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

A computer vision model for the identification and scoring of calcium in aortic valve stenosis: a single-center experience

Background

Echocardiography is widely used to assess aortic stenosis (AS) but can yield inconsistent results, leading to uncertainty about AS severity and the need for further diagnostics. This retrospective study aimed to evaluate a novel echocardiography-based marker, the signal intensity coefficient (SIC), for its potential in accurately identifying and quantifying calcium in AS, enhancing noninvasive diagnostic methods.

Methods

Between May 2022 and October 2023, 112 cases of AS that were previously considered severe by echocardiography were retrospectively evaluated, as well as a group of 50 cases of mild or moderate AS, both at the Eastern Slovak Institute of Cardiovascular Diseases in Kosice, Slovakia. Utilizing ImageJ software, we quantified the SIC based on ultrasonic signal intensity distribution at the aortic valve's interface. Pixel intensity histograms were generated to measure the SIC, and it was compared with echocardiographic variables. To account for variations in brightness due to differing acquisition settings in echocardiography images (where the highest intensity corresponds to calcium), adaptive image binarization has been implemented. Subsequently, the region of interest (ROI) containing calcium was interactively selected and extracted. This process enables the calculation of a calcium pixel count, representing the spatial quantity of calcium. This study employed multivariate logistic regression using backward elimination and stepwise techniques. Receiver operating characteristic (ROC) curves were utilized to assess the model's performance in predicting AS severity and to determine the optimal cut-off point.

Results

The SIC emerged as a significant predictor of AS severity, with an odds ratio (OR) of 0.021 [95% confidence interval (CI): 0.004-0.295, P=0.008]. Incorporating SIC into a model alongside standard echocardiographic parameters notably enhanced the C-statistic/ROC area from 0.7023 to 0.8083 (P=0.01).

Conclusions

The SIC, serving as an additional echocardiography-based marker, shows promise in enhancing AS severity detection.

Cellular and molecular mechanisms driving cardiac tissue fibrosis: On the precipice of personalized and precision medicine

Cardiac fibrosis is a significant contributor to heart failure, a condition that continues to affect a growing number of patients worldwide. Various cardiovascular comorbidities can exacerbate cardiac fibrosis. While fibroblasts are believed to be the primary cell type underlying fibrosis, recent and emerging data suggest that other cell types can also potentiate or expedite fibrotic processes. Over the past few decades, clinicians have developed therapeutics that can blunt the development and progression of cardiac fibrosis. While these strategies have yielded positive results, overall clinical outcomes for patients suffering from heart failure continue to be dire. Herein, we overview the molecular and cellular mechanisms underlying cardiac tissue fibrosis. To do so, we establish the known mechanisms that drive fibrosis in the heart, outline the diagnostic tools available, and summarize the treatment options used in contemporary clinical practice. Finally, we underscore the critical role the immune microenvironment plays in the pathogenesis of cardiac fibrosis.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Signal intensity coefficient as a detector of aortic stenosis-induced myocardial fibrosis and its correlation to the long term outcome

OBJECTIVE

Despite advanced aortic valve replacement techniques, aortic stenosis (AS)-induced irreversible myocardial fibrosis contributes to poorer outcomes. Therefore, in addition to early diagnosis of AS, detecting myocardial fibrosis is crucial for physicians to determine the timing of surgery. The Signal Intensity Coefficient (SIC) was used to detect subtle myocardial deformation. Hence, we aimed to investigate whether SIC correlated with myocardial dysfunction and fibrosis from both clinical and preclinical perspectives.

METHODS

We collected medical records and echocardiography images, including the SIC of patients who underwent surgical aortic valve replacement (AVR) for AS from 2010 to 2015. The endpoint of the study was mortality. Median follow-up period was 80 months.

RESULTS

Among 109 patients, 15 died due to cardiovascular causes. Although SIC decreased in all patients post-AVR, patients with an SIC ≥0.34 before surgeries presented with a higher probability of cardiovascular death. In contrast, changes in the left ventricular (LV) ejection fraction, LV mass index, and LV volume failed to predict outcomes. Similarly, SIC was obtained in mice undergoing aortic banding and debanding surgery for comparison with the degree of myocardial fibrosis. SIC was continuously elevated after aortic banding and declined gradually after debanding surgery in mice. Debanding surgery indicated the regression of aortic banding-induced myocardial fibrosis.

CONCLUSION

Pre-AVR SIC was associated with the risk of cardiovascular death and reflected the degree of myocardial fibrosis. Further investigations are required to study the clinical application of SIC in patients with AS.

The interplay between sex, time of day, fasting status, and their impact on cardiac mitochondrial structure, function, and dynamics

Mitochondria morphology and function, and their quality control by mitophagy, are essential for heart function. We investigated whether these are influenced by time of the day (TOD), sex, and fed or fasting status, using transmission electron microscopy (EM), mitochondrial electron transport chain (ETC) activity, and mito-QC reporter mice. We observed peak mitochondrial number at ZT8 in the fed state, which was dependent on the intrinsic cardiac circadian clock, as hearts from cardiomyocyte-specific BMAL1 knockout (CBK) mice exhibit different TOD responses. In contrast to mitochondrial number, mitochondrial ETC activities do not fluctuate across TOD, but decrease immediately and significantly in response to fasting. Concurrent with the loss of ETC activities, ETC proteins were decreased with fasting, simultaneous with significant increases of mitophagy, mitochondrial antioxidant protein SOD2, and the fission protein DRP1. Fasting-induced mitophagy was lost in CBK mice, indicating a direct role of BMAL1 in regulating mitophagy. This is the first of its kind report to demonstrate the interactions between sex, fasting, and TOD on cardiac mitochondrial structure, function and mitophagy. These studies provide a foundation for future investigations of mitochondrial functional perturbation in aging and heart diseases.

Collagenase treatment reduces the anisotropy of ultrasonic backscatter in rat myocardium by reducing collagen crosslinks

Dysregulation of collagen deposition, degradation, and crosslinking in the heart occur in response to increased physiological stress. Collagen content has been associated with ultrasonic backscatter (brightness), and we have shown that the anisotropy of backscatter can be used to measure myofiber alignment, that is, variation in the brightness of a left ventricular short-axis ultrasound. This study investigated collagen's role in anisotropy of ultrasonic backscatter; female Sprague-Dawley rat hearts were treated with a collagenase-containing solution, for either 10 or 30 min, or control solution for 30 min. Serial ultrasound images were acquired at 2.5-min intervals throughout collagenase treatment. Ultrasonic backscatter was assessed from anterior and posterior walls, where collagen fibrils are predominately aligned perpendicular to the angle of insonification, and the lateral and septal walls, where collagen is predominately aligned parallel to the angle of insonification. Collagenase digestion reduced backscatter anisotropy within the myocardium. Collagen remains present in the myocardium throughout collagenase treatment, but crosslinking is altered within 10 min. These data suggest that crosslinking of collagen modulates the anisotropy of ultrasonic backscatter. An Anisotropy Index, derived from differences in backscatter from parallel and perpendicularly aligned fibers, may provide a noninvasive index to monitor the progression and state of myocardial fibrosis.