A review of the pivotal role of cardiac MRI in mitral valve regurgitation

"Circumnavigation": A Worthy Descriptor to Depict Torrential Mitral Regurgitation and a Reverence to Ferdinand Magellan

"Circumnavigation" is never used as a verb in cardiology. Hypertrophic obstructive cardiomyopathy is a complex entity that often involves the mitral valve leading to eccentric mitral regurgitation. Utilizing multimodality imaging, assessing the severity of mitral regurgitation, and phenotyping the type of myocardial hypertrophy are achievable with certainty. We describe a case of a 42-year-old male who presented with palpitations and was ultimately diagnosed with hypertrophic obstructive cardiomyopathy with a mitral regurgitation. The torrential mitral regurgitation jet was so severe that it was "circumnavigating" the left atrium. In addition, we also draw historical parallelism with Magellan's heroic "circumnavigation" of the globe as we celebrate 500 years of his journey. Furthermore, we also describe the multimodality assessment of hypertrophic obstructive cardiomyopathy utilizing transthoracic echocardiography and cardiac magnetic resonance imaging. We discuss the challenges in quantifying such severe mitral regurgitation with any individual imaging modality.

Rationale and clinical applications of 4D flow cardiovascular magnetic resonance in assessment of valvular heart disease: a comprehensive review

BACKGROUND

Accurate evaluation of valvular pathology is crucial in the timing of surgical intervention. Whilst transthoracic echocardiography is widely available and routinely used in the assessment of valvular heart disease, it is bound by several limitations. Although cardiovascular magnetic resonance (CMR) imaging can overcome many of the challenges encountered by echocardiography, it also has a number of limitations.

MAIN TEXT

4D Flow CMR is a novel technique, which allows time-resolved, 3-dimensional imaging. It enables visualisation and direct quantification of flow and peak velocities of all valves simultaneously in one simple acquisition, without any geometric assumptions. It also has the unique ability to measure advanced haemodynamic parameters such as turbulent kinetic energy, viscous energy loss rate and wall shear stress, which may add further diagnostic and prognostic information. Although 4D Flow CMR acquisition can take 5-10 min, emerging acceleration techniques can significantly reduce scan times, making 4D Flow CMR applicable in contemporary clinical practice.

CONCLUSION

4D Flow CMR is an emerging CMR technique, which has the potential to become the new reference-standard method for the evaluation of valvular lesions. In this review, we describe the clinical applications, advantages and disadvantages of 4D Flow CMR in the assessment of valvular heart disease.

Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information

Background

Heart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information.

Methods

Standardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization.

Results

The mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59–4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group.

Conclusion

In this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.