Predicting in-hospital all-cause mortality in heart failure using machine learning.

Background: The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre. Methods: Six supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%. Results: The mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean LVEF was 29 ± 9.2%. The median duration of hospitalisation was 7 days (interquartile range: 4-11), and it did not differ between patients discharged alive and those who died. After a prediction window of 4 years (interquartile range: 2-6), 84 (16.8%) patients died before discharge from the hospital. The area under the receiver operating characteristic curve was 0.82, 0.78, 0.77, 0.76, 0.75, and 0.62 for random forest, logistic regression, support vector machines (SVM), extreme gradient boosting, multilayer perceptron (MLP), and decision trees, and the accuracy during the test phase was 88, 87, 86, 82, 78, and 76% for random forest, MLP, SVM, extreme gradient boosting, decision trees, and logistic regression. The support vector machines were the best performing algorithm, and furosemide, beta-blockers, spironolactone, early diastolic murmur, and a parasternal heave had a positive coefficient with the target feature, whereas coronary artery disease, potassium, oedema grade, ischaemic cardiomyopathy, and right bundle branch block on electrocardiogram had negative coefficients. Conclusion: Despite a small sample size, supervised machine learning algorithms successfully predicted all-cause mortality with modest accuracy. The SVM model will be externally validated using data from multiple cardiology centres in South Africa before developing a uniquely African risk prediction tool that can potentially transform heart failure management through precision medicine.

Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review.

OBJECTIVE: The partnership between humans and machines can enhance clinical decisions accuracy, leading to improved patient outcomes. Despite this, the application of machine learning techniques in the healthcare sector, particularly in guiding heart failure patient management, remains unpopular. This systematic review aims to identify factors restricting the integration of machine learning derived risk scores into clinical practice when treating adults with acute and chronic heart failure. METHODS: Four academic research databases and Google Scholar were searched to identify original research studies where heart failure patient data was used to build models predicting all-cause mortality, cardiac death, all-cause and heart failure-related hospitalization. RESULTS: Thirty studies met the inclusion criteria. The selected studies' sample size ranged between 71 and 716 790 patients, and the median age was 72.1 (interquartile range: 61.1-76.8) years. The minimum and maximum area under the receiver operating characteristic curve (AUC) for models predicting mortality were 0.48 and 0.92, respectively. Models predicting hospitalization had an AUC of 0.47 to 0.84. Nineteen studies (63%) used logistic regression, 53% random forests, and 37% of studies used decision trees to build predictive models. None of the models were built or externally validated using data originating from Africa or the Middle-East. CONCLUSIONS: The variation in the aetiologies of heart failure, limited access to structured health data, distrust in machine learning techniques among clinicians and the modest accuracy of existing predictive models are some of the factors precluding the widespread use of machine learning derived risk calculators.

Machine learning and statistical methods for predicting mortality in heart failure.

Heart failure is a debilitating clinical syndrome associated with increased morbidity, mortality, and frequent hospitalization, leading to increased healthcare budget utilization. Despite the exponential growth in the introduction of pharmacological agents and medical devices that improve survival, many heart failure patients, particularly those with a left ventricular ejection fraction less than 40%, still experience persistent clinical symptoms that lead to an overall decreased quality of life. Clinical risk prediction is one of the strategies that has been implemented for the selection of high-risk patients and for guiding therapy. However, most risk predictive models have not been well-integrated into the clinical setting. This is partly due to inherent limitations, such as creating risk predicting models using static clinical data that does not consider the dynamic nature of heart failure. Another limiting factor preventing clinicians from utilizing risk prediction models is the lack of insight into how predictive models are built. This review article focuses on describing how predictive models for risk-stratification of patients with heart failure are built.

Review of children with Takayasu's arteritis at a Southern African tertiary care centre.

IntroductionTakayasu's arteritis is a rare idiopathic arteritis causing stenosis or aneurysms of the aorta, pulmonary arteries, and their branches. It usually occurs in women, but has been described in children. OBJECTIVE: The objective of this study was to determine the clinical presentation, demographic profile, vascular involvement, origins, management, and outcome of children diagnosed with Takayasu's arteritis at a Southern African tertiary care centre between 1993 and 2015. METHODS: This is a retrospective analysis of all children with Takayasu's arteritis captured on a computerised electronic database during the study period. RESULTS: A total of 55 children were identified. The female:male ratio was 3.2:1, and the mean age was 9.7±3.04 years. Most originated outside the provincial borders of the study centre. The majority presented with hypertension and heart failure. In all, 37 (67%) patients had a cardiomyopathy with a mean fractional shortening of 15±5%. A positive purified protein derivative test was documented in 73%. Abdominal aorta and renal artery stenosis were the predominant angiographic lesions. A total of 23 patients underwent 30 percutaneous interventions of the aorta, pulmonary, and renal arteries: eight stents, 22 balloon angioplasties, and seven had nephrectomies. All patients received empiric tuberculosis treatment, immunosuppressive therapy, and anti-hypertensive agents as required. Overall, there was a significant reduction in systolic blood pressure and improvement in fractional shortening (p<0.05) with all treatments. CONCLUSION: Takayasu's arteritis is more common in girls and frequently manifests with hypertension and heart failure. The abdominal aorta and renal arteries are mostly affected. Immunosuppressive, anti-hypertensive, and vascular intervention therapies improve blood pressure control and cardiac function.

Patient-specific simulations for planning treatment in congenital heart disease.

Patient-specific computational models have been extensively developed over the last decades and applied to investigate a wide range of cardiovascular problems. However, translation of these technologies into clinical applications, such as planning of medical procedures, has been limited to a few single case reports. Hence, the use of patient-specific models is still far from becoming a standard of care in clinical practice. The aim of this study is to describe our experience with a modelling framework that allows patient-specific simulations to be used for prediction of clinical outcomes. A cohort of 12 patients with congenital heart disease who were referred for percutaneous pulmonary valve implantation, stenting of aortic coarctation and surgical repair of double-outlet right ventricle was included in this study. Image data routinely acquired for clinical assessment were post-processed to set up patient-specific models and test device implantation and surgery. Finite-element and computational fluid dynamics analyses were run to assess feasibility of each intervention and provide some guidance. Results showed good agreement between simulations and clinical decision including feasibility, device choice and fluid-dynamic parameters. The promising results of this pilot study support translation of computer simulations as tools for personalization of cardiovascular treatments.

Cardiac Changes and Their Association with Fetuin-A and Fibroblast Growth Factor-23 in Children with Chronic Kidney Disease.

AIMS: In children with chronic kidney disease (CKD), fetuin-A and fibroblast growth factor-23 (FGF-23) have been implicated in the mechanism and progression of several cardiac changes. This study aimed to determine the types and rates of cardiac changes in children with CKD and their association with fetuin-A, FGF-23, and other cardiovascular risk factors (CVRFs). METHODS: This comparative cross-sectional study recruited 88 children (5-18 years): 27 CKD I with a glomerular filtration rate (GFR) >90 mL/min/1.73 m2 and 61 with a GFR of <90 mL/min/1.73 m2 (29 CKD II-IV, 32 CKD V-dialysis). Each patient had a short demographic and clinical history taken along with a physical examination. Blood was taken and sent for routine tests and for fetuin-A and FGF-23 assay. All patients had an echocardiogram to evaluate cardiac structure and function. RESULTS: The distribution of left atrial diameter (LAD) and left ventricular (LV) mass differed significantly (p < 0.05) across the different CKD groups. Abnormal LAD was seen in 10% of patients; LV hypertrophy (LVH) in 27%; LV systolic dysfunction in 6% and diastolic dysfunction in 1 patient. Fetuin-A was the only independent predictor for abnormal LAD; mean arterial pressure was independently associated with concentric LVH, and age and hypoalbuminemia with eccentric LVH. Overall, the dialysis group had the highest rate of cardiac changes and associated risk factors. CONCLUSION: Though not common, the importance of left atrial changes in children with CKD is highlighted along with the need to address modifiable CVRFs such as hypertension and hypoalbuminemia.

Utility of Cardiovascular Magnetic Resonance-Derived Wave Intensity Analysis As a Marker of Ventricular Function in Children with Heart Failure and Normal Ejection Fraction.

OBJECTIVE: This study sought to explore the diagnostic insight of cardiovascular magnetic resonance (CMR)-derived wave intensity analysis to better study systolic dysfunction in young patients with chronic diastolic dysfunction and preserved ejection fraction (EF), comparing it against other echocardiographic and CMR parameters. BACKGROUND: Evaluating systolic and diastolic dysfunctions in children is challenging, and a gold standard method is currently lacking. METHODS: Patients with presumed diastolic dysfunction [n = 18; nine aortic stenosis (AS), five hypertrophic, and four restrictive cardiomyopathies] were compared with age-matched control subjects (n = 18). All patients had no mitral or aortic incompetence, significant AS, or reduced systolic EF. E/A ratio, E/E' ratio, deceleration time, and isovolumetric contraction time were assessed on echocardiography, and indexed left atrial volume (LAVi), acceleration time (AT), ejection time (ET), and wave intensity analyses were calculated from CMR. The latter was performed on CMR phase-contrast flow sequences, defining a ratio of the peaks of the early systolic forward compression wave (FCW) and the end-systolic forward expansion wave (FEW). RESULTS: Significant differences between patients and controls were seen in the E/E' ratio (8.7 ± 4.0 vs. 5.1 ± 1.3, p = 0.001) and FCW/FEW ratio (2.5 ± 1.6 vs. 7.2 ± 4.2 × 10-5 m/s, p < 0.001), as well as-as expected-LAVi (80.7 ± 22.5 vs. 51.0 ± 10.9 mL/m2, p < 0.001). In particular, patients exhibited a lower FCW (2.5 ± 1.6 vs. 7.2 ± 4.2 × 10-5 m/s, p < 0.001) in the face of preserved EF (67 ± 11 vs. 69 ± 5%, p = 0.392), as well as longer isovolumetric contraction time (49 ± 7 vs. 34 ± 7 ms, p < 0.001) and ET/AT (0.35 ± 0.04 vs. 0.27 ± 0.04, p < 0.001). CONCLUSION: This study shows that the wave intensity-derived ratio summarizing systolic and diastolic function could provide insight into ventricular function in children, on top of CMR and echocardiography, and it was here able to identify an element of ventricular dysfunction with preserved EF in a small group of young patients.

Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis.

Diagnosis of ventricular dysfunction in congenital heart disease is more and more based on medical imaging, which allows investigation of abnormal cardiac morphology and correlated abnormal function. Although analysis of 2D images represents the clinical standard, novel tools performing automatic processing of 3D images are becoming available, providing more detailed and comprehensive information than simple 2D morphometry. Among these, statistical shape analysis (SSA) allows a consistent and quantitative description of a population of complex shapes, as a way to detect novel biomarkers, ultimately improving diagnosis and pathology understanding. The aim of this study is to describe the implementation of a SSA method for the investigation of 3D left ventricular shape and motion patterns and to test it on a small sample of 4 congenital repaired aortic stenosis patients and 4 age-matched healthy volunteers to demonstrate its potential. The advantage of this method is the capability of analyzing subject-specific motion patterns separately from the individual morphology, visually and quantitatively, as a way to identify functional abnormalities related to both dynamics and shape. Specifically, we combined 3D, high-resolution whole heart data with 2D, temporal information provided by cine cardiovascular magnetic resonance images, and we used an SSA approach to analyze 3D motion per se. Preliminary results of this pilot study showed that using this method, some differences in end-diastolic and end-systolic ventricular shapes could be captured, but it was not possible to clearly separate the two cohorts based on shape information alone. However, further analyses on ventricular motion allowed to qualitatively identify differences between the two populations. Moreover, by describing shape and motion with a small number of principal components, this method offers a fully automated process to obtain visually intuitive and numerical information on cardiac shape and motion, which could be, once validated on a larger sample size, easily integrated into the clinical workflow. To conclude, in this preliminary work, we have implemented state-of-the-art automatic segmentation and SSA methods, and we have shown how they could improve our understanding of ventricular kinetics by visually and potentially quantitatively highlighting aspects that are usually not picked up by traditional approaches.

Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches.

OBJECTIVE: Today's growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensional patient shape data and results depend heavily on the chosen clustering distance metrics and linkage functions. In this study, we sought to assess clustering classification performance of various distance/linkage combinations and of different types of input data to obtain clinically meaningful shape clusters. METHODS: We present a processing pipeline combining automatic segmentation, statistical shape modeling, and agglomerative hierarchical clustering to automatically subdivide a set of 60 aortic arch anatomical models into healthy controls, two groups affected by congenital heart disease, and their respective subgroups as defined by clinical diagnosis. Results were compared with traditional morphometrics and principal component analysis of shape features. RESULTS: Our pipeline achieved automatic division of input shape data according to primary clinical diagnosis with high F-score (0.902 ± 0.042) and Matthews correlation coefficient (0.851 ± 0.064) using the correlation/weighted distance/linkage combination. Meaningful subgroups within the three patient groups were obtained and benchmark scores for automatic segmentation and classification performance are reported. CONCLUSION: Clustering results vary depending on the distance/linkage combination used to divide the data. Yet, clinically relevant shape clusters and subgroups could be found with high specificity and low misclassification rates. SIGNIFICANCE: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and medical device development in complex disease.

Utility of adenosine stress perfusion CMR to assess paediatric coronary artery disease.

AIMS: Cardiovascular magnetic resonance (CMR), using adenosine stress perfusion and late-gadolinium enhancement (LGE), is becoming the 'gold standard' non-invasive imaging modality in the assessment of adults with coronary artery disease (CAD). However, despite its proved feasibility in paediatric patients, clinical utility has not been demonstrated. Therefore, this study aims to establish the role of adenosine stress perfusion CMR as a screening test in paediatric patients with acquired or congenital CAD. METHODS AND RESULTS: A total of 58 paediatric patients underwent 61 consecutive clinically indicated coronary artery assessments for diagnostic and clinical decision-making purposes. The diagnosis was based on X-ray or computed tomography coronary angiography for anatomy, adenosine stress CMR imaging for myocardial perfusion and LGE for tissue characterization. Two studies were aborted because of unwanted side effects of adenosine stress, thus 59 studies were completed in 56 patients [median age 14.1 years (interquartile range 10.9-16.2)]. When compared with coronary anatomical imaging, adenosine stress perfusion CMR performed as follows: sensitivity 100% (95% confidence interval, CI: 71.6-100%), specificity 98% (95% CI: 86.7-99.9%), positive predictive value (PPV) 92.9% (95% CI: 64.2-99.6%), and negative predictive value 100% (95% CI: 89.9-100%). CONCLUSION: In paediatric CAD, adenosine stress perfusion CMR imaging is adequate as an initial, non-invasive screening test for the identification of significant coronary artery lesions, with anatomical imaging used to confirm the extent of the culprit lesion.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.

BACKGROUND: Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. METHODS: Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. RESULTS: The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. CONCLUSIONS: The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.

Using 4D Cardiovascular Magnetic Resonance Imaging to Validate Computational Fluid Dynamics: A Case Study.

Computational fluid dynamics (CFD) can have a complementary predictive role alongside the exquisite visualization capabilities of 4D cardiovascular magnetic resonance (CMR) imaging. In order to exploit these capabilities (e.g., for decision-making), it is necessary to validate computational models against real world data. In this study, we sought to acquire 4D CMR flow data in a controllable, experimental setup and use these data to validate a corresponding computational model. We applied this paradigm to a case of congenital heart disease, namely, transposition of the great arteries (TGA) repaired with arterial switch operation. For this purpose, a mock circulatory loop compatible with the CMR environment was constructed and two detailed aortic 3D models (i.e., one TGA case and one normal aortic anatomy) were tested under realistic hemodynamic conditions, acquiring 4D CMR flow. The same 3D domains were used for multi-scale CFD simulations, whereby the remainder of the mock circulatory system was appropriately summarized with a lumped parameter network. Boundary conditions of the simulations mirrored those measured in vitro. Results showed a very good quantitative agreement between experimental and computational models in terms of pressure (overall maximum % error = 4.4% aortic pressure in the control anatomy) and flow distribution data (overall maximum % error = 3.6% at the subclavian artery outlet of the TGA model). Very good qualitative agreement could also be appreciated in terms of streamlines, throughout the cardiac cycle. Additionally, velocity vectors in the ascending aorta revealed less symmetrical flow in the TGA model, which also exhibited higher wall shear stress in the anterior ascending aorta.

Performance monitoring of the arterial switch operation: a moving target.

OBJECTIVES: Twenty years ago our institution published an analysis of a cluster of failures associated with the arterial switch operation (ASO). The concept of 'near miss' was explored to detect warning signs of suboptimal performance. The aim of the current study was to review the parameters best suited to monitoring early ASO outcomes in the modern setting and re-examine the concept of near misses as failure equivalents. METHODS: All ASOs performed in our institution between 1983 and 2012 were reviewed. The experience was divided into three eras (1983-92, 1993-2002 and 2003-12). The cumulative sum graphic for sequential monitoring was used for early mortality. The need to re-establish cardiopulmonary bypass (CPB), CPB time >240 min and extracorporeal membrane oxygenation (ECMO) were explored as variables of near misses. RESULTS: The cohort consisted of 606 patients. The 30-day mortality rate was 23% (n = 29) in Era 1, 6% (n = 14) in Era 2 and 1% (n = 3) in Era 3. There were further 4, 8 and 6 deaths between 30 and 90 days in the three eras, respectively. In Era 3, the majority of deaths occurred between 30 and 90 days. In the current era, ECMO and CPB time >240 min as a marker of near miss was associated with an increased risk of death both within 30 days and 90 days after ASO. CONCLUSIONS: The 30-day outcomes of ASO have significantly improved over the last 30 years. As life-saving mechanical support after surgery has been implemented more often, an extended 90-day window of reporting can offer a more realistic outcome indicator of performance.

Ventriculovascular interactions late after atrial and arterial repair of transposition of the great arteries.

OBJECTIVE: The aim of this study was to compare atrial switch and arterial switch operations for the repair of transposition of the great arteries (TGA), assessing cardiac function and ventriculoarterial (VA) coupling based on cardiac magnetic resonance (CMR) data. Using CMR-derived wave intensity analysis, this study provides a noninvasive comparison of the 2 systemic ventricles and evaluates the subclinical hemodynamic burden of these operations. METHODS: Fifty-four patients (18 controls, 18 atrial switches, 18 arterial switches) were studied. Dimensional and functional data, including the ejection fraction (EF) and end-diastolic volume, were derived from CMR, as well as aortic distensibility. Wave intensity was computed from CMR according to a formulation based on changes in area and velocity, and the peaks of the 2 major systolic waves (forward compression wave [FCW], and forward expansion wave [FEW]) were measured as indicators of ventricular function. RESULTS: Both switches exhibited significantly increased end-diastolic volume and enlarged aortic root areas, and atrial switches were also characterized by significantly lower EF. Patients with TGA presented stiffer ascending aortas, particularly those repaired with an arterial switch. Both FCW and FEW were significantly lower in both TGA cohorts than the controls, suggesting compromised VA coupling, likely a result of increased impedance caused by the stiffer ascending aorta. A significant difference between switch type was noticed in terms of the FEW peak, which was lower in the atrial switch group. CONCLUSIONS: These data highlight reduced aortic distensibility and abnormal VA coupling in repaired TGA. An intraoperational difference was noted in terms of EF and peak FEW, likely reflecting the different nature of the subaortic ventricle.

3D morphometric analysis of the arterial switch operation using in vivo MRI data.

The arterial switch operation (ASO) is widely used nowadays as the surgical strategy of choice to repair transposition of the great arteries (TGA). Residual morphological and geometrical abnormalities of the aorta, pulmonary arteries and coronary arteries, however, have not been fully studied in a three-dimensional (3D) domain. These morphometric complications might have implications on long-term outcomes of ASO patients, hence the need to explore them in detail and study them with reference to healthy controls of comparable age and body surface area. These anatomical characteristics were examined using 3D patient-specific anatomical models reconstructed from cardiovascular magnetic resonance (CMR) images of 20 ASO patients (mean age 14.4 ± 2.4 years, 16 males and 4 females) compared with healthy controls (mean age 15.2 ± 2.0 years, 17 males and 3 females). It was found that the aorta, pulmonary arteries and re-implanted coronary arteries of ASO patients were significantly different morphologically and geometrically to those of healthy controls. In particular, the aortic root was dilated, with abnormal 3D angulation and additional acute angulation of the curvature of the aortic arch in the ASO group compared with controls. This could theoretically impinge on aortic flow profiles and physiological stresses, which can act as a primer for the development of early atherosclerotic disease in the ASO population.

Ventriculoarterial coupling in palliated hypoplastic left heart syndrome: Noninvasive assessment of the effects of surgical arch reconstruction and shunt type.

OBJECTIVE: To assess the coupling efficiency in hypoplastic left heart syndrome, considering the effect of surgical arch reconstruction and the shunt type received during the Norwood procedure. METHODS: Ventriculoarterial coupling was assessed before Fontan completion in 32 patients with hypoplastic left heart syndrome (19 modified Blalock-Taussig and 13 Sano shunts at stage 1). Cardiovascular magnetic resonance data were analyzed, deriving functional parameters and 3-dimensional volumes. Dimensional indexes were computed from 3-dimensional data sets as the area ratio of the isthmus to the descending aorta (Risthmus) and the isthmus to surgically enlarged transverse arch (Rarch). Wave intensity was calculated from cardiac magnetic resonance, using the peaks of the forward compression and expansion waves in early and late systole as surrogate indicators of ventriculoarterial coupling. RESULTS: Aortic distensibility (3.6±2.7×10(-3) 1/mm Hg) was not associated with the time elapsed from stage 1 palliation (P=.94), suggesting an early loss of elasticity that did not progress thereafter. Risthmus was 1.0±0.4, and Rarch was 0.3±0.1, indicating the dilated reconstructed arch was the main anatomic feature. The forward compression wave correlated significantly with Rarch (R2=0.23, P=.006) but not with Risthmus (R2<0.01, P=.63). Patients with a reduced ejection fraction exhibited a larger ventricular mass (R2=0.28, P=.003). The Sano shunt patients had a lower ejection fraction (51%±6% vs 57%±6%, P=.02); however, neither the forward compression nor expansion wave varied significantly between shunt type or the other functional parameters. CONCLUSIONS: Ventriculoarterial coupling in operated hypoplastic left heart syndrome was affected by aortic arch size mismatch but not by the type of shunt placed at the Norwood operation.

Aortic arch shape is not associated with hypertensive response to exercise in patients with repaired congenital heart diseases.

BACKGROUND: Aortic arch geometry is linked to abnormal blood pressure (BP) response to maximum exercise. This study aims to quantitatively assess whether aortic arch geometry plays a role in blood pressure (BP) response to exercise. METHODS: 60 age- and BSA-matched subjects--20 post-aortic coarctation (CoA) repair, 20 transposition of great arteries post arterial switch operation (ASO) and 20 healthy controls--had a three-dimensional (3D), whole heart magnetic resonance angiography (MRA) at 1.5 Tesla, 3D geometric reconstructions created from the MRA. All subjects underwent cardiopulmonary exercise test on the same day as MRA using an ergometer cycle with manual BP measurements. Geometric analysis and their correlation with BP at peak exercise were assessed. RESULTS: Arch curvature was similarly acute in both the post-CoA and ASO cases [0.05 ± 0.01 vs. 0.05 ± 0.01 (1/mm/m²); p = 1.0] and significantly different to that of normal healthy controls [0.05 ± 0.01 vs. 0.03 ± 0.01 (1/mm/m²), p < 0.001]. Indexed transverse arch cross sectional area were significantly abnormal in the post-CoA cases compared to the ASO cases (117.8 ± 47.7 vs. 221.3 ± 44.6; p < 0.001) and controls (117.8 ± 47.7 vs. 157.5 ± 27.2 mm²; p = 0.003). BP response to peak exercise did not correlate with arch curvature (r = 0.203, p = 0.120), but showed inverse correlation with indexed minimum cross sectional area of transverse arch and isthmus (r = -0.364, p = 0.004), and ratios of minimum arch area/ descending diameter (r = -0.491, p < 0.001). CONCLUSION: Transverse arch and isthmus hypoplasia, rather than acute arch angulation plays a role in the pathophysiology of BP response to peak exercise following CoA repair.

Trends in pediatric cardiovascular magnetic resonance imaging.

Cardiac magnetic resonance (CMR) imaging has significantly evolved over the last decade, becoming an integral part of the contemporary assessment of both congenital and acquired pediatric heart disease. Recent trends show that there is a growing interest in clinical applications and research in this field. An attempt to discuss the evolving technologies, techniques, and applications of CMR in pediatrics is not complete without understanding the current strengths of the modality. CMR complements readily available echocardiography, in many cases information from CMR can remove the need for invasive angiographic catheterization, and in other cases can be used to augment cardiac catheterisation.

Reduced ascending aorta distensibility relates to adverse ventricular mechanics in patients with hypoplastic left heart syndrome: noninvasive study using wave intensity analysis.

OBJECTIVE: To evaluate the aortic arch elastic properties and ventriculoarterial coupling efficiency in patients with single ventricle physiology, with and without a surgically reconstructed arch. METHODS: We studied 21 children with single ventricle physiology after bidirectional superior cavopulmonary surgery: 10 with hypoplastic left heart syndrome, who underwent surgical arch reconstruction, and 11 with other types of single ventricle physiology but without arch reconstruction. All children underwent pre-Fontan magnetic resonance imaging. No patient exhibited aortic recoarctation. Data on aortic wave speed, aortic distensibility and wave intensity profiles were all extracted from the magnetic resonance imaging studies using an in-house-written plug-in for the Digital Imaging and Communications in Medicine viewer OsiriX. RESULTS: Children with hypoplastic left heart syndrome had significantly greater wave speed (P = .002), and both stiffer (P = .004) and larger (P < .0001) ascending aortas than the patients with a nonreconstructed arch. Aortic distensibility was not influenced by ventricular stroke volume but depended on a combination of increased aortic diameter and abnormal wall mechanical properties. Those with hypoplastic left heart syndrome had a lower peak wave intensity and reduced energy carried by the forward compression and the forward expansion waves, even after correction for stroke volume, suggesting an abnormal systolic and diastolic function. Lower wave energy was associated with an increased aortic diameter. CONCLUSIONS: Using a novel, noninvasive technique based on image analysis, we have demonstrated that aortic arch reconstruction in children with hypoplastic left heart syndrome is associated with reduced aortic distensibility and unfavorable ventricular-vascular coupling compared with those with single ventricle physiology without aortic arch reconstruction.