Screening for Pregnancy Related Heart Failure in Nigeria

Mayo Clinic1,232 enrolled

Overview

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

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

NONE

Enrollment

1,232

Conditions

Cardiomyopathy Pregnancy Related

Interventions

Digital stethoscope electrocardiogramOTHER

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.

Eligibility

Sex: FEMALEMin age: 18 YearsMax age: 49 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (6)

Rasheed Shekoni Specialist Hospital

Dutse, Jigawa State, Nigeria

University of Ilorin Teaching Hospital

Ilorin, Kwara State, Nigeria

Olabisi Onabanjo University Teaching Hospital

Sagamu, Ogun State, Nigeria

University College Hospital

Ibadan, Oyo State, Nigeria

Outcomes

Primary Outcomes

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.

Time frame: 18 months

Secondary Outcomes

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

This is defined as a positive point-of-care AI prediction for LVEF ≤ 35% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography

Time frame: 18 months

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

This is defined as a positive point-of-care AI prediction for LVEF \< 40% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography

Time frame: 18 months

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

This is defined as a positive point-of-care AI prediction for LVEF \<45% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography

Time frame: 18 months

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

This is defined as a positive point-of-care AI prediction for LVEF \<50% (maximum prediction across all stethoscope recording locations) confirmed with echocardiography

Time frame: 18 months

Data from ClinicalTrials.gov

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