Value CMR: Towards a Comprehensive, Rapid, Cost-Effective Cardiovascular Magnetic Resonance Imaging

Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. Data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted.

Can artificial intelligence lower the global sudden cardiac death rate? A narrative review

PURPOSE OF REVIEW

WHO defines SCD as sudden unexpected death either within 1 h of symptom onset (witnessed) or within 24 h of having been observed alive and symptom-free (unwitnessed). Sudden cardiac arrest is a major cause of mortality worldwide, with survival to hospital discharge for hospital cardiac arrest and in-hospital cardiac arrest being only 9.3 % and 21.2 %, respectively, despite treatment highlighting the importance of effectively predicting and preventing cardiac arrest. This literature review aims to explore the role and application of AI (Artificial Intelligence) in predicting and preventing sudden cardiac arrest.

MATERIAL AND METHODS

Eligible studies were searched from PubMed and Web of Science. The inclusion criteria were fulfilled if sudden cardiac death prediction and prevention, artificial intelligence, machine learning, and deep learning were included.

CONCLUSIONS

Artificial intelligence, machine learning, and deep learning have shown remarkable prospects in SCA risk stratification, which can improve the survival rate from SCA. Nonetheless, they have not been adequately trained and tested, necessitating further studies with explainable techniques, larger sample sizes, external validation, more diverse patient samples, multimodal tools, ethics, and bias mitigation to unlock their full potential.

Comparative evaluation of left atrial size in healthy cats measured by two-dimensional echocardiography and cardiovascular MRI

OBJECTIVES

This study aimed to assess left atrial (LA) size in healthy cats using cardiovascular MRI (cMRI) and to compare this with LA size assessed by two-dimensional echocardiography. The hypothesis was that cMRI would accurately determine LA size in domestic cats.

METHODS

A prospective comparative study was performed. Six healthy cats were selected for the study. Standard two-dimensional echocardiography was performed with and without general anaesthesia. cMRI was conducted under general anaesthesia. A comprehensive analysis of LA mass and function measurements was performed to determine the consistency and correlation of LA size and function indicators between two-dimensional echocardiography and cMRI.

RESULTS

Our study found that intraobserver variability for cMRI measurements was lower than that for two-dimensional echocardiography. Compared with cMRI, echocardiography under anaesthesia significantly overestimated maximal LA volume (LAVmax_2D, P <0.01) and significantly underestimated minimal LA volume (LAVmin_2D, P <0.01). The LAVmin measured by two-dimensional echocardiography exhibited the highest consistency (intraclass correlation coefficient = 0.857) and correlation (R = 0.75, P <0.01) with LAVmin measured by cMRI. The linear regression equation was LAVmin_ cMRI = 0.891 × LAVmin_2D + 0.304.

CONCLUSIONS AND RELEVANCE

cMRI represents a reproducible method for assessing LA mass in domestic cats. This study underscored the importance of echocardiography in veterinary cardiology, and the LAVmin measured by two-dimensional echocardiography may reflect the true LAVmin.

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

BACKGROUND

Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals.

METHOD

We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients.

RESULTS

The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients.

CONCLUSION

The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

Comparison of Stress-Rest and Stress-LGE Analysis Strategy in Patients Undergoing Stress Perfusion Cardiovascular Magnetic Resonance

BACKGROUND

Stress perfusion cardiovascular magnetic resonance can be performed without rest perfusion for the quantification of ischemia burden. However, the optimal method of analysis is uncertain.

METHODS

We identified 666 patients from CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) with complete stress perfusion, rest perfusion, late gadolinium enhancement (LGE), and quantitative coronary angiography data. For each segment of the 16-segment model, perfusion was visually graded during stress and rest imaging, with infarct transmurality assessed from LGE imaging. In the stress-LGE analysis, a segment was defined as ischemic if it had a subendocardial perfusion defect with no infarction. Rest perfusion was not used in this analysis. We compared the diagnostic accuracy of stress-LGE analysis against quantitative coronary angiography and the stress-rest method validated in the original CE-MARC analysis. The diagnostic accuracy of the stress-LGE method was evaluated with different thresholds of infarct transmurality used to define whether an infarcted segment had peri-infarct ischemia.

RESULTS

The optimal stress-LGE analysis classified all segments with a stress perfusion defect as ischemic unless they had >75% infarct transmurality (area under the curve, 0.843; sensitivity, 75.6%; specificity, 93.1%; P<0.001). This analysis method has superior diagnostic accuracy to the stress-rest method (area under the curve, 0.834; sensitivity, 73.6%; specificity, 93.1%; P<0.001, P value for difference=0.02). Patients were followed-up for median 6.5 years for major adverse cardiovascular events, with the presence of inducible ischemia by either the stress-LGE or stress-rest analysis being similar and strongly predictive (hazard ratio, 2.65; P<0.001, for both).

CONCLUSIONS

In this analysis of CE-MARC, the optimum definition of inducible ischemia was the presence of a stress-induced perfusion defect without transmural infarction. This definition improved the diagnostic accuracy compared with the stress-rest analysis validated in the original study. The absence of ischemia by either analysis strategy conferred a favorable long-term prognosis.

The Impact of COVID-19 on the Cardiovascular Health of Emerging Adults Aged 18-25: Findings From a Scoping Review

There is limited knowledge regarding the cardiovascular impact of coronavirus disease 2019 (COVID-19) on emerging adults aged 18-25, a group that disproportionately contracts COVID-19. To guide future cardiovascular disease (CVD) research, policy, and practice, a scoping review was conducted to: (i) examine the impact of the COVID-19 pandemic on the cardiovascular health of emerging adults; and (ii) identify strategies to screen for and manage COVID-19-related cardiovascular complications in this age group. A comprehensive search strategy was applied to several academic databases and grey literature sources. An updated search yielded 6738 articles, 147 of which were extracted and synthesized. Reports identified COVID-19-associated cardiac abnormalities, vascular alterations, and multisystem inflammatory syndrome in emerging adults; based on data from student-athlete samples, prevalence estimates of myocarditis and cardiac abnormalities were 0.5%-3% and 0%-7%, respectively. Obesity, hypertension, CVD, congenital heart disease, and marginalization are potential risk factors for severe COVID-19, related cardiovascular complications, and mortality in this age group. As a screening modality for COVID-19-associated cardiac involvement, it is recommended that cardiac magnetic resonance imaging be indicated by a positive cardiac history and/or abnormal "triad" testing (cardiac troponin, electrocardiogram, and transthoracic echocardiogram) to improve diagnostic utility. To foster long-term cardiovascular health among emerging adults, cardiorespiratory fitness, health literacy and education, and telehealth accessibility should be priorities of health policy and clinical practice. Ultimately, surveillance data from the broader emerging adult population will be crucial to assess the long-term cardiovascular impact of both COVID-19 infection and vaccination, guide screening and management protocols, and inform CVD prevention efforts.

The Merits, Limitations, and Future Directions of Cost-Effectiveness Analysis in Cardiac MRI with a Focus on Coronary Artery Disease: A Literature Review

Cardiac magnetic resonance (CMR) imaging has a wide range of clinical applications with a high degree of accuracy for many myocardial pathologies. Recent literature has shown great utility of CMR in diagnosing many diseases, often changing the course of treatment. Despite this, it is often underutilized possibly due to perceived costs, limiting patient factors and comfort, and longer examination periods compared to other imaging modalities. In this regard, we conducted a literature review using keywords “Cost-Effectiveness” and “Cardiac MRI” and selected articles from the PubMed MEDLINE database that met our inclusion and exclusion criteria to examine the cost-effectiveness of CMR. Our search result yielded 17 articles included in our review. We found that CMR can be cost-effective in quality-adjusted life years (QALYs) in select patient populations with various cardiac pathologies. Specifically, the use of CMR in coronary artery disease (CAD) patients with a pretest probability below a certain threshold may be more cost-effective compared to patients with a higher pretest probability, although its use can be limited based on geographic location, professional society guidelines, and differing reimbursement patterns. In addition, a stepwise combination of different imaging modalities, with conjunction of AHA/ACC guidelines can further enhance the cost-effectiveness of CMR.

Diastolic Cardiac Function by MRI-Imaging Capabilities and Clinical Applications

Most cardiac studies focus on evaluating left ventricular (LV) systolic function. However, the assessment of diastolic cardiac function is becoming more appreciated, especially with the increasing prevalence of pathologies associated with diastolic dysfunction like heart failure with preserved ejection fraction (HFpEF). Diastolic dysfunction is an indication of abnormal mechanical properties of the myocardium, characterized by slow or delayed myocardial relaxation, abnormal LV distensibility, and/or impaired LV filling. Diastolic dysfunction has been shown to be associated with age and other cardiovascular risk factors such as hypertension and diabetes mellitus. In this context, cardiac magnetic resonance imaging (MRI) has the capability for differentiating between normal and abnormal myocardial relaxation patterns, and therefore offers the prospect of early detection of diastolic dysfunction. Although diastolic cardiac function can be assessed from the ratio between early and atrial filling peaks (E/A ratio), measuring different parameters of heart contractility during diastole allows for evaluating spatial and temporal patterns of cardiac function with the potential for illustrating subtle changes related to age, gender, or other differences among different patient populations. In this article, we review different MRI techniques for evaluating diastolic function along with clinical applications and findings in different heart diseases.