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Patient-specific computational fluid dynamics (CFD) studies on coarctation of the aorta (CoA) in resource-constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography is considered a suitable velocity acquisition modality due to its low cost and safety. This study aims to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach to obtain boundary conditions (BCs) from Doppler echocardiography images for haemodynamic modelling using CFD. Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. The key feature of the approach is the use of ML models to calibrate the inlet and outlet BCs of the CFD model. In the ML model, patient heart rate served as the crucial input variable due to its temporal variation across the measured vessels. ANSYS Fluent was used for the CFD component of the study, whilst the Scikit-learn Python library was used for the ML component. We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations was compared to the measured maximum coarctation velocity obtained from the patient whose geometry was used within the study. Of the 5 ML models used to obtain BCs, the top model was within 5% of the maximum measured coarctation velocity. The framework demonstrated that it was capable of taking into account variations in the patient’s heart rate between measurements. Therefore, it allowed for the calculation of BCs that were physiologically realistic when the measurements across each vessel were scaled to the same heart rate while providing a reasonably accurate solution.

Background and ObjectivesThe Western Cape public pediatric cardiac service is under-resourced. COVID-19 regulations are likely to have long-term effects on patient care but may provide insight into service capacity requirements. As such, we aimed to quantify the impact of COVID-19 regulations on this service.MethodsAn uncontrolled retrospective pre-post study of all presenting patients over two, one-year periods; the pre-COVID-19 period (01/03/2019–29/02/2020) and the peri-COVID-19 period (01/03/2020–28/02/2021).ResultsAdmissions decreased by 39% (624 to 378) and cardiac surgeries decreased by 29% (293 to 208) in the peri-COVID-19 period, with an increase in urgent cases (PR:5.99, 95%CI:3.58–10.02, p

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.

Congenital heart disease (CHD) is the most common birth defect globally and coarctation of the aorta (CoA) is one of the commoner CHD conditions, affecting around 1/1800 live births. CoA is considered a CHD of critical severity. Unfortunately, the prognosis for a child born in a low and lower-middle income country (LLMICs) with CoA is far worse than in a high-income country. Reduced diagnostic and interventional capacities of specialists in these regions lead to delayed diagnosis and treatment, which in turn lead to more cases presenting at an advanced stage. Computational fluid dynamics (CFD) is an important tool in this context since it can provide additional diagnostic data in the form of hemodynamic parameters. It also provides an in silico framework, both to test potential procedures and to assess the risk of further complications arising post-repair. Although this concept is already in practice in high income countries, the clinical infrastructure in LLMICs can be sparse, and access to advanced imaging modalities such as phase contrast magnetic resonance imaging (PC-MRI) is limited, if not impossible. In this study, a pipeline was developed in conjunction with clinicians at the Red Cross War Memorial Children’s Hospital, Cape Town and was applied to perform a patient-specific CFD study of CoA. The pipeline uses data acquired from CT angiography and Doppler transthoracic echocardiography (both much more clinically available than MRI in LLMICs), while segmentation is conducted via SimVascular and simulation is realized using OpenFOAM. The reduction in cost through use of open-source software and the use of broadly available imaging modalities makes the methodology clinically feasible and repeatable within resource-constrained environments. The project identifies the key role of Doppler echocardiography, despite its disadvantages, as an intrinsic component of the pipeline if it is to be used routinely in LLMICs.