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Clinical Trial Details — Status: Active, not recruiting

Administrative data

NCT number NCT05438576
Other study ID # 22-000539
Secondary ID K12HD065987
Status Active, not recruiting
Phase N/A
First received
Last updated
Start date August 15, 2022
Est. completion date May 30, 2024

Study information

Verified date May 2024
Source Mayo Clinic
Contact n/a
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

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


Recruitment information / eligibility

Status Active, not recruiting
Enrollment 1200
Est. completion date May 30, 2024
Est. primary completion date February 29, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender Female
Age group 18 Years to 49 Years
Eligibility 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


Related Conditions & MeSH terms


Intervention

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

Locations

Country Name City State
Nigeria Rasheed Shekoni Specialist Hospital Dutse Jigawa
Nigeria University College Hospital Ibadan Oyo
Nigeria University of Ilorin Teaching Hospital Ilorin Kwara
Nigeria Aminu Kano Teaching Hospital Kano
Nigeria Lagos University Teaching Hospital Lagos
Nigeria Olabisi Onabanjo University Teaching Hospital Sagamu Ogun

Sponsors (3)

Lead Sponsor Collaborator
Mayo Clinic Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)

Country where clinical trial is conducted

Nigeria, 

References & Publications (1)

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. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Other Adverse cardiovascular outcomes Determine the diagnostic yield of an AI-ECG on cardiovascular outcomes among pregnant and postpartum women. 18 months
Other Echocardiography utilization Determine the impact of an AI-ECG on echocardiography utilization 18 months
Other Effectiveness of AI point of care tools for Cardiomyopathy Detection in the Intervention Arm Develop and evaluate the diagnostic performance of an AI-enhanced point of care screening tool 18 months
Primary 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. 18 months
Secondary AI-ECG Performance overall and within Subgroups Determine if an AI-ECG improves the detection of cardiomyopathy among Black pregnant and postpartum women. 18 months
Secondary 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%) 18 months
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