Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Phase: NA
Conditions: Cardiomyopathy, Pregnancy Related
Sponsor: Mayo Clinic

Screening for Peripartum 
Cardiomyopathies using 
Artificial Intelligence (SPEC-
AI)-Nigeria 
NCT05438576
21 April 2023 

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CONFIDENTIAL 
Screening for Peripartum Cardiomyopathies using Artificial Intelligence (SPEC-AI) – 
Nigeria 
 
 
Principal Investigator: 
Demilade Adedinsewo, MD 
Department of Cardiovascular Medicine 
Mayo Clinic 
4500 San Pablo Rd 
Jacksonville, FL 32224 
 
Protocol Number: (IRBe) 
 
22-000539 
 
Protocol Version 
April 21, 2023, Version (1.3) 
 
Clinical Trial Registration 
Number: 
NCT05438576 
 
 
 
 
 

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Protocol Revision Log: 
Protocol Date (Version) 
Key Changes Made 
March 1, 2022, Version (1.0) 
Initial version 
April 14, 2022, Version (1.1) 
Protocol restructured and expanded to be 
consistent with ICH guidelines 
July 25, 2022, Version (1.2) 
Minor updates to add current NCT clinical 
trial number; clarifications to protocol 
methods; reframing sampling size 
justification to increase clarity 
April 21, 2023, Version (1.3) 
Protocol updated to modify overall target 
enrollment to 1,400 study participants, in 
order to achieve an accrual target of 1,200, 
with each site accruing 200 participants (6 
sites). 
 
 
 
 

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Table of Contents 
 
1. 
INTRODUCTION............................................................................................................. 6 
1.1. 
BACKGROUND .................................................................................................................. 6 
1.2. 
INVESTIGATIONAL AGENTS .............................................................................................. 7 
1.3. 
PRELIMINARY DATA ........................................................................................................ 9 
1.4. 
RISKS AND BENEFITS ..................................................................................................... 10 
2. 
STUDY OBJECTIVES ................................................................................................... 11 
2.1. 
PRIMARY OBJECTIVE ..................................................................................................... 12 
2.2. 
SECONDARY OBJECTIVES ............................................................................................... 12 
2.3. 
EXPLORATORY OBJECTIVES ........................................................................................... 12 
3. 
STUDY DESIGN ............................................................................................................. 12 
3.1. 
GENERAL DESCRIPTION ................................................................................................. 12 
3.2. 
NUMBER OF SUBJECTS ................................................................................................... 15 
3.3. 
DURATION OF PARTICIPATION ....................................................................................... 15 
3.4. 
IDENTIFICATION OF SOURCE DATA ................................................................................ 15 
4. 
SUBJECT SELECTION ENROLLMENT AND WITHDRAWAL .......................... 16 
4.1. 
INCLUSION CRITERIA ..................................................................................................... 16 
4.2. 
EXCLUSION CRITERIA .................................................................................................... 16 
4.3. 
SUBJECT SCREENING, RECRUITMENT, AND ENROLLMENT ............................................. 16 
4.4. 
EARLY WITHDRAWAL OF SUBJECTS .............................................................................. 17 
4.4.1. 
When and How to Withdraw Subjects ................................................................... 17 
4.4.2. 
Data Collection and Follow-up for Withdrawn Subjects ..................................... 17 
5. 
ECG TESTING AND ECHOCARDIOGRAM DETAILS ......................................... 18 
5.1. 
DESCRIPTION ................................................................................................................. 18 
6. 
STUDY PROCEDURES ................................................................................................ 19 
6.1. 
SCHEDULE OF EVENTS ................................................................................................... 19 
6.2. 
VISITS ............................................................................................................................ 21 
6.2.1. 
Screening............................................................................................................... 21 
6.2.2. 
Randomization/Baseline visit ................................................................................ 21 
6.3. 
POSITIVE AI-ECG SCREEN/NEW CARDIOMYOPATHY DIAGNOSIS (INTERVENTION ARM 
ONLY) 23 
6.4. 
AGE AND SEX ECG ESTIMATION ................................................................................... 23 
7. 
STATISTICAL PLAN .................................................................................................... 23 
7.1. 
SAMPLE SIZE DETERMINATION ...................................................................................... 23 
7.2. 
STUDY ENDPOINTS ......................................................................................................... 24 
7.2.1. 
Primary Study Endpoints ...................................................................................... 24 
7.2.2. 
Secondary Study Endpoints................................................................................... 24 
7.3. 
STATISTICAL HYPOTHESES AND PLANNED ANALYSES .................................................... 24 
7.3.1. 
Descriptive Statistics ............................................................................................. 24 

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7.3.2. 
Primary Hypothesis: ............................................................................................. 24 
7.3.3. 
Secondary Hypotheses: ......................................................................................... 25 
7.3.4. 
Exploratory Hypotheses: ...................................................................................... 25 
7.4. 
ADDITIONAL STATISTICAL CONSIDERATIONS ................................................................ 26 
7.4.1. 
Handling of Missing Data ..................................................................................... 26 
7.4.2. 
Multiplicity ............................................................................................................ 26 
7.4.3. 
Interim Analysis .................................................................................................... 26 
7.4.4. 
Analysis Sets.......................................................................................................... 26 
7.4.5. 
Pre-specified analysis ........................................................................................... 26 
7.5. 
DEFINITIONS .................................................................................................................. 26 
7.6. 
RECORDING/REPORTING OF ADVERSE EVENTS .............................................................. 27 
7.7. 
STOPPING RULES ............................................................................................................ 28 
7.8. 
MEDICAL MONITORING ................................................................................................. 28 
8. 
DATA HANDLING AND RECORD KEEPING ......................................................... 28 
8.1. 
CONFIDENTIALITY ......................................................................................................... 28 
8.2. 
SOURCE DOCUMENTS .................................................................................................... 28 
8.3. 
CASE REPORT FORMS .................................................................................................... 29 
8.4. 
RECORDS RETENTION .................................................................................................... 30 
9. 
STUDY MONITORING, AUDITING, AND INSPECTING ..................................... 30 
9.1. 
STUDY SITE MONITORING PLAN .................................................................................... 30 
10. 
ETHICAL CONSIDERATIONS ................................................................................... 31 
10.1. CONFLICT OF INTEREST .................................................................................................. 31 
11. 
REFERENCES ................................................................................................................ 31 
 
 
 
 

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Study Summary 
Title 
Screening for Peripartum Cardiomyopathies using Artificial 
Intelligence (SPEC-AI) – Nigeria 
Running Title 
Screening for cardiac dysfunction in pregnancy 
Protocol Number 
22-000539 
Phase 
Other 
Methodology 
Prospective randomized controlled trial 
Overall Study 
Duration 
12 months 
Subject Participation 
Duration 
Participation will range from a single encounter/visit to a maximum of 
12 months   
Single or Multi-Site  
Multi-Site 
Objectives 
To determine the effectiveness of an AI-enabled ECG to detect 
cardiomyopathy and its impact on clinical outcomes in a 
predominantly Black obstetric population in Nigeria  
Number of Subjects 
1,400 enrolled / 1,200 accrued  
Diagnosis and Main 
Inclusion Criteria 
The condition being studied is pregnancy related cardiomyopathy  
 
Inclusion Criteria:  
• Adult female age of 18 – 49 years 
• Currently pregnant or within 12 months postpartum 
• Able to provide consent 
 
Study Product, 
Dose, Route, 
Regimen 
Intervention: AI algorithm to evaluate electrocardiogram data and 
generate prediction probabilities for a diagnosis of cardiomyopathy. To 
be performed intermittently at pre-specified time points during the study 
 
Drug, Dose, Route: Not applicable 
Statistical 
Methodology 
The analysis plan will focus on comparing AI-ECG algorithm 
predictions of low ventricular ejection fraction (<50%) with baseline 
echocardiograms as the reference criterion (clinical standard) and 
evaluating the impact of AI-ECG screening on maternal outcomes.  
 
 

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1. Introduction 
 
This document is a protocol for a human research study. This study will be carried out in 
accordance with the applicable United States government regulations, Mayo Clinic research 
policies and procedures and local regulations in Nigeria as applicable including data privacy rules 
and ethics board requirements 
1.1. Background 
 
An artificial intelligence (AI) model was developed by members of our Mayo Clinic team to 
identify cardiac dysfunction (defined as left ventricular ejection fraction (LVEF) ≤35%) using an 
electrocardiogram (ECG) alone with high discrimination - area under the curve (AUC) of 0.93. (Z. 
I. Attia et al., 2019) We validated this AI-ECG model in the emergency room setting among 
patients with undifferentiated dyspnea (D. Adedinsewo et al., 2020) and in a clinical trial among 
patients seen in the primary care setting and it remained robust. (Yao et al., 2021) 
 
Given that cardiovascular disease (CVD) is the leading cause of pregnancy-related mortality, we 
retrospectively evaluated the AI-ECG among pregnant and postpartum women seen across the 
Mayo Clinic enterprise and found it to be effective with an AUC of 0.92 (D. A. Adedinsewo et al., 
2021). These prior studies provide preliminary data and support the feasibility of using the AI-
ECG in a clinical setting. It also establishes the premise for its application to routine obstetric and 
postpartum care. Black women are disproportionately affected by pregnancy-related cardiac 
dysfunction (cardiomyopathy) and underrepresented in clinical research studies. To reduce health 
disparities and promote health equity, a prospective evaluation of the AI-ECG in a Black obstetric 
population is essential. 
 
Although maternal mortality has been on the decline worldwide, maternal mortality rates in the 
U.S. have increased over the last 20 years with rates 3 times higher now than in the 1980s. 
(CDC:Division of Reproductive Health; G.B.D. Maternal Mortality Collaborators, 2016) . 
Cardiomyopathy (a disease of the heart muscle that makes it difficult to pump blood to the rest of 
the body) is responsible for the majority of deaths among pregnant and postpartum women, with 
Black women bearing a disproportionate burden of disease. (Melinda B Davis et al., 2021; Mehta 
et al., 2020) Failure to identify CVD symptoms is believed to be contributing to this critical health 
inequity. (ACOG, 2019) In low-income countries, particularly sub-Saharan Africa, maternal 
mortality rates are up to 40 times higher (1074/100,000 live births) than in the U.S. (G.B.D. 
Maternal Mortality Collaborators, 2016). This issue is of global concern which made it a key 
priority item on the United Nations Millennium development goals. (Hogan et al., 2010) What 
remains unknown are effective methods to screen for cardiomyopathy during pregnancy and the 
postpartum period. Symptoms of normal pregnancy and cardiomyopathy frequently overlap 
(Hameed et al., 2015) making it difficult for clinicians to identify high-risk women in routine 
obstetric practice. Thus, there is a critical need to identify simple and effective screening tools to 
detect cardiomyopathy in pregnant and postpartum women. In the absence of this knowledge, the 
development of effective intervention strategies to decrease cardiomyopathy-related maternal 
mortality among Black women will remain challenging. 
 

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Our long-term goal is to identify effective strategies for cardiomyopathy detection among pregnant 
and postpartum women so that appropriate interventions can be developed to decrease 
cardiomyopathy-related maternal mortality. The overall objective of this proposal is to determine 
if an AI-ECG is an effective tool to screen for cardiomyopathy among pregnant and postpartum 
women. Our central hypothesis is that an AI-ECG improves the diagnosis of cardiomyopathy 
among pregnant and postpartum women. We have based our central hypothesis upon data from a 
retrospective analysis we conducted among pregnant and postpartum women seen across the Mayo 
Clinic enterprise which showed the AI-ECG was effective for cardiomyopathy detection. (D. A. 
Adedinsewo et al., 2021) Our rationale for this project is that its successful completion would 
provide a cost-effective and scalable option for cardiomyopathy screening in the obstetric 
population and enable the development of appropriate interventions targeted at reducing 
cardiomyopathy-related mortality. We are well-positioned to lead this project based on our team’s 
expertise and experience with developing and validating AI-ECG models for CVD detection. 
Members of our team were the first to develop and publish results supporting the AI-ECG as an 
effective method to screen for cardiomyopathy. (Z. I. Attia et al., 2019)  
 
Following the successful completion of this project, we expect to have established the 
effectiveness of a novel AI-ECG to detect cardiomyopathy in Black pregnant and postpartum 
women that will ultimately improve diagnosis and decrease maternal mortality. This achievement 
would not only cause a positive paradigm shift in the field of obstetric care, but it would also help 
(1) promote health equity, (2) increase the inclusion of women in cardiovascular clinical research, 
and (3) support the use of AI tools to improve cardiovascular care. 
  
1.2. Investigational Agents 
 
This protocol intends to evaluate a standard clinical 12-lead ECG recordings (Fig 1) and portable 
ECG device recordings (Fig 2 and 3) analyzed with AI-ECG algorithms for cardiomyopathy 
detection.  These algorithms are investigational at this time and not yet approved for medical use 
by the US Food and Drug Agency.  
 
 

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Figure 1. Standard 12-lead ECG 
 
 
 
 
Figure 2. AliveCor 6L wireless ECG device with portable 6-lead ECG displayed 
 
 
 

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Figure 3. Eko DUO Digital Stethoscope with portable single-lead ECG displayed 
 
1.3. Preliminary Data 
 
An artificial intelligence (AI) model has been developed by members of our Mayo Clinic team to 
identify cardiac dysfunction (originally defined as left ventricular ejection fraction (LVEF) ≤35%; 
although alternative LVEF thresholds are under investigation including <=40% in the current 
registration trial for the LVEF algorithm) using an electrocardiogram (ECG) alone with high 
discrimination - area under the curve (AUC) of 0.93. (Z. I. Attia et al., 2019). The initial model 
was developed using a dataset of 97,829 patients (with paired ECG and echocardiograms). 
 
The ROC curve from the published manuscript is shown below. 

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Figure 4. Network ROC and sensitivity and specificity across age and gender subsets. 
 
An ongoing pilot study has collected prospective ECG and echocardiogram data under Mayo 
Clinic IRB 21-003427 and we have enrolled 66 out of a target 100 study participants including 
standard 12-lead and portable ECG device recordings obtained at pre-specified intervals during 
the study.  
 
1.4. Risks and Benefits 
 
The risks of this research study are expected to be minimal and represent the general risks 
associated with prenatal wellness screening and routine medical care. However, there are some 
risks that are associated with study procedures. Anticipated study related risk includes the potential 
skin irritation from placement of ECG lead electrode stickers directly on the skin for 12-lead ECG 
recordings. To minimize risk for skin irritation, the ECG lead electrode stickers will only be placed 
on the skin for the duration that the ECG is recorded as with standard clinical practice with an 
estimated duration of contact less than 5 mins.   
Another risk in this study is the communication of results for the attention control arm.  Two 
experimental algorithms will be used that predict biological sex and age.  There is a potential for 
discordance in sex to be reported along with large variances in estimated age. The implications of 
such predictions is not yet known. Study staff will be trained on the algorithms and a study 
information sheet will be used to describe the study procedures and test results.  
 
As with all research, there is a chance that confidentiality could be compromised; however, we 
take precautions to minimize this risk.  
 
• All enrolling sites in Nigeria will maintain their own confidential screening logs following 
local institutional procedures, local regulations and ethics boards requirements.  
 

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• Confidentiality of patient information will be maintained by assigning study identifiers to 
the medical data.  
 
• Digital ECG data will be sent using a secure, encrypted online-based file share service with 
detailed instructions provided to study sites and as detailed in the study’s Manual of 
Operations. No identifiable data received will be shared external to Mayo Clinic. Only 
study staff will have access to the data collected. 
 
• Additional Considerations: The following is a list of study participant confidentiality 
safeguards: 
o Data flow procedures – Data identifying participants should not be transmitted 
from study sites to the Coordinating Center. 
o Electronic files – Data identifying participants that are stored electronically should 
be maintained in an encrypted form or in a separate file with device password 
protection. 
o Forms – Forms or pages containing personal identifying information should be 
separated from other pages of the data forms and retained in a secure location.  
o Data listings – Participant name, name code, hospital chart, record number, Social 
Security Number, or other unique identifiers should not be included in any 
published data listing. 
o Data distribution – Data listings that contain participant name, name code, or other 
identifiers easily associated with a specific participant should not be distributed. 
o Data disposal – Computer listings that contain participant-identifying information 
should be disposed of in an appropriate manner. 
o Access – Participant records should not be accessible to persons outside the study 
without the express written consent of the participant. 
o Storage – Study forms and related documents retained both during and after study 
completion should be stored in a secure location. 
o Passwords – Passwords provide limitations on general access to computer systems 
and to the functions that individuals can use.  Passwords should be changed on a 
regular basis. 
o User Training – Study staff with access to clinical computer systems should be 
trained in their use and in related security measures.  Training should include 
explanations of how to access the system and a discussion of the need for, and 
importance of, system security.  
 
There may be direct benefit to study subjects participating in this research study as early heart 
failure or other heart condition may be detected sooner than it may have been otherwise. Other 
than that, study participants may not receive any other direct benefit from participating in this 
study. The results of this study could potentially lead to a positive paradigm shift in the field of 
obstetric care, and help (1) promote health equity, (2) increase the inclusion of women in 
cardiovascular clinical research, and (3) support the use of AI tools to improve cardiovascular care. 
 
2. Study Objectives 
 

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2.1. Primary Objective 
 
The overall objective of this study is to determine if an artificial intelligence enhanced 
electrocardiogram (AI-ECG) is an effective tool to screen for cardiomyopathy (LVEF < 50%) in 
a predominantly Black population of pregnant and postpartum women. 
 
 
2.2. Secondary Objectives 
 
a) Determine if an AI-ECG improves the detection of cardiomyopathy among Black 
pregnant and postpartum women. 
 
b) Determine the effectiveness pf an AI-ECG for cardiomyopathy detection at different 
LVEF cut offs (<45%, <40%, ≤ 35%) 
2.3. Exploratory Objectives 
 
(a) Determine the diagnostic yield of an AI-ECG on cardiovascular outcomes among 
pregnant and postpartum women.  
(b) Determine the impact of an AI-ECG on echocardiography utilization  
(c) Develop and evaluate the diagnostic performance of an AI-enhanced point of care 
screening tool using portable, smartphone-compatible ECG devices (AIPOC-ECG) for 
the detection of cardiomyopathies in pregnant and postpartum women. 
 
3. Study Design 
3.1. General Description 
 
Nigeria is estimated to have the highest incidence of peripartum cardiomyopathy in the world, as 
high as 1 in 100 deliveries (M. B. Davis, Arany, McNamara, Goland, & Elkayam, 2020). As such, 
a prospective randomized-controlled study will be conducted at multiple sites in Northern and 
Southern Nigeria with study participants randomly assigned in a 1:1 fashion to the intervention vs.  
the control arm over a 12 to 18-month period.  
 
Following informed consent, all participants will have ECGs acquired at baseline and at selected 
intervals during pregnancy and postpartum (Fig 5 and 6). Half of the participants will be 
randomized to either undergo: 
- 
AI-ECG screening for cardiomyopathy + baseline echocardiography + AI-ECG age and 
sex estimation (intervention arm) or  
- 
Standard of care + AI-ECG age and sex estimation only (Zachi I Attia, Paul A Friedman, 
et al., 2019) (attention control arm).   
 

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Figure 5. Patient enrollment flow diagram

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Figure 6. Study procedures 
 
 

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All patients will have follow-up and medical record review to ascertain the development of study 
clinical outcomes. Patients can enter the study at any time point (Fig 6) during pregnancy or the 
postpartum period (up to 12 months after delivery). All women will have a standard 12-lead ECG 
performed at the time of enrollment and at pre-specified intervals through 12 months postpartum 
or study end (whichever is earlier). ECGs will be performed up to 7 times during the study period 
including during each trimester of pregnancy (up to 3 ECGs), post-delivery ECGs may be taken 
between delivery and 6 weeks (1 ECG), between 6 weeks and 3 months (1 ECG), between 3 and 
5 months (1 ECG), and between 5 and 12 months postpartum (1 ECG). Only participants in the 
intervention arm will have a baseline echocardiogram as well as simultaneous portable ECGs 
recorded with near real-time provision of AI-ECG cardiomyopathy prediction. Additionally, a 
newly positive AI-ECG screen for cardiomyopathy during any of the 6 visits will trigger an 
echocardiogram for participants in the intervention arm. This strategy for performing 
echocardiograms was selected as it is not practical to obtain an echocardiogram during each 
prenatal care visit due to time, skilled expertise required, and cost. In addition, the negative 
predictive value of the AI-enabled ECG is known to be very high (consistently exceeding 95%) in 
multiple internal and external patient populations (D. Adedinsewo et al., 2020; Attia et al., 2021; 
Z. I. Attia et al., 2019). Patients in the intervention group with cardiomyopathy detected on an 
echocardiogram will have a repeat echocardiogram performed at the end of the study period (or at 
12 months postpartum depending on which is earlier) as part of the study protocol to assess for left 
ventricular recovery (Fig 6). 
 
All echocardiograms will be reviewed by the local cardiologists and performed in accordance with 
American Society of Echocardiography guidelines for ejection fraction assessment as part of the 
study.  LVEF measurements will be recorded for study participants using REDCap.  
 
3.2. Number of Subjects 
 
A total of 1,400 study participants will be enrolled, in order to achieve an overall accrual target 
of 1,200 participants across sites 
3.3. Duration of Participation 
 
An individual participant will be seen ranging from a single visit to a maximum of 7 visits (at 
pre-specified time points) depending on the time they enter the study (Fig 6).  The maximum 
duration of participation for any one participant is 18 months if they enter the study in the first 
trimester (after the first 8 weeks to allow room for pregnancy confirmation). In addition, the 
maximum study duration is estimated to be 18 months due to funding/budgetary restrictions. The 
minimum duration of participation is one visit (1 day) if they enroll after 9 months postpartum 
and baseline visit is performed on the same day. 
 
3.4. Identification of Source Data 
 

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Study-specific case report forms (CRFs) will be used to document demographic and clinical data 
obtained as part of the study.  
 
All recorded or clinical and demographic data will be stored and shared with the Mayo study team 
as a limited data set using a secure online REDCap database with password protection. The 
Coordinating Center will provide individual (staff-specific) usernames and passwords to the 
electronic REDCap database for the study investigators and research staff at each site in Nigeria. 
This will help ensure attribution of data and changes to existing data to the individual study staff 
member. Mayo Clinic servers will be utilized to store the data and will be maintained by study 
staff. Any data recorded on paper documents will kept in locked cabinets at participating sites (See 
Guidance Document on Study Operations and Procedures for details).  
 
The following source data will not be directly collected in the CRFs, but will be collected through 
the secure file share transfer portal as previously described and integrated into the master database: 
• Digital ECG data  
• AI-ECG predictions  
4. Subject Selection Enrollment and Withdrawal 
4.1. Inclusion Criteria 
• Adult female age 18 – 49 years 
• Currently pregnant or within 12 months postpartum 
• Willing and able to provide informed consent 
 
4.2. Exclusion Criteria 
• Under 18 years of age 
• 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  
 
4.3. Subject Screening, Recruitment, and Enrollment  
 
Study participants will be enrolled from multiple healthcare institutions in Nigeria, estimated to 
have the highest incidence of peripartum cardiomyopathy in the world (incidence of 1 in 100 
deliveries) (M. B. Davis et al., 2020). No subjects will be enrolled at Mayo Clinic. 
 All enrolling sites in Nigeria will conduct enrollment at identified clinics or hospitals. All 
enrolling sites will develop a site-specific recruitment and enrollment plan following local 
institutional procedures, local regulations and ethics boards requirements.  
4.3.1 Screening, Recruitment, and Enrollment Plan 

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The local site investigator and/or study coordinator will pre-screen potential participants by 
reviewing medical records of patients already seen or newly identified patients. A study staff 
member will present the study information to the potential participants during a clinic or hospital 
encounter. If the participant is interested and willing to consent immediately, then an approved 
study staff will review the informed consent process with the participant. If the participant needs 
additional time to think about the study and participation, they will be given a copy of the informed 
consent form (ICF), and any other related IRB study approved document(s). Study staff will follow 
up with the potential participant at 1-2 weeks to learn if he/she is still interested and would like to 
participate. If a potential participant is still interested, a screening visit will be scheduled to review 
the ICF to obtain signatures required to enroll in the study.  
The site Study Coordinator/staff will utilize the steps that follow to screen participants for the 
study. A detailed operational plan along with templates (i.e., screening logs) is contained in the 
Study Protocol Manual of Operations.  
I. 
Pre-Screening Phase 
a. Potential participant will be identified during clinic or hospital encounters  
II. 
Screening Phase 
a. Study staff will meet with potential participant to explain the study 
b. Study staff will confirm that participant meets eligibility criteria  
c. Study staff will have the participant sign an Informed Consent Form and provide copies 
to participant while placing originals in participant file. 
d. Study staff will collect contact information for participant 
 
The study coordinators will be hired and trained locally to present the trial information without 
bias or pressure and in a culturally appropriate manner. Translators will be used at the local site as 
needed. Screening and enrollment failures and related reasons for same will be discussed at the 
monthly review meetings and potential actions will be taken as need. Attention will be paid to 
adhering to regulatory issues.  
4.4. Early Withdrawal of Subjects 
4.4.1. When and How to Withdraw Subjects 
 
Participation is strictly voluntary. Participants may withdraw at any point in the study which will 
not negatively impact future care. Investigators may withdraw participants at any point if they feel 
it is in the best interest of the participant to do so.  Reasons for withdrawal will be documented 
locally. 
4.4.2. Data Collection and Follow-up for Withdrawn Subjects 
 
In the event a participant discontinues study interventions before study completion, every effort 
will be made by the study team to have the participant continue to complete all other study 

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procedures. However, if the participant is not willing to continue study participation, the study 
team will attempt to collect the final visit data. 
 
5. ECG Testing and Echocardiogram Details  
5.1. Description 
 
Standard ECGs: The study will provide each participating site with a standard 12-lead ECG 
machine for use for the study as well as standard operating procedures (SOPs) on how to 
complete and transmit de-identified electronic ECGs to Mayo Clinic with a study identifier 
attached to each ECG file.  Each site will perform a 12-lead ECG during each participant visit 
using standard clinical protocols. The ECGs recorded will be stripped of personally identifying 
and study identifier will be attached to the digital 12-lead ECG file. The ECG file will be 
exported and transmitted to Mayo Clinic study staff in XML format using the secure Mayo 
Clinic On-Demand File transfer portal. If the stripping of identifiable information should be 
missed by the site, Mayo Clinic staff will remove the identifying information, and or treat the 
data as confidential and will keep the file secure in the Mayo Clinic Research Server where only 
study staff will have access to it.   
 
Echocardiogram: The coordinating center will cover the costs associated with performing 
echocardiograms per study protocol among study participants randomized to the intervention 
arm at each site. Results of the echocardiogram will be documented in REDCap. A repeat 
echocardiogram will be performed if a subsequent AI-ECG result flags positive cardiomyopathy 
in the intervention arm (if the baseline echocardiogram was normal). The results from the repeat 
echocardiograms will also be reported in REDCap. All echocardiograms will be reviewed by the 
local cardiologist at each site and performed in accordance with the American Society of 
Echocardiography guidelines for ejection fraction assessment. 
 
Portable point-of-care (POC) ECGs (Eko DUO Stethoscope and AliveCor Kardia Mobile 
devices): will be recorded simultaneously in the intervention arm based on device manufacturer 
protocols following training at each site. AliveCor and Eko health will provide the portable ECG 
recordings taken at the participating sites to Mayo Clinic for analysis by our data 
engineering/science team. The de-identified data collected under this IRB may be used for future 
research. Participants in the intervention arm will have a seated wireless AliveCor Kardia Mobile 
6-lead ECG (Figure 1) and a supine/seated Eko DUO Digital Stethoscope ECG recorded before 
or after a standard 12-lead ECG. The duration of patient involvement in the study will include 
consent review and signature, completion of a standard 12-lead ECG for all participants as well 
as the portable ECG recordings as described above. All ECG data will be recorded by study 
coordinators at each site following appropriate training on how to obtain the ECGs. The portable 
electrocardiographic tracings recorded by both devices are strictly for research purposes as it 
relates to this study and is not intended for use in guiding the patient’s clinical care. 
 
AliveCor Kardia 6L device: This is an FDA approved handheld smart device. This device will be 
observed for handheld recording as approved by FDA. The ECG signal is communicated 
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running the Kardia application (App). The Kardia App processes the signal into a real-time ECG. 
The data from Leads I and II are used to calculate, on a mobile or web-based app and in real 
time, Leads III, aVR, aVL, aVF. The results of the electrocardiographic tracings recorded by the 
AliveCor devices will not be entered into the electronic medical record or used to guide the 
patient’s clinical care. De-identified data from the recordings will be uploaded to the AliveCor 
cloud, which is only accessible to the study team by username and password. For individuals 
unable to record from all three electrodes, data can be acquired from two electrodes only, as 
shown in Figure 1. The AliveCor recordings will be identifiable by an assigned study ID and 
date of transmission. Data from the AliveCor device will be transmitted via the research study’s 
smartphone/tablet app to the cloud, where it will be password protected via account log-in, and 
only accessible to the research team for download to the Mayo research server. The AliveCor 
cloud server will only be utilized during the duration of the research study and is not accessible 
to external parties.  
 
Eko DUO Digital Stethoscope: This is an FDA-cleared and CE-marked electronic stethoscope 
that allows audio recording of heart sound to produce a phonocardiogram (PCG) as well as a 
single-lead ECG recording. Based on the AI-ECG model for cardiomyopathy detection 
developed by Attia et. al. (Z. I. Attia et al., 2019), Eko health has developed a single-lead version 
of the same model to be used with the Eko DUO digital stethoscope which can provide near real-
time AI-ECG predictions for cardiomyopathy at the point of care (Zachi I Attia, Jennifer Dugan, 
et al., 2019). The results of the electrocardiographic tracings recorded by the Eko DUO 
stethoscope will not be entered into the electronic medical record or used to guide the patient’s 
clinical care. The Eko DUO recordings will be identifiable by an assigned study ID and date of 
transmission. Raw data from the Eko DUO Stethoscope device will be transmitted via the 
research study’s smartphone/tablet app to the cloud, where it will be password protected via 
account log-in, and only accessible to the research team for download to the Mayo research 
server. The Eko DUO cloud server will only be utilized during the duration of the research study 
and is not accessible to external parties. 
 
Using the AliveCor or Eko DUO device to record electrocardiographic tracings should not cause 
discomfort or inconvenience other than a minimal delay to acquire the readings. The AliveCor 
Kardia 6L and Eko Duo are 510(k) cleared devices.  Between uses the electrodes and all surfaces 
will be cleaned using antiseptic (75% alcohol) wipes. Sequential numbers will be used for 
enrollment de-identification for the portable ECG recordings. Acquired data from each device 
will be stored on HIPAA-compliant, encrypted servers.  
  
6. Study Procedures 
 
6.1. Schedule of Events  
 
6.1.1 - Control Arm 
Assessment  
Screening  
Baseline, 
Randomization  
Visit 1  
Visits 2 -7   

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Inclusion/Exclusion Criteria   
 X  
X  
   
Informed Consenta    
   
X 
   
Demographics  
  
X 
   
Clinical Assessmentsb  
  
 X  
X   
Medical History   
  
 X   
   
New Clinical Diagnosis   
  
 
X  
Current Medications  
  
X  
   
Blood Chemistriesc  
  
X  
  
Hematologyd  
  
X  
  
Urine Analysise  
  
X  
X  
Inclusion/Exclusion Criteria   
 X  
X  
   
Enrollment/Randomization  
   
X  
   
12 lead ECG   
   
X  
X  
AI-ECG – Sex and Age Determination 
  
X  
X  
aConsent may be obtained prior to baseline/randomization visit 
bHeight, weight, blood pressure and heart rate  
cTo be abstracted from medical records – Serum Cr, eGFR  
dTo be abstracted from medical records – Hemoglobin (g/dl) or Hematocrit (%) or Packed Cell Volume; Hemoglobin 
Genotype (Hb AA, AS, SS, Sc); Rhesus factor (negative or positive), infectious screen.  
eTo be abstracted from medical records – Positive for proteins (Yes or No)  
 
  
6.1.2 - Intervention Arm 
Assessment  
Screening 
Baseline, 
Randomization Visit 
1  
Visits 2-7  
Inclusion/Exclusion Criteria   
 X  
X  
   
Informed Consenta    
  
X  
 
Demographics  
  
X  
   
Clinical Assessmentsb  
  
 X  
X   
Medical History   
  
 X   
   
New Clinical Diagnosis   
  
 
X  
Current Medications  
  
X  
   
Blood Chemistriesc  
  
X  
  
Hematologyd  
  
X  
  
Urine Analysise  
  
X  
X  
Enrollment/Randomization  
   
X  
   
12 lead ECG   
   
X  
X  
AI-ECG - Sex, Age, and Cardiomyopathy 
Determination 
  
X  
X  
Portable ECGf 
  
X  
X  
Echocardiogramg* 
  
X  
  
aConsent may be obtained prior to baseline/randomization visit 
bHeight, weight, blood pressure and heart rate  
cTo be abstracted from medical records – Serum Cr, eGFR  
dTo be abstracted from medical records – Hemoglobin (g/dl) or Hematocrit (%) or Packed Cell Volume; Hemoglobin 
Genotype (Hb AA, AS, SS, Sc); Rhesus factor (negative or positive), infectious screen.  
eTo be abstracted from medical records – Positive for proteins (Yes or No)  
fPortable ECGs will be performed only among those in the intervention arm   

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gEchocardiograms will be performed on all participants in the intervention arm at baseline and *if any new subsequent 
ECGs flag positive based on the AI-ECG predictions for cardiomyopathy (low ejection fraction) provided the baseline 
echocardiogram was normal. Participants with cardiomyopathy detected at any time point during the study will have 
a repeat echocardiogram at the end of study participation or 12 months postpartum (whichever is earlier) to assess for 
left ventricular recovery. 
 
6.2. Visits  
 
All patients will have follow-up and medical record review to ascertain the development of study 
clinical outcomes. Patients can enter the study at any time point during pregnancy or the 
postpartum period (up to 12 months after delivery). All women will have a standard 12-lead ECG 
performed at the time of enrollment and at pre-specified intervals through 12 months postpartum 
or study end (whichever is earlier). ECGs will be performed up to 7 times during the study period 
including during each trimester of pregnancy (up to 3 ECGs), between delivery and 6 weeks (1 
ECG), between 6 weeks and 3 months (1 ECG), between 3 and 5 months (1 ECG), and between 5 
and 12 months postpartum (1 ECG). Only participants in the intervention arm will have a baseline 
echocardiogram as well as simultaneous portable ECGs recorded with near real-time provision of 
AI-ECG cardiomyopathy prediction. Additionally, a newly positive AI-ECG screen for 
cardiomyopathy during any of the 6 visits will trigger an echocardiogram for participants in the 
intervention arm. This strategy for performing echocardiograms was selected as it is not practical 
to obtain an echocardiogram during each prenatal care visit due to time, skilled expertise required, 
and cost. In addition, the negative predictive value of the AI-enabled ECG is known to be very 
high (consistently exceeding 95%) in multiple internal and external patient populations (D. 
Adedinsewo et al., 2020; Attia et al., 2021; Z. I. Attia et al., 2019).   Patients in the intervention 
group with cardiomyopathy detected on an echocardiogram will have a repeat echocardiogram 
performed at the end of the study period (or at 12 months postpartum depending on which is 
earlier) as part of the study protocol to assess for left ventricular recovery. 
 
All echocardiograms will be reviewed for adherence to the American Society of Echocardiography 
guidelines for ejection fraction assessment as part of the study.  LVEF measurements will be 
recorded for study participants using REDCap.  
 
 
6.2.1. Screening 
 
To be conducted as described in section 4 of this protocol 
 
6.2.2. Randomization/Baseline visit  
 
After informed consent has been obtained, the following procedures will be completed per the 
schedule of events table: 
 
Control Arm 
  

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• Enrollment Randomization 
• Demographic information 
• Clinical Assessments 
• Medical history 
• Current Medications 
• Blood Chemistries 
• Hematology 
• Urine Analysis 
• 12-lead ECG 
• AI-ECG Sex & Age Determination  
 
Intervention Arm 
• Enrollment Randomization 
• Demographic information 
• Clinical Assessments 
• Medical history 
• Current Medications 
• Blood Chemistries 
• Hematology 
• Urine Analysis 
• 12-lead ECG 
• AI-ECG Sex, Age, & Cardiomyopathy Determination 
• Portable ECGs 
• Echocardiogram(s) 
 
Visits 2-7 
 
Control Arm 
  
• New Clinical Diagnosis 
• Clinical Assessments 
• Current Medications 
• Urine Analysis 
• 12-lead ECG 
• AI-ECG Sex & Age Determination 
 
 
Intervention Arm 
• New Clinical Diagnosis 
• Clinical Assessments 
• Urine Analysis 
• 12-lead ECG 
• AI-ECG Sex, Age, & Cardiomyopathy Determination 
• Portable ECGs 

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• Repeat echocardiogram for Positive AI-ECG screen/new cardiomyopathy diagnosis (if 
baseline echocardiogram was normal) 
 
6.3. Positive AI-ECG Screen/New Cardiomyopathy Diagnosis (Intervention arm only) 
 
In the event that the AI-ECG detects cardiomyopathy (and baseline echocardiogram showed 
normal ejection fraction), this would trigger an echocardiogram as part of the research study 
protocol and the results would be reviewed by a local cardiologist and provided to the managing 
obstetrician which have already been identified at each site. For participants with newly diagnosed 
cardiomyopathy (confirmed by echocardiography), the decision to initiate guideline directed 
medical therapy or other treatment will be based on a clinical evaluation by a local cardiologist at 
each site, local clinical practice standards, treatment options available and patient preferences. 
 
6.4.  Age and Sex ECG Estimation 
 
The application of the AI-ECG algorithms to estimate age and sex will occur at the coordinating 
center. As such, the estimation will be asynchronous with the clinical visits at each sites. The 
Coordinating Center will process ECGs as they arrive (~ monthly for each site) and return a report 
back to study investigators with the model results.  These results may be communicated to the 
participant at a subsequent study visit (at the provider’s discretion).  
7. Statistical Plan 
7.1. Sample Size Determination 
 
LVEF<50% is not expected to be diagnosed in the control arm due to the lack of routine 
echocardiograms, but for sample size calculations, we have assumed that 1% will be diagnosed 
based on literature derived incidence rate of 1 in 100 deliveries (M. B. Davis et al., 2020). With 
the AI-ECG screening, we hypothesize that an increase in the number of cases will be detected. 
For sample size estimation consideration,  an unpublished, ongoing pilot study conducted in the 
US estimates the prevalence as 5.3% (3/57) for <=35% and 7% (4/57) for <50%. Sample size 
estimation is based on a conservative estimate of 4% in the AI-ECG arm. At the alpha=0.05 and 
80% power, the sample size is estimated to be 424/group. For the secondary composite endpoint, 
approximately 5% of the sample is expected to experience hypertensive disorders (Ying, Catov, 
& Ouyang, 2018). We hypothesize that the addition of AI-ECG will increase the detection of 
cardiomyopathies and other structural defects of the heart. Accordingly, we assume composite 
event rates of 5% vs. 10% for the control vs. AI-ECG arm. At 80% power and alpha=0.05, a 
sample size of 434/group is required. For the final sample size, these estimates are rounded up to 
500/group (1000 total) to account for the uncertainties in the calculations. Due to the variability 
in the occurrence of cardiomyopathy across sites, need to account for study participant 
withdrawal from the study, no-shows to study visits, and loss to follow up, we plan to enroll 
1,400 patients to achieve an accrual target of 1,200 participants while ensuring balanced accrual 
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7.2. Study Endpoints 
 
7.2.1. Primary Study Endpoints 
 
The primary study endpoint will be left ventricular ejection fraction (LVEF) <50% diagnosed by 
echocardiography during pregnancy or within 12 months postpartum 
7.2.2. Secondary Study Endpoints 
 
Our secondary endpoints will include:  
- 
A composite outcome of all-cause mortality and adverse cardiovascular events (including 
cardiomyopathy or systolic heart failure, diastolic heart failure, hypertensive disorders of 
pregnancy, preeclampsia/eclampsia, valvular heart disease, heart failure-related 
hospitalization, sustained ventricular arrhythmias, resuscitated cardiac arrest, and other 
pregnancy-related complications) 
- 
Identification of LVEF ≤35%, < 40% and <45% at any time among study participants 
during the study period 
- 
Impact of AI-enabled ECG screening on echocardiography utilization  
 
7.3. Statistical Hypotheses and planned analyses 
7.3.1. Descriptive Statistics 
 
Descriptive statistics including measures of central tendency for numeric variables and 
frequency distributions for categorical variables will be calculated, as appropriate for all variables.  
Demographic, clinical, and outcome variables (primary and secondary) will be tabulated for all 
enrolled study participants. These data will be summarized for the entire cohort and stratified by 
randomized group and region.  We will summarize the measures of diagnostic performance (i.e., 
AUC, sensitivity, specificity) stratified by study entry to account for the differential follow-up 
testing that may have occurred as a result of the AI-ECG screening  
 
7.3.2. Primary Hypothesis:  
 
AI-ECG is an effective tool to screen for cardiomyopathy, defined as LVEF < 50%, in 
pregnant women.  
 
Statistical Hypothesis: 
o H0: P_ai-ecg = P_control 
o HA: P_ai-ecg  P_control 
Where P defines the proportion of participants that have documented LVEF <50% 
by the conclusion of the study.  
 

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Primary analysis: A chi-square test without continuity correction will be applied to the resulting 
two by two table (rows: random treatment assignment, columns LVEF < 50% reported by 
conclusion of the study). The primary analysis will be conducted at the alpha=0.05 level of 
significance (two-sided).  
7.3.3. Secondary Hypotheses: 
AI-ECG will provide consistent performance across race, ethnicity, and postpartum 
enrollment status. 
 
Secondary analysis: The introduction of stratification factors of race, ethnicity and postpartum 
status (all unique tables) will be used to produce a stratified contingency table. The Breslow-Day 
test of homogeneity of the odds ratio will be used to test for effect modification (differential 
treatment effects by strata). There is the potential for sparse strata with this analysis. If the odds 
ratio is not estimatable due to zero cells, the empirical logit estimator as well as pooled strata (to 
increase sample size for each strata) will be utilized.  
 
An AI-ECG is an effective tool to screen for cardiomyopathy at different LVEF cut-off 
values (<45%, <40%, ≤ 35%). 
 
Secondary analysis: Within the intervention arm that obtains Echos as a part of the study 
protocol, the AI-ECG model performance will be evaluated. First, the existing algorithm, without 
retraining, will be used to evaluate discrimination (area under the receiver operating 
characteristics curve) at various thresholds for LVEF (i.e., 50% as planned for the primary along 
with 45%, 40% and 35% as noted here). Performance of the model by the patient stratification 
factors of Age, Race, Ethnicity, and postpartum status will be considered. For these, the 
reporting standards outlined in the STARD criteria will be adhered too, e.g., inclusion of 
measures of precision (confidence intervals) and fractional values associated with measures of 
performance. 
 
7.3.4. Exploratory Hypotheses: 
 
AI-ECG will improve the diagnosis of cardiovascular disease and associated clinical 
outcomes among pregnant and postpartum women.  
 
Statistical plan: The analysis of this endpoint will use the same techniques as the primary and 
secondary analyses. Here, a composite score of any cardiovascular disease diagnoses during the 
course of the study will be utilized as the outcome measure.   
 
 
An AI-enhanced point of care screening tool using portable, smartphone-compatible ECG 
devices (AIPOC-ECG) will be effective for the detection of cardiomyopathies among Black 
pregnant and postpartum women. 
 

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Statistical plan: The primary analysis will utilize the predictions based on the clinical 12 lead 
ECG machine. This exploratory analysis will compare the model performance on the point of 
care devices with the clinical standard ECG machine.  
 
 
 
7.4. Additional Statistical Considerations 
 
7.4.1. Handling of Missing Data 
 
Missing data may occur throughout the study.  The primary analysis, which is based on the AI-
ECG prediction for cardiomyopathy is expected to be calculated once digital is received. In the 
event that the digital ECG file is missing or corrupted, attempts will be made to re-record the 
ECG. In order to ensure that these repeated ECGs are obtained in a timely fashion, ECGs will be 
reviewed at the local sites daily and will be transmitted to the coordinating center at Mayo Clinic 
at the end of every week for additional review. For demographic and medical history data, any 
missing data will be reported as such in the final analysis.  
7.4.2. Multiplicity 
 
There will be no adjustment to the level of significance for multiple testing.  All results will be 
reported unadjusted at the alpha=0.05 level of significance using estimates and 95% confidence 
intervals. 
7.4.3. Interim Analysis 
 
There is no planned interim analysis. 
 
7.4.4. Analysis Sets 
The analysis set for this study will follow the modified intention to treat principle.  To be included 
in the analysis, the participant needs to be randomized and complete at least the baseline 
assessments and testing.  
 
7.4.5. Pre-specified analysis 
A detailed study protocol and statistical analysis plan (SAP) will be written to support both the 
clinicaltrials.gov registration and publication in top medical journals. Safety and Adverse Events 
7.5. Definitions 
Unanticipated Problems Involving Risk to Subjects or Others (UPIRTSO) 
Any unanticipated problem or adverse event that meets the following three criteria:  
• Serious: Serious problems or events that results in significant harm, (which may be 
physical, psychological, financial, social, economic, or legal) or increased risk for the 

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subject or others (including individuals who are not research subjects). These include: (1) 
death; (2) life threatening adverse experience; (3) hospitalization - inpatient, new, or 
prolonged; (4) disability/incapacity - persistent or significant; (5) birth defect/anomaly; 
(6) breach of confidentiality and (7) other problems, events, or new information (i.e. 
publications, interim findings, product labeling change) that in the opinion of the local 
investigator may adversely affect the rights, safety, or welfare of the subjects or others, or 
substantially compromise the research data, AND 
• Unanticipated: (i.e., unexpected) problems or events are those that are not already 
described as potential risks in the protocol, consent document, not listed in the 
Investigator’s Brochure, or not part of an underlying disease. A problem or event is 
"unanticipated" when it was unforeseeable at the time of its occurrence. A problem or 
event is "unanticipated" when it occurs at an increased frequency or at an increased 
severity than expected, AND 
• Related: A problem or event is "related" if it is possibly related to the research procedures. 
 
Adverse Event 
An untoward or undesirable experience associated with the use of a medical product (i.e., drug, 
device, biologic) in a patient or research subject. 
Serious Adverse Event 
Adverse events are classified as serious or non-serious.  Serious problems/events can be well 
defined and include; 
• death 
• life threatening adverse experience 
• inpatient hospitalization or prolongation of existing hospitalization 
• persistent or significant disability or incapacity 
• substantial disruption of the ability to conduct normal life functions 
• birth defect/congenital anomaly 
 
and/or per protocol may be problems/events that in the opinion of the sponsor-investigator 
may have adversely affected the rights, safety, or welfare of the subjects or others, or 
substantially compromised the research data. 
 
All adverse events that do not meet any of the criteria for serious, should be regarded as non-
serious adverse events.  
 
7.6. Recording/Reporting of Adverse Events 
Anticipated study related risk is limited to potential skin irritation from placement of ECG lead 
electrode stickers directly on the skin for ECG measurements. No other adverse event is 
expected in relation to this study.  
 
Reports of skin irritation in relation to ECG electrode stickers will be captured in REDCap. 
 
Adverse events/experiences will only be collected within 3 hours following study interventions 
or within the duration of the specific clinical encounter where study interventions were 

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performed (depending on which is longer). An expected hospitalization for childbirth or other 
pregnancy related complication will be excluded. 
  
7.7. Stopping Rules  
 
No study stopping rules are proposed based on the risk profile for the study.  In the event the 
feasibility of the study is not tenable due to challenges in recruitment or other study operational 
considerations, the study may be terminated at the request of the study PI in conjunction with the 
institutional review board of record.  
7.8. Medical Monitoring 
It is the responsibility of the Principal Investigator and site PIs to oversee the safety of the study 
at his/her site.  This safety monitoring will include careful assessment and appropriate reporting 
of adverse events as noted above. 
8. Data Handling and Record Keeping 
8.1. Confidentiality 
Confidentiality of patient information is maintained by assigning study identifiers to the medical 
data before data is transmitted to Mayo Clinic. 
 
 
. 
 
 
 
 No data received will be shared external to Mayo Clinic. Only study staff will have 
access to the data collected. The CRF exported from REDCap is attached in the appendix 
section.   
 
In the event that a subject revokes authorization to collect or use PHI, the investigator retains the 
ability to use all information collected prior to the revocation of subject authorization.  For 
subjects that have revoked authorization to collect or use PHI, attempts would be made to obtain 
permission to collect at least vital status (long term survival status that the subject is alive) at the 
end of their scheduled study period. 
8.2. Source Documents 
Source data is all information, original records of clinical findings, observations, or other 
activities in a clinical trial necessary for the reconstruction and evaluation of the trial.  Source 
data are contained in source documents.  Examples of these original documents and data records 
include: hospital records, clinical and office charts, laboratory notes, memoranda, subjects’ 
diaries or evaluation checklists, pharmacy dispensing records, recorded data from automated 
instruments, x-rays, subject files, and records kept at the pharmacy, at the laboratories, and at 
medico-technical departments involved in the clinical trial. 
 
Source documents will be stored at each local site according to local data privacy rules and 
regulations.   
 

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All essential study documents will be retained by the local PI in a Participant Binder and 
generally include the following:  
• Source documents 
• Signed consent forms 
 
8.3. Case Report Forms 
 
The study case report form (CRF) is the primary data collection instrument for the study.  All data 
requested on the CRF is expected to be recorded.  All missing data must be explained.  All entries 
will be documented electronically using REDCap.   
 
Data Management 
 
This study will use REDCap to capture all clinical and study related data entries with the exception 
of ECGs. REDCap will utilize a user-friendly interface for data collection which will allow data 
entry by study coordinator/staff at each study site.  
 
Data Tracking, editing, updating and reporting will be tracked in REDCap. For statistical analysis, 
data can be exported as CSV, SAS or R files for analysis.  
 
ECG Data 
The standard 12-lead ECG:  
- 
will be collected and stored using a GE MAC 2000 device. ECGs will be exported from 
this machine and securely transferred to Mayo Clinic for analysis.  
 
Portable ECGs:  
- 
Data from the AliveCor device will be transmitted via the research study’s smartphone/tablet app 
to the cloud, where it will be password protected via account log-in, and only accessible to the 
research team for download to the Mayo research server. The AliveCor cloud server will only be 
utilized during the duration of the research study and is not accessible to external parties. The 
AliveCor cloud server will only be utilized during the duration of the research study and is not 
accessible to external parties.  
- 
Raw data from the Eko DUO Stethoscope device will be transmitted via the research study’s 
smartphone/tablet app to the cloud, where it will be password protected via account log-in, and 
only accessible to the research team for download to the Mayo research server. The Eko DUO 
cloud server will only be utilized during the duration of the research study and is not accessible 
to external parties.  
 
Echocardiographic data  
Echocardiographic data will be stored in accordance with local standard data protection and 
privacy rules and established clinical process at each participating site. Specific echo 
measurements will be abstracted from the echocardiogram report and entered into REDCap 
following review by an echo-cardiologist (in accordance with the American Society of 
Echocardiography guidelines) at each participating site.  
 

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Quality Control Procedures 
Instructions for use for the ECG devices, data entry forms on REDCap and additional reporting 
forms are attached in the appendix section.  
 
 
Study-Specific ECG Device Use Guidelines 
The following Instructions for Use (IFU) are attached in the appendix:  
- 
KardiaMobile 6L IFU 
- 
EkoDuo IFU 
- 
GE ECG machine IFU 
 
Data and Form Checks 
Data quality control checks will be performed in REDCap to identify potential data anomalies, 
such as: missing data, outliers, illogical dates, erroneous entries and other data inconsistencies. 
Data quality checks have also been built into REDCap to prevent entry of erroneous data such as 
preventing “text” entries in a numeric field and use of pre-specified checkboxes to limit data entry 
errors. 
8.4. Records Retention 
 
The study investigators will maintain records and essential documents related to the conduct of 
the study.  These will include subject case histories and regulatory documents. 
 
After the study ends, study staff shall maintain participant forms in a secure location for 3 years, 
as indicated by the protocol, federal regulations, and IRB guidance. 
 
9. Study Monitoring, Auditing, and Inspecting 
9.1. Study Site Monitoring Plan 
 
Site monitoring conducted by the coordinating center (Mayo Clinic) will take place every quarter 
via virtual video conferencing in addition to one more in-person site visit during the study 
period.   
 
During the scheduled monitoring visits, the coordinating site will: 
• Ensure the rights and safety of participants 
• Confirm that the study is conducted in accordance with guidelines 
• Ensure maintenance of required documents  
• Verify adherence to the protocol 
• Monitor the quality of data collected 
• Ensure accurate reporting and documentation of unanticipated problems 
 
During monitoring visits, the data recorded on CRFs will be reviewed to ensure: 
• Informed consent has been obtained and documented in accordance with IRB regulations 
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• Missing examinations are indicated  
• Participant disposition when exiting the study is accurately recorded 
 
Site investigators must ensure that the study coordinator or identified study staff has access to all 
study documents, including informed consent forms, intervention accountability records, and 
source documents.  
 
Once the site visit is complete, a site monitoring report will be drafted to provide feedback 
regarding any problems or issues that may have been uncovered during the visit. The report will 
state the problems uncovered during the visit and describe recommendations to correct them. A 
timeline will be included in the report to ensure that the follow-up of the issues is completed and 
implemented into the study’s procedures. 
 
10. Ethical Considerations 
This study is to be conducted according to United States government regulations and Regulatory 
standards, Mayo Clinic Institutional research policies and procedures, ethical principles of the 
International Conference on Harmonization (ICH) as well as local (Nigeria) federal, institutional 
procedures, regulations and ethics boards requirements.  
 
This protocol and any amendments will be submitted to a properly constituted local Institutional 
Review Board (IRB) for formal approval of the study. The decision of the IRB concerning the 
conduct of the study will be made in writing to the investigator before commencement of this 
study. 
 
All study participants will be provided a consent form describing this study and providing 
sufficient information for subjects to make an informed decision about their participation in this 
study.  This consent form will be submitted with the protocol for review and approval by each 
local IRB for the study.  The formal consent of a subject, using the approved IRB consent form, 
must be obtained before that subject undergoes any study procedure.  The consent form must be 
signed by the subject or the subject’s legally authorized representative, and the individual 
obtaining the informed consent. 
10.1. 
Conflict of Interest 
 
Any study team member who has a conflict of interest with this study (patent ownership, royalties, 
or financial gain greater than the minimum allowable by their institution, etc.) must have the 
conflict reviewed by a properly constituted Conflict of Interest Committee with a Committee-
sanctioned conflict management plan that has been reviewed and approved by the study sponsor-
investigator prior to participation in this study. 
 
11. References 
 
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Attia, I. Z., Tseng, A. S., Benavente, E. D., Inojosa, J. M., Clark, T. G., Malyutina, S., . . . Lopez-
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stethoscope to automatically detect a low ejection fraction using neural network 
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Davis, M. B., Arany, Z., McNamara, D. M., Goland, S., & Elkayam, U. (2020). Peripartum 
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