PROTEA, A Southern African Multicenter Congenital Heart Disease Registry and Biorepository: Rationale, Design, and Initial Results

  • Frontiers in Pediatrics, Volume 9, Year 2021

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Study Justification:
– The PROTEA project aims to establish a comprehensive Congenital Heart Disease (CHD) cohort for southern Africa.
– The study will contribute to research on the epidemiology and genetic determinants of CHD in the region.
– The findings will help identify potential under-diagnosis of mild to moderate CHD and differences in CHD management and outcomes.
Study Highlights:
– The study enrolled 1,473 participants over a 2-year period.
– The median age of participants was 1.9 years.
– The most common CHD subtypes in the cohort were ventricular septal defect (VSD), atrial septal defect (ASD), patent ductus arteriosus, atrioventricular septal defect (AVSD), and tetralogy of Fallot.
– Certain mild CHD subtypes were found to be less prevalent in the PROTEA cohort compared to international estimates, while certain severe subtypes were more prevalent.
Study Recommendations:
– Conduct further research to understand the reasons for the differences in CHD subtype prevalence.
– Investigate potential under-diagnosis of mild to moderate CHD and explore differences in CHD management and outcomes.
– Emphasize the need for robust CHD epidemiological research in the southern African region.
Key Role Players:
– Researchers and scientists specializing in congenital heart disease.
– Medical professionals and specialists in cardiology and genetics.
– Hospital administrators and staff involved in patient recruitment and data collection.
– CHD advocacy groups and organizations.
Cost Items for Planning Recommendations:
– Research personnel salaries and benefits.
– Data collection and management systems.
– Laboratory equipment and supplies for genetic analysis.
– Travel and accommodation for researchers and participants.
– Administrative and logistical support.
– Communication and dissemination of research findings.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study is a prospective multicenter registry and biorepository with a large initial cohort of 1,473 participants. The methods used to compare the cohort-prevalences of CHD-subtypes with international findings are clearly described. However, the abstract does not provide specific details about the study design, data collection methods, or statistical analysis. To improve the strength of the evidence, the abstract could include more information about the study design, such as the inclusion and exclusion criteria, recruitment process, and data collection procedures. Additionally, providing more details about the statistical analysis, such as the specific tests used and the significance levels, would enhance the clarity and robustness of the findings.

Objectives: The PartneRships in cOngeniTal hEart disease (PROTEA) project aims to establish a densely phenotyped and genotyped Congenital Heart Disease (CHD) cohort for southern Africa. This will facilitate research into the epidemiology and genetic determinants of CHD in the region. This paper introduces the PROTEA project, characterizes its initial cohort, from the Western Cape Province of South Africa, and compares the proportion or “cohort-prevalences” of CHD-subtypes with international findings. Methods: PROTEA is a prospective multicenter CHD registry and biorepository. The initial cohort was recruited from seven hospitals in the Western Cape Province of South Africa from 1 April 2017 to 31 March 2019. All patients with structural CHD were eligible for inclusion. Descriptive data for the preliminary cohort are presented. In addition, cohort-prevalences (i.e., the proportion of patients within the cohort with a specific CHD-subtype) of 26 CHD-subtypes in PROTEA’s pediatric cohort were compared with the cohort-prevalences of CHD-subtypes in two global birth-prevalence studies. Results: The study enrolled 1,473 participants over 2 years, median age was 1.9 (IQR 0.4–7.1) years. Predominant subtypes included ventricular septal defect (VSD) (339, 20%), atrial septal defect (ASD) (174, 11%), patent ductus arteriosus (185, 11%), atrioventricular septal defect (AVSD) (124, 7%), and tetralogy of Fallot (121, 7%). VSDs were 1.8 (95% CI, 1.6–2.0) times and ASDs 1.4 (95% CI, 1.2–1.6) times more common in global prevalence estimates than in PROTEA’s pediatric cohort. AVSDs were 2.1 (95% CI, 1.7–2.5) times more common in PROTEA and pulmonary stenosis and double outlet right ventricle were also significantly more common compared to global estimates. Median maternal age at delivery was 28 (IQR 23–34) years. Eighty-two percent (347/425) of mothers used no pre-conception supplementation and 42% (105/250) used no first trimester supplements. Conclusions: The cohort-prevalence of certain mild CHD subtypes is lower than for international estimates and the cohort-prevalence of certain severe subtypes is higher. PROTEA is not a prevalence study, and these inconsistencies are unlikely the result of true differences in prevalence. However, these findings may indicate under-diagnosis of mild to moderate CHD and differences in CHD management and outcomes. This reemphasizes the need for robust CHD epidemiological research in the region.

The PROTEA study is a prospective cohort of CHD in both children and adults which commenced in April 2017. The aim was to enroll 1,200 registry participants and collect 500 DNA repository samples over a 2-year period from April 1, 2017 to March 31, 2019. Enrolment is ongoing. Patients are recruited to Aim 1, the CHD registry, via convenience sampling primarily from three tertiary centers in the Western Cape Province of South Africa: Red Cross War Memorial Children’s Hospital (RCWMCH), Tygerberg Hospital (TBH) and Groote Schuur Hospital (GSH) via the neonatal, pediatric, adult, and obstetric clinics and wards. Participants are also enrolled from the Mowbray Maternity Hospital, pediatric cardiology outreach clinics at George, Paarl, and Worcester Hospitals, and via engagement with CHD advocacy groups and CHD awareness events (Figure 2). Additionally, recruitment has begun at Windhoek Central Hospital, Namibia, however these participants are not included in this analysis. To minimize selection bias, recruitment to Aim 1 was systematic. All patients referred to the above-mentioned cardiology service were screened via folder review (for prevalent cases) and echocardiogram (for all incident and certain prevalent cases). All patients found to have structural CHD and fitting the inclusion and exclusion criteria were invited to participate in Aim 1. PROTEA recruitment: Inclusion and exclusion criteria, participating centers, and recruitment to aims 1–3. GSH, Groote Schuur Hospital; RCWMCH, Red Cross War Memorial Children’s Hospital; TBH, Tygerberg Hospital; HCC, Health Care Centre; CNV, Copy Number Variant analysis; GUCH, Grown-Up Congenital Heart; PDA, patent ductus arteriosus; PFO, patent foramen ovale; PPS, peripheral pulmonary stenosis; RF, Risk Factor; SES, Socio-economic status; TOF, tetralogy of Fallot; WES, Whole Exome Sequencing. Aim 2 and 3 participants are selected from Aim 1 via convenience and purposive sampling, respectively. Additionally, a convenience sample of pediatric participants admitted to the RCWMCH cardiology ward were selected for interview regarding socioeconomic status and maternal perinatal risk factors for CHD. All patients with an echocardiogram-confirmed diagnosis of structural CHD are considered eligible for inclusion in the study. Participants with isolated conduction or functional abnormalities, patent foramen ovale, peripheral pulmonary stenosis or patent ductus arteriosus in premature infants were excluded. The proportion of CHD-subtypes in PROTEA’s pediatric cohort was compared with the proportion of CHD subtypes in two global CHD birth-prevalence studies by van der Linde et al. (4) and Liu et al. (8). Twenty-six CHD-subtypes were selected for comparison. These subtypes were selected to match the ICD 9 and 10 subtype data presented in Liu et al. (8). Van der Linde et al. (4) only present data for the 8 most common CHD-subtypes in their analysis, all of which are included in the 26 subtypes above. Cohort-prevalence ratios were calculated using R (version 4.0.0, R Foundation) (21) and the R-package epiR (version 1.0-14, Stevenson 2020) (22). Contingency tables were created for each CHD subtype and used to calculate prevalence ratios between PROTEA and both Liu et al. (8) and van der Linde et al. (4) independently. The 95% confidence intervals (CI) for the prevalence ratios were calculated using the Wald method, in addition p-values were generated using the chi-square test for independence, p < 0.05 were considered significant. The methods and results of aims 2 and 3 are beyond the scope of this paper and will be presented in future articles (19). All data are stored in the PROTEA application and database. The PROTEA application was developed using FileMaker (Claris International Inc., Santa Clara, CA) and integrates an EHR with a research database. Security features include encryption of data at rest, hierarchical access control and data encryption between client and server. Data integrity is ensured via intelligent prompting, audit logs recording all changes as well as incremental backups to geographically separated, redundant disk arrays.

The PROTEA study is a research project aimed at establishing a comprehensive Congenital Heart Disease (CHD) cohort in southern Africa. The study collects data and samples from patients with structural CHD in order to facilitate research into the epidemiology and genetic determinants of CHD in the region.

Some potential innovations to improve access to maternal health based on the information provided in the description could include:

1. Telemedicine: Implementing telemedicine services to provide remote access to maternal health care, including prenatal check-ups, consultations, and monitoring. This can help overcome geographical barriers and improve access for women in remote areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information for pregnant women. These apps can also facilitate communication with healthcare providers and enable remote monitoring of maternal health parameters.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in underserved areas. These workers can help bridge the gap between communities and healthcare facilities, improving access to care for pregnant women.

4. Maternal health clinics: Establishing dedicated maternal health clinics in areas with limited access to healthcare facilities. These clinics can provide comprehensive prenatal care, including screenings, vaccinations, and counseling, to ensure the well-being of pregnant women and their babies.

5. Health education campaigns: Conducting targeted health education campaigns to raise awareness about the importance of maternal health and the available resources. These campaigns can help empower women with knowledge and encourage them to seek timely and appropriate care during pregnancy.

6. Mobile clinics: Setting up mobile clinics that can travel to remote and underserved areas to provide maternal health services. These clinics can offer prenatal check-ups, screenings, and basic treatments, bringing healthcare closer to communities that lack access to traditional healthcare facilities.

It’s important to note that these are general recommendations based on the information provided. The specific context and needs of the region should be taken into consideration when implementing any innovation to improve access to maternal health.
AI Innovations Description
The PROTEA study is a research project aimed at establishing a comprehensive Congenital Heart Disease (CHD) cohort in southern Africa. The project collects data and samples from patients with CHD to facilitate research into the epidemiology and genetic determinants of CHD in the region.

The study recruited participants from seven hospitals in the Western Cape Province of South Africa over a two-year period. Patients with structural CHD were eligible for inclusion in the study. The initial cohort consisted of 1,473 participants, with a median age of 1.9 years. The most common CHD subtypes in the cohort were ventricular septal defect (VSD), atrial septal defect (ASD), patent ductus arteriosus, atrioventricular septal defect (AVSD), and tetralogy of Fallot.

The study compared the prevalence of CHD subtypes in the PROTEA cohort with global prevalence estimates from two birth-prevalence studies. It found that certain mild CHD subtypes were less common in the PROTEA cohort compared to global estimates, while certain severe subtypes were more common. These findings may indicate under-diagnosis of mild to moderate CHD and differences in CHD management and outcomes in the region.

The PROTEA study aims to contribute to robust CHD epidemiological research in southern Africa. It collects data and samples from both children and adults with CHD and utilizes a secure application and database for storage and analysis. The study’s results and future articles will provide further insights into the genetic and epidemiological aspects of CHD in the region.
AI Innovations Methodology
The provided text describes the PROTEA project, which aims to establish a Congenital Heart Disease (CHD) cohort in southern Africa for research purposes. The project collects data from patients with structural CHD and includes a biorepository for DNA samples. The methodology used in the project involves systematic recruitment of patients from various hospitals and clinics in the Western Cape Province of South Africa. Patients are screened through folder review and echocardiogram, and those fitting the inclusion and exclusion criteria are invited to participate. The proportion of CHD subtypes in the PROTEA cohort is compared with global prevalence estimates using contingency tables and statistical analysis. The data collected is stored in a secure application and database.

To improve access to maternal health, some potential recommendations could include:

1. Increasing awareness and education: Implementing programs to educate women and communities about the importance of maternal health, including prenatal care, nutrition, and hygiene practices.

2. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals to ensure that maternal health services are available and accessible to all women, especially in rural and underserved areas.

3. Improving transportation: Enhancing transportation systems to facilitate access to healthcare facilities, particularly for women living in remote areas.

4. Providing financial support: Implementing policies or programs that provide financial assistance to pregnant women, such as maternity benefits or subsidies for prenatal care and delivery services.

5. Telemedicine and mobile health solutions: Utilizing technology to provide remote access to maternal health services, including teleconsultations, remote monitoring, and health information dissemination through mobile applications.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of prenatal visits, percentage of deliveries attended by skilled birth attendants, or maternal mortality rates.

2. Baseline data collection: Gather data on the current status of maternal health access, including the identified indicators, before implementing any recommendations. This can be done through surveys, interviews, or analysis of existing data sources.

3. Implement recommendations: Introduce the recommended interventions or policies to improve access to maternal health services. This could be done gradually or in specific target areas.

4. Monitor and collect data: Continuously monitor the implementation of the recommendations and collect data on the identified indicators. This can be done through surveys, interviews, or analysis of existing data sources.

5. Analyze and compare data: Compare the data collected after implementing the recommendations with the baseline data to assess the impact of the interventions. Statistical analysis can be used to determine if there are significant improvements in the identified indicators.

6. Evaluate and adjust: Evaluate the effectiveness of the recommendations and make adjustments as necessary. This may involve refining the interventions, expanding their reach, or addressing any unforeseen challenges.

By following this methodology, it is possible to simulate the impact of recommendations on improving access to maternal health and make informed decisions on how to further enhance maternal healthcare services.

Statistics:
Citations: 2
Authors: 23
Identifiers:
DOI: 10.3389/fped.2021.763060
Research Areas:
Genetics and Genomics, Health System and Policy, Maternal and Child Health, Noncommunicable Diseases, Social Determinants
Study Countries:
Multi-Countries, Namibia, South Africa
Study Design:
Cohort Study, Cross Sectional Study
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