Clinical trial NCT05438576: Screening for Pregnancy Related Heart Failure in Nigeria
Official title: Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Cardiomyopathy
Pregnancy Related
Keywords: Cardiomyopathy
Artificial Intelligence
Women's Health
Digital Health
Lead sponsor: Mayo Clinic
Sponsor class: OTHER
Collaborators: name: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
name: National Center for Advancing Translational Sciences (NCATS)
name: National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
First posted: 2022-06-30
Enrollment: 1232
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 49
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

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.


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

Primary outcomes:
measure: Left Ventricular Ejection Fraction (LVEF) <50%
timeFrame: 18 months
Secondary outcomes:
measure: Effectiveness of AI-ECG for Cardiomyopathy Detection in the Intervention Arm for Left Ventricular Ejection Fraction (LVEF) ≤ 35%
timeFrame: 18 months
measure: Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 40%
timeFrame: 18 months
measure: Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 45%
timeFrame: 18 months
measure: Effectiveness AI-ECG for Cardiomyopathy Detection in the Intervention Arm in LVEF < 50%
timeFrame: 18 months
Other outcomes:
measure: Composite Adverse Cardiovascular Events
timeFrame: 18 months
measure: Echocardiography Utilization
timeFrame: 18 months
measure: Effectiveness of AI Point of Care Tools for Cardiomyopathy Detection in the Intervention Arm
timeFrame: 18 months
Locations:
city: Dutse
country: Nigeria
facility: Rasheed Shekoni Specialist Hospital
state: Jigawa
city: Ilorin
country: Nigeria
facility: University of Ilorin Teaching Hospital
state: Kwara
city: Sagamu
country: Nigeria
facility: Olabisi Onabanjo University Teaching Hospital
state: Ogun
city: Ibadan
country: Nigeria
facility: University College Hospital
state: Oyo
city: Kano
country: Nigeria
facility: Aminu Kano Teaching Hospital
city: Lagos
country: Nigeria
facility: Lagos University Teaching Hospital
Study officials:
affiliation: Mayo Clinic
name: Demilade Adedinsewo, MD, MPH
role: PRINCIPAL_INVESTIGATOR
References:
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.
citation: Adedinsewo DA, Morales-Lara AC, Afolabi BB, Kushimo OA, Mbakwem AC, Ibiyemi KF, Ogunmodede JA, Raji HO, Ringim SH, Habib AA, Hamza SM, Ogah OS, Obajimi G, Saanu OO, Jagun OE, Inofomoh FO, Adeolu T, Karaye KM, Gaya SA, Alfa I, Yohanna C, Venkatachalam KL, Dugan J, Yao X, Sledge HJ, Johnson PW, Wieczorek MA, Attia ZI, Phillips SD, Yamani MH, Tobah YB, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Noseworthy PA, Carter RE; SPEC-AI Nigeria Investigators. Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial. Nat Med. 2024 Oct;30(10):2897-2906. doi: 10.1038/s41591-024-03243-9. Epub 2024 Sep 2.