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| ID | Type | Description | Link |
|---|---|---|---|
| K12AR084222 | U.S. NIH Grant/Contract | View source | |
| UL1TR002377 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | NIH |
| National Center for Advancing Translational Sciences (NCATS) | NIH |
| National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) |
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This study will evaluate the effectiveness of an artificial intelligence-enabled ECG (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention | Experimental | Participants will have ECGs analyzed with artificial intelligence for cardiomyopathy detection. |
|
| Control | No Intervention | Participants will have standard clinical ECGs acquired. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Digital stethoscope electrocardiogram | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| Composite Adverse Cardiovascular Events | 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 |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Demilade Adedinsewo, MD, MPH | Mayo Clinic | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Rasheed Shekoni Specialist Hospital | Dutse | Jigawa State | Nigeria | |||
| University of Ilorin Teaching Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36966922 | Background | 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. | |
| 39223284 | Result |
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| ID | Title | Description |
|---|---|---|
| FG000 | Intervention | Participants had ECGs analyzed with artificial intelligence for cardiomyopathy detection. 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. |
| FG001 | Control | Participants had standard clinical ECGs acquired. |
| Title | Milestones | Reasons Not Completed | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Intervention | Participants had ECGs analyzed with artificial intelligence for cardiomyopathy detection. 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Median |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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. | Analysis was categorized by pregnant and post-partum participants. Total of each group equals the overall number of participants analyzed per arm. | Posted | Count of Participants | Participants | 18 months |
|
Long-term clinical outcomes including mortality were passively tracked for all participants (for up to 18 months) beyond the duration of active trial participation. These outcomes are also described as "adverse cardiovascular events" in order to match prevailing descriptions in published literature but are completely unrelated to the study intervention. Study defined adverse events per protocol were collected for up to 3 hours following study intervention (ECG and Echo recording).
Anticipated study related risk is limited to potential skin irritation from placement of ECG lead electrode stickers directly on the skin for ECG measurements. The "adverse cardiovascular events" reported in the manuscript are not "events" in the context of this clinical trial. They are described collectively as "adverse cardiovascular events" to facilitate a comprehensive grouping for descriptive and summarization purposes, and to ensure consistency with the existing literature.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Intervention | Participants had ECGs analyzed with artificial intelligence for cardiomyopathy detection. 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. |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Diastolic heart failure | Cardiac disorders | Systematic Assessment | Non-treatment related (not related to study intervention) |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Skin Irritation | Skin and subcutaneous tissue disorders | Systematic Assessment |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Demilade Adedinsewo, M.B, Ch.B. | Mayo Clinic | 904-953-0859 | adedinsewo.demilade@mayo.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 21, 2023 | Jan 8, 2025 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D009202 | Cardiomyopathies |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| 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 |
| 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 | 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 | 18 months |
| Echocardiography Utilization |
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 | Develop and evaluate the diagnostic performance of an AI-enhanced point of care screening tool | 18 months |
| Ilorin |
| Kwara State |
| Nigeria |
| Olabisi Onabanjo University Teaching Hospital | Sagamu | Ogun State | Nigeria |
| University College Hospital | Ibadan | Oyo State | Nigeria |
| Aminu Kano Teaching Hospital | Kano | Nigeria |
| Lagos University Teaching Hospital | Lagos | Nigeria |
| 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. |
| Not Eligible |
|
| Did not complete baseline testing |
|
| BG001 |
| Control |
Participants had standard clinical ECGs acquired. |
| BG002 | Total | Total of all reporting groups |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Race/Ethnicity, Customized | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| Control |
Participants had standard clinical ECGs acquired. |
|
|
|
| Secondary | 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 | To explain the differences in number analyzed for each section below, details are provided: Sensitivity, is calculated as True Positive (TP) / (TP + FN), as such the denominator in this case is 17. Specificity is calculated as True Negatives (TN) divided by True Negatives and False Positives (TN + FP), and the denominator is 563. Positive Predictive Value (PPV) = TP / (TP + FP), with a denominator of 122 and Negative Predictive Value (PPV) = TN / (TN + FN) with a denominator of 458. | Posted | Count of Participants | Participants | 18 months |
|
|
|
| Secondary | 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 | To explain the differences in number analyzed for each section below, details are provided: Sensitivity, is calculated as True Positive (TP) / (TP + FN), as such the denominator in this case is 20. Specificity is calculated as True Negatives (TN) divided by True Negatives and False Positives (TN + FP), and the denominator is 560. Positive Predictive Value (PPV) = TP / (TP + FP), with a denominator of 122 and Negative Predictive Value (PPV) = TN / (TN + FN) with a denominator of 458. | Posted | Count of Participants | Participants | 18 months |
|
|
|
| Secondary | 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 | To explain the differences in number analyzed for each section below, details are provided: Sensitivity, is calculated as True Positive (TP) / (TP + FN), as such the denominator in this case is 23. Specificity is calculated as True Negatives (TN) divided by True Negatives and False Positives (TN + FP), and the denominator is 557. Positive Predictive Value (PPV) = TP / (TP + FP), with a denominator of 122 and Negative Predictive Value (PPV) = TN / (TN + FN) with a denominator of 458. | Posted | Count of Participants | Participants | 18 months |
|
|
|
| Secondary | 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 | To explain the differences in number analyzed for each section below, details are provided: Sensitivity, is calculated as True Positive (TP) / (TP + FN), as such the denominator in this case is 23. Specificity is calculated as True Negatives (TN) divided by True Negatives and False Positives (TN + FP), and the denominator is 557. Positive Predictive Value (PPV) = TP / (TP + FP), with a denominator of 122 and Negative Predictive Value (PPV) = TN / (TN + FN) with a denominator of 458. | Posted | Count of Participants | Participants | 18 months |
|
|
|
| Other Pre-specified | Composite Adverse Cardiovascular Events | 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. | Posted | Count of Participants | Participants | 18 months |
|
|
|
|
| Other Pre-specified | Echocardiography Utilization | Determine the impact of an AI-ECG on echocardiography utilization | Data was not collected nor analyzed for this outcome measure | Posted | 18 months |
|
|
| Other Pre-specified | 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 | Data was not collected nor analyzed for this outcome measure | Posted | 18 months |
|
|
| 12 |
| 587 |
| 56 |
| 587 |
| 1 |
| 587 |
| EG001 | Control | Participants had standard clinical ECGs acquired. | 3 | 608 | 53 | 608 | 4 | 608 |
|
| Atrial Arrhythmias | Cardiac disorders | Systematic Assessment | Non-treatment related (not related to study intervention) |
|
| Sustained Ventricular Arrhythmias | Cardiac disorders | Systematic Assessment | Non-treatment related (not related to study intervention) |
|
| Gestational Hypertension | Pregnancy, puerperium and perinatal conditions | Systematic Assessment | Non-treatment related (not related to study intervention) |
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| Preeclampsia | Pregnancy, puerperium and perinatal conditions | Systematic Assessment | Non-treatment related (not related to study intervention) |
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| Eclampsia | Pregnancy, puerperium and perinatal conditions | Systematic Assessment | Non-treatment related (not related to study intervention) |
|
| Valvular Disease | Cardiac disorders | Systematic Assessment | Non-treatment related (not related to study intervention) |
|
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| LVEF ≤ 35% (PPV) |
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| LVEF ≤ 35% (NPV) |
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| LVEF < 40% (PPV) |
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| LVEF < 40% (NPV) |
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| LVEF < 45% (PPV) |
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| LVEF < 45% (NPV) |
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| LVEF < 50% (PPV) |
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| LVEF < 50% (NPV) |
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