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BACKGROUND Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. METHODS/DESIGN PROTEUS is a randomised, multicentre, two-armed, non-inferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomised 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. DISCUSSION This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE.

Aims Stress echocardiography is widely used to identify obstructive coronary artery disease (CAD). High accuracy is reported in expert hands but is dependent on operator training and image quality. The EVAREST study provides UK-wide data to evaluate real-world performance and accuracy of stress echocardiography. Methods and results Participants undergoing stress echocardiography for CAD were recruited from 31 hospitals. Participants were followed up through health records which underwent expert adjudication. Cardiac outcome was defined as anatomically or functionally significant stenosis on angiography, revascularization, medical management of ischaemia, acute coronary syndrome, or cardiac-related death within 6 months. A total of 5131 patients (55% male) participated with a median age of 65 years (interquartile range 57–74). 72.9% of studies used dobutamine and 68.5% were contrast studies. Inducible ischaemia was present in 19.3% of scans. Sensitivity and specificity for prediction of a cardiac outcome were 95.4% and 96.0%, respectively, with an accuracy of 95.9%. Sub-group analysis revealed high levels of predictive accuracy across a wide range of patient and protocol sub-groups, with the presence of a resting regional wall motion abnormalitiy significantly reducing the performance of both dobutamine (P Conclusion Stress echocardiography has high accuracy across UK-based hospitals and thus indicates stress echocardiography is being delivered effectively in real-world practice, reinforcing its role as a first-line investigation in the assessment of patients with stable chest pain.

Aims To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and −17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69–0.87) to 0.83 (0.74–0.91) or 0.84 (0.75–0.92), respectively. Conclusion AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.

Supplemental Digital Content is available in the text., Hypertensive pregnancy is associated with increased maternal cardiovascular risk in later life. A range of cardiovascular adaptations after pregnancy have been reported to partly explain this risk. We used multimodality imaging to identify whether, by midlife, any pregnancy-associated phenotypes were still identifiable and to what extent they could be explained by blood pressure. Participants were identified by review of hospital maternity records 5 to 10 years after pregnancy and invited to a single visit for detailed cardiovascular imaging phenotyping. One hundred seventy-three women (age, 42±5 years, 70 after normotensive and 103 after hypertensive pregnancy) underwent magnetic resonance imaging of the heart and aorta, echocardiography, and vascular assessment, including capillaroscopy. Women with a history of hypertensive pregnancy had a distinct cardiac geometry with higher left ventricular mass index (49.9±7.1 versus 46.0±6.5 g/m2; P=0.001) and ejection fraction (65.6±5.4% versus 63.7±4.3%; P=0.03) but lower global longitudinal strain (−18.31±4.46% versus −19.94±3.59%; P=0.02). Left atrial volume index was also increased (40.4±9.2 versus 37.3±7.3 mL/m2; P=0.03) and E:A reduced (1.34±0.35 versus 1.52±0.45; P=0.003). Aortic compliance (0.240±0.053 versus 0.258±0.063; P=0.046) and functional capillary density (105.4±23.0 versus 115.2±20.9 capillaries/mm2; P=0.01) were reduced. Only differences in functional capillary density, left ventricular mass, and atrial volume indices remained after adjustment for blood pressure (P

Transthoracic echocardiography is the leading cardiac imaging modality for patients admitted with COVID-19, a condition of high short-term mortality. The aim of this study was to test the hypothesis that artificial intelligence (AI)-based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert.Patients admitted to 13 hospitals for acute COVID-19 who underwent transthoracic echocardiography were included. Left ventricular ejection fraction (LVEF) and left ventricular longitudinal strain (LVLS) were obtained manually by multiple expert readers and by automated AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared.In total, 870 patients were enrolled. The mortality rate was 27.4% after a mean follow-up period of 230 ± 115 days. AI analysis had lower variability than manual analysis for both LVEF (P = .003) and LVLS (P = .005). AI-derived LVEF and LVLS were predictors of mortality in univariable and multivariable regression analysis (odds ratio, 0.974 [95% CI, 0.956-0.991; P = .003] for LVEF; odds ratio, 1.060 [95% CI, 1.019-1.105; P = .004] for LVLS), but LVEF and LVLS obtained by manual analysis were not. Direct comparison of the predictive value of AI versus manual measurements of LVEF and LVLS showed that AI was significantly better (P = .005 and P = .003, respectively). In addition, AI-derived LVEF and LVLS had more significant and stronger correlations to other objective biomarkers of acute disease than manual reads.AI-based analysis of LVEF and LVLS had similar feasibility as manual analysis, minimized variability, and consequently increased the statistical power to predict mortality. AI-based, but not manual, analyses were a significant predictor of in-hospital and follow-up mortality.

Background Assessment of LVEF and myocardial deformation with GLS has shown promise in predicting CAD, which may add prognostic information for patients undergoing SE. However, selection bias precludes an accurate assessment of routine clinical SE workflow due to the exclusion of poor image quality and contrast enhanced studies. We hypothesise that an artificial intelligence (AI) pipeline capable of fully automated contouring of the left ventricle and GLS analysis of both non-contrast and contrast SE images is feasible and can predict CAD. Purpose The aim of this study was to evaluate the prediction of obstructive coronary artery disease (CAD) from fully automated left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) measures in a large multicentre population of patients undergoing stress echocardiography (SE). Methods 500 patients from five medical centres undergoing SE for the clinical evaluation of ischaemic heart disease were included in this study. LVEF and GLS was automatically calculated using AI in non-contrast and contrast images at rest and peak stress. The primary endpoint was CAD assessed using invasive coronary angiography. Results Patients with significant CAD demonstrated significantly reduced LVEF and GLS at rest and peak stress (all p Conclusions Fully automated LVEF and GLS in non-contrast and contrast SE images is feasible and independently augment the prediction of obstructive CAD above and beyond traditional SE indexes. Funding Acknowledgement Type of funding sources: None.

The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation.Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests.An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S.How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study.Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93.Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

Funding Acknowledgements Type of funding sources: None. Background Segmentation of cardiac structures in echocardiography is a pre-requisite for accurately assessing cardiac morphology and function. Manual or semi-automated segmentation are both routinely used in clinical practice, although these can be time-consuming, and can introduce high inter- and intra- operator variability resulting in decreased reproducibility. Effective contouring with no manual input has proven to be challenging due to variations in image quality, image noise, motion during the acquisition and the lack of a well-defined geometry. Methods This work proposes a coordinate regression method for automated left ventricle (LV) segmentation, presented in Figure 1 (a). The proposed method is based on a modified U-net architecture that outputs the likelihood of coordinates of landmark points. The obtained likelihood heatmaps are converted to 2D coordinates using a differentiable spatial to numerical transform. The model was trained and validated on UK multisite data (1383 subjects) comprising apical 2 and 4 chamber views for both contrast and non-contrast echocardiographic images. The Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) echocardiographic image segmentation database was used to assess the performance of the proposed method acting as data from a new clinical site. The CAMUS dataset comprises apical 2 and 4 chamber views acquired from 500 patients with manually annotated cardiac structures for end-diastole and end-systole frames. The original CAMUS dataset was split into training (450 patients) and testing (50 patients), with manual contours being available only for the training dataset. Therefore, we used the CAMUS training dataset to both test and improve our model, by using a random sample of 100 studies as an independent testing dataset and the remaining 350 studies were used for retraining the initial model to improve performance for this dataset. Results The results obtained on the testing images are presented in Figure 1 (b). When the model was trained using no CAMUS data for the LV segmentation, a mean Dice coefficient of 0.890 and a median of 0.911 was obtained. Including 350 studies with the original 1383 UK dataset and retraining the same model improved the average Dice coefficient to 0.930 and the median to 0.939. The CAMUS dataset authors reported the best average Dice coefficient of 0.924 on the 50 CAMUS testing images, therefore the proposed points regression method introduces a promising alternative to mask-based segmentation models. Conclusions In conclusion, the auto-contouring framework has proven to be effective in terms of its performance and ability to generalise to new data. Furthermore, this work highlights the importance of both evaluating model performance on data from new clinical sites and also enhancing model performance. Abstract Figure.

Background Stress echocardiography is a widely used, non-invasive imaging modality used to identify prognostically significant coronary artery disease. High levels of accuracy have been reported, however this is highly dependent on operator training and image quality. There are currently limited data available on the accuracy of stress echo in every day clinical practice. Purpose The EVAREST study links stress echo clinics in 30 NHS Hospital Trusts in England and therefore provides data to evaluate the performance and diagnostic accuracy of stress echo in “real-world” clinical practice. Methods Analysis was performed on the first 7415 patients recruited prospectively between 2015 and January 2020. Participants are included if they have undergone stress echo to investigate for ischaemic heart disease. Data is collected on medical history and stress echo performance. Participants are followed up for 12 months through health records and patient phone call, with all outcomes undergoing expert adjudication. A positive cardiac outcome is defined as initiation of anti-anginal medications, ≥70% stenosis on coronary angiography, revascularisation, confirmed acute coronary syndrome or cardiac-related death. Results Mean age of patients undergoing stress echo is 65±12.3 years and 56% are male. Average BMI is 28.9±5.6 kg/m2. 71.4% undergo dobutamine stress (DSE) and 28.4% exercise with Conclusion EVAREST provides unprecedented, large-scale information on the “real world” use and accuracy of stress echo across different healthcare settings in the UK, demonstrating performance consistent with best practice. Ongoing data collection will be used to evaluate sources of heterogeneity in the predictive accuracy of stress echo and identify optimal approaches to further improve performance. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd., Lantheus Ltd.

Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of >99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

Background Stress echocardiography has become established as the most widely applied non-invasive imaging test for diagnosis of coronary artery disease within the UK. However, stress echocardiography has been substantially qualitative, rather than quantitative, based on visual wall motion assessment. For the first time, we have identified and validated quantitative descriptors of cardiac geometry and motion, extracted from ultrasound images acquired using contrast agents in an automated way. Purpose To establish whether these novel imaging features can be generated in an automated, quantifiable and reproducible way from images acquired with perfluoropropane contrast, as well as investigating how these extracted measures compare to those extracted from sulphur hexafluoride contrast and non-contrast studies. Methods 100 patients who received perfluoropropane contrast during their stress echocardiogram were recruited. Their stress echocardiography images were processed through a deep learning algorithm. Novel feature values were recorded and a subset of 10 studies were repeated. The automated measures of global longitudinal strain (GLS) and ejection fraction (EF) extracted from these images were compared to values previously extracted from sulphur hexafluoride contrast and non-contrast images using the same software. Results A full set of 31 novel imaging features were successfully extracted from 79 studies acquired using the perfluoropropane contrast agent with a dropout rate of 14% (n=92, 8 incomplete image sets). Repeated analysis in a subset of 10 perfluoropropane cases demonstrated excellent reproducibility of the extracted feature values (R2=1). Automated values of GLS and EF, at both rest (GLS = −16.4±4.8%, EF = 63±13%) and stress stages (GLS = −17.7±5.8%, EF = 68±11%), were extracted from 83 perfluoropropane studies, with a dropout rate of 16% (n=99, fewer incomplete sets as short axis view not required). The ranges of GLS and EF measures extracted from the perfluoropropane images were comparable to the other contrast studies (n=222) (Rest GLS = −16.8±5.8%, Rest EF = 63±10%; Stress GLS = −19.1±6.7%, Stress EF = 71±9%) and non-contrast studies (n=86) (Rest GLS = −15.7±5.3%, Rest EF = 57±10%; Stress GLS = −17.3±6.4%, Stress EF = 61±14%). Conclusions Novel features and clinically relevant measures were extracted from images acquired using perfluoropropane contrast for the first time in a fully automated and reproducible way using a deep learning algorithm. The analysis failure rate and generated measures are comparable to those extracted from images using other commonly used sulphur hexafluoride contrast agents and non-contrast stress echocardiography studies. These findings demonstrate that deep learning algorithms can be used for automated quantitative analysis of stress echocardiograms acquired using various contrast agents and in non-contrast studies to improve stress echocardiography practice. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Lantheus Medical Imaging, Inc.

In this paper we consider automated myocardial wall motion assessment by quantifying a cardiac bull's eye map derived from principal strain analysis. The objective is to learn a classification model that can classify between normal and abnormal wall motions. A traditional hand-crafted feature approach based on pixel intensities is compared with a deep learning framework, where a Convolutional Neural Network (CNN) automatically learns features. Experiments on a 3D Dobutamine Stress Echo (DSE) dataset with normal and abnormal wall motions shows that both hand-crafted approaches yield comparable accuracy: Random Forests (72.1%), Support Vector Machines (70.5%), and CNN at a slightly higher accuracy (75.0%) and a lower training computational cost.

Echocardiography remains the imaging modality of choice for the early detection and diagnosis of cardiovascular disease because it is portable, non-invasive, radiation-free, and allows real time im...

Background Heart rate variability (HRV) has emerged as a predictor of later cardiac risk. This study tested whether pregnancy complications that may have long-term offspring cardiac sequelae are associated with differences in HRV at birth, and whether these HRV differences identify abnormal cardiovascular development in the postnatal period. Methods Ninety-eight sleeping neonates had 5-min electrocardiogram recordings at birth. Standard time and frequency domain parameters were calculated and related to cardiovascular measures at birth and 3 months of age. Results Increasing prematurity, but not maternal hypertension or growth restriction, was associated with decreased HRV at birth, as demonstrated by a lower root mean square of the difference between adjacent NN intervals (rMSSD) and low (LF) and high-frequency power (HF), with decreasing gestational age (p Conclusions Altered cardiac autonomic function at birth relates to prematurity rather than other pregnancy complications and does not predict cardiovascular developmental patterns during the first 3 months post birth. Long-term studies will be needed to understand the relevance to cardiovascular risk.

Background Adults born very preterm have increased cardiac mass and reduced function. We investigated whether a hypertrophic phenomenon occurs in later preterm infants and when this occurs during early development. Methods Cardiac ultrasound was performed on 392 infants (33% preterm at mean gestation 34±2 weeks). Scans were performed during fetal development in 137, at birth and 3 months of postnatal age in 200, and during both fetal and postnatal development in 55. Cardiac morphology and function was quantified and computational models created to identify geometric changes. Results At birth, preterm offspring had reduced cardiac mass and volume relative to body size with a more globular heart. By 3 months, ventricular shape had normalized but both left and right ventricular mass relative to body size were significantly higher than expected for postmenstrual age (left 57.8±41.9 vs. 27.3±29.4%, P

Introduction: The risk of heart failure is increased in patients with type 2 diabetes mellitus (type 2 DM) independently of risk factors such as coronary artery disease. The causes of diabetic cardiomyopathy are multifactorial and not fully elucidated. 3D echocardiography deformation imaging provides a non-invasive assessment of the structure and function of the left ventricle (LV) and has been shown to improve risk evaluation in patients with congestive cardiac failure (CCF). Objective: The aim of this study was to assess the earliest subclinical manifestations of diabetic cardiomyopathy using deformation imaging. Method: 20 patients (mean age 58.6±1.8) with early-onset, uncomplicated type 2 DM, 20 healthy volunteers (mean age 53.3±2.8) and 10 patients with advanced CCF (NYHA class III+IV) (mean age 69.2±3.0) were studied. Strain dispersion was assessed by calculating the standard deviation of time to peak longitudinal strain across 16 segments of the LV. Results: Diabetic patients were matched with the controls for age and gender. Strain dispersion was increased in patients with type 2 DM when compared to healthy controls (p= 0.001) and global longitudinal strain was decreased (p=0.001). Strain dispersion in type 2 DM and CCF cohorts yielded similar values and were not significantly different (46.5±6.4 vs 52.2±7.9, respectively p=0.3). Conclusion: Strain dispersion is increased in uncomplicated type 2 DM patients with a short duration of disease. 3D echocardiographic deformation imaging is a sensitive non-invasive tool that could be helpful in the assessment of subclinical cardiac involvement in patients with type 2 DM.

Introduction Patients with type 2 diabetes mellitus (T2DM) are at increased risk of heart failure and early identification subtle changes may allow preventive interventions. Altered ventricular myocardial and fluid mechanics are early manifestations of cardiac dysfunction and can be quantified by novel echocardiography techniques. Objective We aimed to assess whether echocardiographic measures of myocardial and fluid mechanics are altered in patients with T2DM. Methods 34 patients (mean age 54.52 ± 8.3) with uncomplicated T2DM and 22 healthy volunteers (mean age 52.50 ± 12.7) were studied. Echocardiography (Philips ie33) was used to quantify vortex formation time (VFT), a measure of fluid dynamics related to left ventricular geometry and early diastolic mitral flow. 3D datasets were used to quantify axial strain (TomTec) and principal strain (S1), secondary strain (S2) and variability of strain within the myocardium via a dedicated MatLab program. Results VFT was significantly reduced (p = 0.00003) in patients with T2DM and correlated with the ratio of S1 to S2 (r = 0.45 p = 0.002), consistent with an impact of vortex formation on systolic efficiency (Table 1). T2DM was associated with altered myocardial systolic mechanics with reduced S2 (p = 0.004) and increased variability of S1 (p = 0.009) and S2 (p = 0.004) (Figure 1). Interobserver variability for measures of VFT, S1 and S2 was low compared with the traditional axial deformation measures. Conclusion Subclinical changes in myocardial mechanical and ventricular fluid dynamics are identifiable with echocardiography in patients with T2DM. Furthermore, a reduction in VFT is associated with impaired myocardial mechanics.

Background Preterm birth leads to a switch from fetal to postnatal circulation before completion of in utero cardiac development. We hypothesised that those born preterm would have a unique adult cardiac phenotype and that this would develop during postnatal life. Methods 234 young adults underwent cardiovascular magnetic resonance, of which 102 had been followed prospectively since preterm birth. We then studied 200 infants, longitudinally from birth to three months of age using echocardiography, of which 111 were born preterm. To define cardiac shape and function in adult and neonatal life we developed in-house 3D computational approaches and applied novel deformation imaging tools. Results born preterm had increased LV and RV mass index ( P .001 and P .001), proportional to severity of prematurity ( P = 0 .03 and P .001), and reduced chamber volumes and length (P P = 0 .006), as were LV systolic and diastolic myocardial deformation. At birth, preterm-born infants had similar LV mass index, appropriate for gestational body size, as term-born infants ( P = 0 .13). However, by three months a greater percentage increase ( P = 0 .01) resulted in significantly greater LV mass index in those born preterm (P = 0 .04). Interestingly, their LV end-diastolic volume index was already smaller at birth ( P = 0 .003) and this difference persisted at three months ( P = 0 .05). Conclusions Birth is associated with a unique adult cardiac phenotype. Disproportionate increases in mass occur during postnatal life alongside persistent shape changes. Postnatal life may be a critical window for modification of cardiac development, relevant to the adult cardiovasculature.