Tissue-Based Predictors of Impaired Right Ventricular Strain in Coronary Artery Disease: A Multicenter Stress Perfusion Study.

BACKGROUND: Right ventricular (RV) dysfunction is known to impact prognosis, but its determinants in coronary artery disease are poorly understood. Stress cardiac magnetic resonance (CMR) has been used to assess ischemia and infarction in relation to the left ventricle (LV); the impact of myocardial tissue properties on RV function is unknown. METHODS: Vasodilator stress CMR was performed in patients with known coronary artery disease at 7 sites between May 2005 and October 2018. Myocardial infarction was identified on late gadolinium enhancement-CMR, and infarct transmurality was graded on a per-segment basis. Ischemia was assessed on stress CMR based on first-pass perfusion and localized by using segment partitions corresponding to cine and late gadolinium enhancement analyses. RV function was evaluated by CMR-feature tracking for primary analysis with a global longitudinal strain threshold of 20% used to define impaired RV strain (RVIS); secondary functional analysis via RV ejection fraction was also performed. RESULTS: A total of 2604 patients were studied, among whom RVIS was present in 461 patients (18%). The presence and magnitude of RVIS were strongly associated with LV dysfunction, irrespective of whether measured by LV ejection fraction or wall motion score (P<0.001 for all). Regarding tissue substrate, regions of ischemic and dysfunctional myocardium (ie, hibernating myocardium) and infarct size were each independently associated with RVIS (both P<0.001). During follow-up (median, 4.62 [interquartile range, 2.15-7.67] years), 555 deaths (21%) occurred. Kaplan-Meier analysis for patients stratified by presence and magnitude of RV dysfunction by global longitudinal strain and RV ejection fraction each demonstrated strong prognostic utility for all-cause mortality (P<0.001). RVIS conferred increased mortality risk (hazard ratio, 1.35 [95% CI, 1.11-1.66]; P=0.003) even after controlling for LV function, infarction, and ischemia. CONCLUSIONS: RVIS in patients with known coronary artery disease is associated with potentially reversible LV processes, including LV functional impairment due to ischemic and predominantly viable myocardium, which confers increased mortality risk independent of LV function and tissue substrate.

The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSION: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

Association of Adverse Clinical Outcomes With Peri-Infarct Ischemia Detected by Stress Cardiac Magnetic Imaging.

BACKGROUND: Early invasive revascularization guided by moderate to severe ischemia did not improve outcomes over medical therapy alone, underlying the need to identify high-risk patients for a more effective invasive referral. CMR could determine the myocardial extent and matching locations of ischemia and infarction. OBJECTIVES: This study sought to investigate if CMR peri-infarct ischemia is associated with adverse events incremental to known risk markers. METHODS: Consecutive patients were included in an expanded cohort of the multicenter SPINS (Stress CMR Perfusion Imaging in the United States) study. Peri-infarct ischemia was defined by the presence of any ischemic segment neighboring an infarcted segment by late gadolinium enhancement imaging. Primary outcome events included acute myocardial infarction and cardiovascular death, whereas secondary events included any primary events, hospitalization for unstable angina, heart failure hospitalization, and late coronary artery bypass surgery. RESULTS: Among 3,915 patients (age: 61.0 ± 12.9 years; 54.7% male), ischemia, infarct, and peri-infarct ischemia were present in 752 (19.2%), 1,123 (28.8%), and 382 (9.8%) patients, respectively. At 5.3 years (Q1-Q3: 3.9-7.2 years) of median follow-up, primary and secondary events occurred in 406 (10.4%) and 745 (19.0%) patients, respectively. Peri-infarct ischemia was the strongest multivariable predictor for primary and secondary events (HRadjusted: 1.72 [95% CI: 1.23-2.41] and 1.71 [95% CI: 1.32-2.20], respectively; both P < 0.001), adjusted for clinical risk factors, left ventricular function, ischemia extent, and infarct size. The presence of peri-infarct ischemia portended to a >6-fold increased annualized primary event rate compared to those with no infarct and ischemia (6.5% vs 0.9%). CONCLUSIONS: Peri-infarct ischemia is a novel and robust prognostic marker of adverse cardiovascular events.

Risk prediction of heart diseases in patients with breast cancer: A deep learning approach with longitudinal electronic health records data.

Accurately predicting heart disease risks in patients with breast cancer is crucial for clinical decision support and patient safety. This study developed and evaluated predictive models for six heart diseases using real-world electronic health records (EHRs) data. We incorporated a trainable decay mechanism to handle missing values in the long short-term memory (LSTM) model, creating LSTM-D models to predict heart disease risk based on longitudinal EHRs data. Additionally, we deployed NLP methods to extract breast cancer phenotypes from clinical texts, integrating unstructured and structured data to enhance predictions. Our LSTM-D models outperformed baseline models in predicting congestive heart failure, coronary artery disease, cardiomyopathy, myocardial infarction, transient ischemic attack, and aortic regurgitation, with AUC scores ranging from 0.7189 to 0.9548. Observation windows of 12-24 months were found optimal for model performance. This research advances precise, personalized care strategies, enabling early intervention and improved management of cardiovascular risks in breast cancer survivors.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.

OBJECTIVE: This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables. MATERIALS AND METHODS: We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period. RESULTS: The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors. DISCUSSION: The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness. CONCLUSION: This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND METHODS: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI. RESULTS: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.

Right Ventricular Function on Cardiovascular Magnetic Resonance Imaging and Long-Term Outcomes in Stable Heart Transplant Recipients.

BACKGROUND: In heart transplant recipients, right ventricular (RV) dysfunction may occur for a variety of reasons. Whether RV dysfunction in the stable phase after heart transplantation is associated with long-term adverse outcomes is unknown. We aimed to determine the long-term prognostic significance of RV dysfunction identified on cardiovascular magnetic resonance imaging (CMR) at least 1 year after heart transplantation. METHODS: In consecutive heart transplant recipients who underwent CMR for surveillance, we assessed 2 CMR measures of RV function: RV ejection fraction and RV global longitudinal strain (RVGLS). We investigated associations between RV dysfunction and a composite end point of death or major adverse cardiac events, including retransplantation, nonfatal myocardial infarction, coronary revascularization, and heart failure hospitalization. RESULTS: A total of 257 heart transplant recipients (median age, 59 years; 75% men) who had CMR at a median of 4.3 years after heart transplantation were included. Over a median follow-up of 4.4 years after the CMR, 108 recipients experienced death or major adverse cardiac events. In a multivariable Cox regression analysis adjusted for age, time since transplantation, indication for transplantation, cardiac allograft vasculopathy, history of rejection, and CMR covariates, RV ejection fraction was not associated with the composite end point, but RVGLS was independently associated with the composite end point with a hazard ratio of 1.08 per 1% worsening in RVGLS ([95% CI, 1.00-1.17]; P=0.046). RVGLS provided incremental prognostic value over other variables in multivariable analyses. The association was replicated in subgroups of recipients with normal RV ejection fraction and recipients with late gadolinium enhancement imaging. A similar association was seen with a composite end point of cardiovascular death or major adverse cardiac events. CONCLUSIONS: CMR feature tracking-derived RVGLS assessed at least 1 year after heart transplantation was independently associated with the long-term risk of death or major adverse cardiac events. Future studies should investigate its role in guiding clinical decision-making in heart transplant recipients.

Cardiac magnetic resonance in histologically proven eosinophilic myocarditis.

BACKGROUND: Eosinophilic myocarditis (EM) is a life-threatening acute heart disease. Cardiac magnetic resonance (CMR) excels in the assessment of myocardial diseases but CMR studies of EM are limited. We aimed to describe CMR findings in histologically proven EM. METHODS: Patients with histologically proven EM seen at an academic center from 2000 through 2020 were identified. Of the 28 patients ascertained, 15 had undergone CMR for diagnosis and constitute our study cohort. RESULTS: The patients, aged 51 ± 17 years, presented with fever (53%), dyspnea (47%), chest pain (53%), heart block (20%), and blood eosinophilia (60%). On CMR, all 15 patients had myocardial edema with 10 of them (67%) having abnormally high left ventricular (LV) mass as well. LV ejection fraction measured < 50% in 11 patients (73%) and < 30% in 2 (13%), but only 6 (40%) had dilated LV size. Eight patients (53%) had pericardial effusion. LV late gadolinium enhancement (LGE) was found in all but one patient (13/14; 93%). LGE was always multifocal and subendocardial but could involve any myocardial layer. Patients with necrotizing EM by histopathology (n = 6) had higher LGE mass (32.1 ± 16.6% vs 14.5 ± 7.7%, p = 0.050) and more LV segments with LGE (15 ± 2 vs 9 ± 3 out of 17, p = 0.003) than patients (n = 9) without myocyte necrosis. Two patients had LV thrombosis accompanying widespread subendocardial LGE. CONCLUSIONS: In EM, CMR shows myocardial edema and LGE that is typically subendocardial but can involve any myocardial layer. The left ventricle is often non-dilated with moderate-to-severe systolic dysfunction. Pericardial effusion is common. Necrotizing EM presents with extensive myocardial LGE on CMR.