching Hospital
  • Olabisi Onabanjo University Teaching Hospital
  • University College Hospital
  • Aminu Kano Teaching Hospital
  • Lagos University Teaching Hospital

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

No Intervention

Arm Label

Intervention

Control

Arm Description

Participants will have ECGs analyzed with artificial intelligence for cardiomyopathy detection.

Participants will have standard clinical ECGs acquired.

Outcomes

Primary Outcome Measures

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.

Secondary Outcome Measures

AI-ECG Performance overall and within Subgroups
Determine if an AI-ECG improves the detection of cardiomyopathy among Black pregnant and postpartum women.
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%)

Full Information

First Posted
June 24, 2022
Last Updated
October 7, 2023
Sponsor
Mayo Clinic
Collaborators
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)
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1. Study Identification

Unique Protocol Identification Number
NCT05438576
Brief Title
Screening for Pregnancy Related Heart Failure in Nigeria
Official Title
Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Study Type
Interventional

2. Study Status

Record Verification Date
October 2023
Overall Recruitment Status
Active, not recruiting
Study Start Date
August 15, 2022 (Actual)
Primary Completion Date
February 2024 (Anticipated)
Study Completion Date
February 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Mayo Clinic
Collaborators
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Cardiomyopathy, Pregnancy Related
Keywords
Cardiomyopathy, Artificial Intelligence, Women's Health, Digital Health

7. Study Design

Primary Purpose
Screening
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
1200 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Intervention
Arm Type
Experimental
Arm Description
Participants will have ECGs analyzed with artificial intelligence for cardiomyopathy detection.
Arm Title
Control
Arm Type
No Intervention
Arm Description
Participants will have standard clinical ECGs acquired.
Intervention Type
Other
Intervention Name(s)
Artificial Intelligence enabled electrocardiogram (AI-ECG)
Intervention Description
An artificial intelligence algorithm which analyses ECG data and generates prediction probabilities for a diagnosis of cardiomyopathy.
Primary Outcome Measure Information:
Title
Left Ventricular Ejection Fraction (LVEF) <50%
Description
Number of participants diagnosed with left ventricular ejection fraction (LVEF) <50% by echocardiography during pregnancy or within 12 months postpartum.
Time Frame
18 months
Secondary Outcome Measure Information:
Title
AI-ECG Performance overall and within Subgroups
Description
Determine if an AI-ECG improves the detection of cardiomyopathy among Black pregnant and postpartum women.
Time Frame
18 months
Title
Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm
Description
Determine the effectiveness of an AI-ECG for cardiomyopathy detection at different LVEF cut offs (<45%, <40%, ≤ 35%)
Time Frame
18 months
Other Pre-specified Outcome Measures:
Title
Adverse cardiovascular outcomes
Description
Determine the diagnostic yield of an AI-ECG on cardiovascular outcomes among pregnant and postpartum women.
Time Frame
18 months
Title
Echocardiography utilization
Description
Determine the impact of an AI-ECG on echocardiography utilization
Time Frame
18 months
Title
Effectiveness of AI point of care tools for Cardiomyopathy Detection in the Intervention Arm
Description
Develop and evaluate the diagnostic performance of an AI-enhanced point of care screening tool
Time Frame
18 months

10. Eligibility

Sex
Female
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
49 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Demilade Adedinsewo, MD, MPH
Organizational Affiliation
Mayo Clinic
Official's Role
Principal Investigator
Facility Information:
Facility Name
Rasheed Shekoni Specialist Hospital
City
Dutse
State/Province
Jigawa
Country
Nigeria
Facility Name
University of Ilorin Teaching Hospital
City
Ilorin
State/Province
Kwara
Country
Nigeria
Facility Name
Olabisi Onabanjo University Teaching Hospital
City
Sagamu
State/Province
Ogun
Country
Nigeria
Facility Name
University College Hospital
City
Ibadan
State/Province
Oyo
Country
Nigeria
Facility Name
Aminu Kano Teaching Hospital
City
Kano
Country
Nigeria
Facility Name
Lagos University Teaching Hospital
City
Lagos
Country
Nigeria

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
36966922
Citation
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.
Results Reference
background
Links:
URL
https://www.mayo.edu/research/clinical-trials
Description
Mayo Clinic Clinical Trials

Learn more about this trial

Screening for Pregnancy Related Heart Failure in Nigeria

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