Cardiac microstructural alterations in immune-inflammatory myocardial disease: a retrospective case-control study.

BACKGROUND: Immune-inflammatory myocardial disease contributes to multiple chronic cardiac processes, but access to non-invasive screening is limited. We have previously developed a method of echocardiographic texture analysis, called the high-spectrum signal intensity coefficient (HS-SIC) which assesses myocardial microstructure and previously associated with myocardial fibrosis. We aimed to determine whether this echocardiographic texture analysis of cardiac microstructure can identify inflammatory cardiac disease in the clinical setting. METHODS: We conducted a retrospective case-control study of 318 patients with distinct clinical myocardial pathologies and 20 healthy controls. Populations included myocarditis, atypical chest pain/palpitations, STEMI, severe aortic stenosis, acute COVID infection, amyloidosis, and cardiac transplantation with acute rejection, without current rejection but with prior rejection, and with no history of rejection. We assessed the HS-SIC's ability to differentiate between a broader diversity of clinical groups and healthy controls. We used Kruskal-Wallis tests to compare HS-SIC values measured in each of the clinical populations with those in the healthy control group and compared HS-SIC values between the subgroups of cardiac transplantation rejection status. RESULTS: For the total sample of N = 338, the mean age was 49.6 ± 20.9 years and 50% were women. The mean ± standard error of the mean of HS-SIC were: 0.668 ± 0.074 for controls, 0.552 ± 0.049 for atypical chest pain/palpitations, 0.425 ± 0.058 for myocarditis, 0.881 ± 0.129 for STEMI, 1.116 ± 0.196 for severe aortic stenosis, 0.904 ± 0.116 for acute COVID, and 0.698 ± 0.103 for amyloidosis. Among cardiac transplant recipients, HS-SIC values were 0.478 ± 0.999 for active rejection, 0.594 ± 0.091 for prior rejection, and 1.191 ± 0.442 for never rejection. We observed significant differences in HS-SIC between controls and myocarditis (P = 0.0014), active rejection (P = 0.0076), and atypical chest pain or palpitations (P = 0.0014); as well as between transplant patients with active rejection and those without current or prior rejection (P = 0.031). CONCLUSIONS: An echocardiographic method can be used to characterize tissue signatures of microstructural changes across a spectrum of cardiac disease including immune-inflammatory conditions.

Sex-specific differences in the genetic and environmental effects on cardiac phenotypic variation assessed by echocardiography.

The drivers of sexual dimorphism in heart failure phenotypes are currently poorly understood. Divergent phenotypes may result from differences in heritability and genetic versus environmental influences on the interplay of cardiac structure and function. To assess sex-specific heritability and genetic versus environmental contributions to variation and inter-relations between echocardiography traits in a large community-based cohort. We studied Framingham Heart Study participants of Offspring Cohort examination 8 (2005-2008) and Third Generation Cohort examination 1 (2002-2005). Five cardiac traits and six functional traits were measured using standardized echocardiography. Sequential Oligogenic Linkage Analysis Routines (SOLAR) software was used to perform singular and bivariate quantitative trait linkage analysis. In our study of 5674 participants (age 49 ± 15 years; 54% women), heritability for all traits was significant for both men and women. There were no significant differences in traits between men and women. Within inter-trait correlations, there were two genetic, and four environmental trait pairs with sex-based differences. Within both significant genetic trait pairs, men had a positive relation, and women had no significant relation. We observed significant sex-based differences in inter-trait genetic and environmental correlations between cardiac structure and function. These findings highlight potential pathways of sex-based divergent heart failure phenotypes.

Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure.

OBJECTIVE: Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS: We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS: We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS: Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.

Subclinical hepatic fibrosis is associated with coronary microvascular dysfunction by myocardial perfusion reserve index: a retrospective cohort study.

The heart-liver axis is of growing importance. Previous studies have identified independent association of liver dysfunction and fibrosis with adverse cardiac outcomes, but mechanistic pathways remain uncertain. We sought to understand the relations between the degree of hepatic fibrosis identified by the Fibrosis-4 (Fib-4) risk score and comprehensive cardiac MRI (CMR) measures of subclinical cardiac disease. We conducted a retrospective single-center cohort study of patients between 2011 and 2021. We identified consecutive patients who underwent a comprehensive CMR imaging protocol including contrast enhanced with stress/rest perfusion, and lacked pre-existing cardiovascular disease or perfusion abnormalities on CMR. We examined the association of hepatic fibrosis, using the Fib-4 score, with subclinical cardiac disease on CMR while adjusting for cardiometabolic traits. Given known associations of hepatic disease and coronary microvascular dysfunction, we prioritized analyses with the myocardial perfusion reserve index (MPRI), a marker of coronary microvascular function. Of the 66 patients in our study cohort, 54 were female (81%) and the mean age was 53.7 ± 15.3 years. We found that higher Fib-4 was associated with reduction in the MPRI (ß [SE] - 1.12 [0.46], P = 0.02), after adjusting for cardiometabolic risk factors. Importantly, Fib-4 was not significantly associated with any other CMR phenotypes including measures of cardiac remodeling, inflammation, fibrosis, or dysfunction. We found evidence that hepatic fibrosis associated with coronary microvascular dysfunction, in the absence of overt associations with any other subclinical cardiac disease measures. These findings highlight a potentially important precursor pathway leading to development of subsequent heart-liver disease.

Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation.

Purpose of Review: Anatomical segmentation has played a major role within clinical cardiology. Novel techniques through artificial intelligence-based computer vision have revolutionized this process through both automation and novel applications. This review discusses the history and clinical context of cardiac segmentation to provide a framework for a survey of recent manuscripts in artificial intelligence and cardiac segmentation. We aim to clarify for the reader the clinical question of "Why do we segment?" in order to understand the question of "Where is current research and where should be?". Recent Findings: There has been increasing research in cardiac segmentation in recent years. Segmentation models are most frequently based on a U-Net structure. Multiple innovations have been added in terms of pre-processing or connection to analysis pipelines. Cardiac MRI is the most frequently segmented modality, which is due in part to the presence of publically-available, moderately sized, computer vision competition datasets. Further progress in data availability, model explanation, and clinical integration are being pursued. Summary: The task of cardiac anatomical segmentation has experienced massive strides forward within the past five years due to convolutional neural networks. These advances provide a basis for streamlining image analysis, and a foundation for further analysis both by computer and human systems. While technical advances are clear, clinical benefit remains nascent. Novel approaches may improve measurement precision by decreasing inter-reader variability and appear to also have the potential for larger-reaching effects in the future within integrated analysis pipelines.

High-throughput digitization of analog human echocardiography data.

Echocardiographic imaging has been acquired in historical longitudinal cohorts of cardiovascular disease. Many cohorts were established prior to digital recording of echocardiography, and thus have preserved their archival imaging on Video Home System (VHS) tapes. These tapes require large physical storage space, are affected by physical degradation, and cannot be analyzed using modern digital techniques. We have designed and implemented a standardized methodology for digitizing analog data in historical longitudinal cohorts. The methodology creates a pipeline through critical steps of initial review, digitization, anonymization, quality control, and storage. The methodology has been implemented in the Framingham Offspring Study, a community-based epidemiological cohort study with echocardiography performed during serial examinations between 1987 and 1998. We present this method as an accessible pipeline for preserving and repurposing historical imaging data acquired from large cohort studies. The described technique:•Outlines a generalizable pipeline for digitization of analog recordings of echocardiography stored on VHS tapes•Addresses research concerns including quality control, anonymization, and storage•Expresses the authors' individual experience regarding observed image quality, training needs, and potential limitations to help readers understand the costs and benefits of this method.