Optimal use of echocardiography in valvular heart disease evaluation

Superiority of 3D planimetry over pressure half-time method for the assessment of mitral valve area after percutaneous edge-to-edge mitral repair

INTRODUCTION AND OBJECTIVES

There is limited evidence to identify the most accurate method for measuring the mitral valve area (MVA) after percutaneous edge-to-edge mitral repair. Our objective was to evaluate the optimal method in this context and its correlation with the mean transmitral gradient.

METHODS

A registry of patients undergoing percutaneous mitral repair was conducted, analyzing different methods of measuring MVA and their correlation with the mean gradient.

RESULTS

We analyzed data from 167 patients. The mean age was 76±10.3 years, 54% were men, and 46% were women. Etiology was degenerative in 45%, functional in 39%, and mixed in 16%. Postclip MVA measurements were 1.89±0.60 cm2 using pressure half-time (PHT), 2.87±0.83 cm2 using 3D planimetry, and the mean gradient was 3±1.19mmHg. MVA using 3D planimetry showed a stronger correlation with the mean gradient (r=0.46, P<.001) than MVA obtained by PHT (r=0.19, P=.048). Interobserver agreement was also higher with 3D planimetry than with PHT (intraclass correlation coefficient of 0.90 vs 0.81 and variation coefficient of 9.6 vs 19.7%, respectively).

CONCLUSIONS

Our study demonstrates that the PHT method significantly underestimates MVA after clip implantation compared with direct measurement using transesophageal 3D planimetry. The latter method also correlates better with postimplantation gradients and has less interobserver variability. These results suggest that 3D planimetry is a more appropriate method for assessing postclip mitral stenosis.

Clinical characteristics and management of coexistent cardiomyopathy in patients with bicuspid aortic valve

BACKGROUND

Bicuspid aortic valve (BAV) is the most common congenital heart disease. However, the prevalence, clinical characteristics, and current management of BAV associated with inherited cardiomyopathy, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and left ventricular noncompaction (LVNC) have not been well described.

METHODS

Consecutive patients diagnosed with BAV at a large tertiary cardiovascular referral center between 2009 and 2018 were retrospectively assessed for HCM, DCM, and LVNC based on clinical and echocardiographic criteria. Patients with coexistent conditions were investigated further.

RESULTS

Of 3533 patients with BAV screened, 57 (1.6%) had concomitant cardiomyopathy. BAV was combined with HCM in 30 of these patients, with DCM in 19, and with LVNC in eight. Forty-six patients (80.7%) were male, and the mean age at first diagnosis was 47 years for BAV with HCM, 49 years for BAV with DCM, and 35 years for BAV with LVNC. Heart failure and aortic valve dysfunction were common in these patients, and the prevalence of coexisting aortopathy was 43.3%, 26.3% and 25.0%, respectively, for BAV with HCM, DCM and LVNC. During the index hospitalization, 24 of the 57 patients (42.1%) underwent surgery, 16 (28%) underwent aortic valve and/or aortic surgery, and 16 of the 30 patients with HCM had a Morrow procedure. There were no deaths or other major adverse cardiovascular events.

CONCLUSIONS

The prevalence of inherited cardiomyopathy was higher in our patients with BAV than in the general population. Aortopathy and heart failure were common, with almost half of patients requiring surgery at diagnosis.

Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability

The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a "clinical level" study. Almost 39% of the articles achieved a "robust candidate" and as many as 61% a "proof of concept" status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.

Cardiomyopathy in Children: Classification and Diagnosis: A Scientific Statement From the American Heart Association

In this scientific statement from the American Heart Association, experts in the field of cardiomyopathy (heart muscle disease) in children address 2 issues: the most current understanding of the causes of cardiomyopathy in children and the optimal approaches to diagnosis cardiomyopathy in children. Cardiomyopathies result in some of the worst pediatric cardiology outcomes; nearly 40% of children who present with symptomatic cardiomyopathy undergo a heart transplantation or die within the first 2 years after diagnosis. The percentage of children with cardiomyopathy who underwent a heart transplantation has not declined over the past 10 years, and cardiomyopathy remains the leading cause of transplantation for children >1 year of age. Studies from the National Heart, Lung, and Blood Institute-funded Pediatric Cardiomyopathy Registry have shown that causes are established in very few children with cardiomyopathy, yet genetic causes are likely to be present in most. The incidence of pediatric cardiomyopathy is ≈1 per 100 000 children. This is comparable to the incidence of such childhood cancers as lymphoma, Wilms tumor, and neuroblastoma. However, the published research and scientific conferences focused on pediatric cardiomyopathy are sparcer than for those cancers. The aim of the statement is to focus on the diagnosis and classification of cardiomyopathy. We anticipate that this report will help shape the future research priorities in this set of diseases to achieve earlier diagnosis, improved clinical outcomes, and better quality of life for these children and their families.

Mitral leaflet separation to evaluate the severity of mitral stenosis: Validation of the index by transesophageal three-dimensional echocardiography

BACKGROUND

Determining severity of mitral stenosis (MS) by planimetry of mitral valve orifice area (MVA) has been a challenging issue in clinical practice, especially for less experienced cardiologists. Mitral leaflet separation (MLS) has shown a good correlation with MVA measurements. However, it has never been validated against multiplane 3DTEE planimetry (MVA_3D ). We aimed to evaluate the accuracy of MLS index (MLSI_2D ) in predicting MS severity.

METHODS

We prospectively enrolled 144 patients with MS who underwent clinically indicated 2DTTE and 3DTEE. MLSI_2D was yield by averaging the maximal leaflet tip distance in diastole, in parasternal long-axis and apical four-chamber views. MVA_3D was used as the reference method.

RESULTS

MLSI_2D showed an excellent discriminatory ability between different grades of MS (P < .001). There was a significant positive correlation between MLSI_2D and MVA_3D (r = .93, P < .001) irrespective of concurrent mitral regurgitation (r = .94, P < .001) and/or atrial fibrillation (r = .92, P < .001). By receiver operating characteristic (ROC) curves, MLSI_2D  ≤ 8.6 mm showed 100% sensitivity and 76% specificity for very severe MS. MLSI_2D  ≥ 11.2 mm determined progressive MS with 100% sensitivity and 82% specificity. The study population was then divided into a derivation group and a validation group. A regression equation for MVA by MLSI_2D was derived in first group. Then, the MVA was calculated by this equation in validation group and was not significantly different from MVA_3D .

CONCLUSION

MLSI_2D showed an excellent ability to assess MS severity and correlates well with planimetered MVA measured by 3DTEE.

Early Diagnosis of Cardiovascular Diseases in Workers: Role of Standard and Advanced Echocardiography

Cardiovascular disease (CVD) still remains the main cause of morbidity and mortality and consequently early diagnosis is of paramount importance. Working conditions can be regarded as an additional risk factor for CVD. Since different aspects of the job may affect vascular health differently, it is important to consider occupation from multiple perspectives to better assess occupational impacts on health. Standard echocardiography has several targets in the cardiac population, as the assessment of myocardial performance, valvular and/or congenital heart disease, and hemodynamics. Three-dimensional echocardiography gained attention recently as a viable clinical tool in assessing left ventricular (LV) and right ventricular (RV) function, volume, and shape. Two-dimensional (2DSTE) and, more recently, three-dimensional speckle tracking echocardiography (3DSTE) have also emerged as methods for detection of global and regional myocardial dysfunction in various cardiovascular diseases and applied to the diagnosis of subtle LV and RV dysfunction. Although these novel echocardiographic imaging modalities have advanced our understanding of LV and RV mechanics, overlapping patterns often show challenges that limit their clinical utility. This review will describe the current state of standard and advanced echocardiography in early detection (secondary prevention) of CVD and address future directions for this potentially important diagnostic strategy.

Echocardiographic assessment of long-term hemodynamic characteristics of mechanical mitral valve prostheses with different mitral valvular diseases

Mitral stenosis (MS) and mitral insufficiency (MI) have different pre-operative hemodynamic characteristics. However, it is unclear if there are differences in long-term echocardiographic characteristics of MS and MI patients after mechanical mitral valve replacement. This study is to compare long-term echocardiographic results of mechanical mitral valve prostheses between MS and MI patients. From January 2003 to January 2009, a total of 199 consecutive patients were recruited in this study. Patients were classified as group MS (n = 123) and MI (n = 76) according to the manifestation of mitral valvular disease. The mean age for patients was 50.1 ± 10.5 years and follow-up time was 7.2 ± 2.0 years. The MS after operation were more likely to experience atrial fibrillation (p = 0.002). The New York Heart Association (NYHA) class in MI showed a greater improvement (p = 0.006) than in MS. The left ventricular end-diastolic dimension (LVEDD) (p = 0.010) and stroke volume (SV) (p = 0.000) in MI were still larger than that in MS patients. These differences did not disappear with time after operation. The long-term echocardiographic results of mechanical mitral valve prostheses between MS and MI patients are significantly different. Over a long-term follow up, MI patients still have a larger LVEDD and SV than MS, and associated with a greater improvement of NYHA class.