Sudden death mechanisms in nonischemic cardiomyopathies: Insights gleaned from clinical implantable cardioverter-defibrillator trials

Contemporary trends in cardiac electrophysiology procedures in the United States, and impact of a global pandemic

Background

There are limited data on trends in nationwide cardiac electrophysiology (EP) procedures in the United States before and during the global COVID-19 pandemic.

Objective

We aimed to understand contemporary EP procedural trends and how the COVID-19 pandemic impacted them.

Methods

Trends were obtained from publicly reported Centers for Medicare and Medicaid Services data from 2013 to 2020 (latest available). Rates of catheter-based EP procedures (EP studies and ablations) and cardiac implantable electronic device (CIED) procedures were analyzed. All procedural rates were calculated per 100,000 Medicare beneficiaries (year specific). Procedure physician subspecialty was also reported.

Results

From 2013 to 2019, annual rate of all cardiac EP procedures increased from 817.91 to 1089.68 per 100,000 beneficiaries. Catheter-based EP procedures increased from 323.73 to 675.01, while CIED rates decreased from 494.18 to 414.67. While all ablation procedures increased over time, relative proportion of ablation procedures being pulmonary vein isolation (PVI) increased (9.9% of ablations in 2013, to 18.2% in 2019). In 2020, rates of both catheter-based EP procedures and CIED procedures decreased; however, PVI share of ablation continued to increase in 2020 comprising 25.2% of ablation procedures.

Conclusion

Rates of EP procedures have increased among Medicare beneficiaries, with catheter-based procedures now eclipsing CIEDs. Additionally, a greater proportion of catheter-based EP procedures are PVI, but they still represent a minority of all ablations. In 2020, rates of EP procedures were attenuated, yet the proportion of PVI ablations increased to over one-fourth of ablation procedures. These data have important implications for the EP workforce.

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

BACKGROUND

Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms.

OBJECTIVE

The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML.

METHODS

To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death.

RESULTS

With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients.

CONCLUSIONS

Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.

Role of Circular RNAs in the Pathogenesis of Cardiovascular Disease

Circular RNAs (circRNAs) are single-strand covalently closed circular noncoding RNAs that are endogenous transcripts generated from linear precursor mRNA through a backsplicing mechanism. With the development of high-throughput sequencing technology, a number of circRNAs have been identified and proved to play key roles in various pathophysiological processes, such as metabolic diseases, cancers, and cardiovascular diseases. An increasing number of studies have shown that circRNAs are widely expressed in cardiac tissues and play important roles in the development of multiple cardiovascular diseases. Here, we review the current understanding of circRNA biogenesis and functions and the roles of circRNAs in cardiovascular diseases. We also highlight the molecular mechanisms underlying the role of circRNAs in the pathogenesis of cardiovascular diseases. A better understanding of the biological function of circRNAs in cardiovascular diseases will be helpful for the development of effective biomarkers for the diagnosis and treatment of these diseases.

Circular noncoding RNAs as potential therapies and circulating biomarkers for cardiovascular diseases

Recent advancements in genome-wide analyses and RNA-sequencing technologies led to the discovery of small noncoding RNAs, such as microRNAs (miRs), as well as both linear long noncoding RNAs (lncRNAs) and circular long noncoding RNAs (circRNAs). The importance of miRs and lncRNAs in the treatment, prognosis and diagnosis of cardiovascular diseases (CVDs) has been extensively reported. We also previously reviewed their implications in therapies and as biomarkers for CVDs. More recently, circRNAs have also emerged as important regulators in CVDs. CircRNAs are circular genome products that are generated by back splicing of specific regions of pre-messenger RNAs (pre-mRNAs). Growing interest in circRNAs led to the discovery of a wide array of their pathophysiological functions. CircRNAs have been shown to be key regulators of CVDs such as myocardial infarction, atherosclerosis, cardiomyopathy and cardiac fibrosis. Accordingly, circRNAs have been recently proposed as potential therapeutic targets and biomarkers for CVDs. In this review, we summarize the current state of the literature on circRNAs, starting with their biogenesis and global mechanisms of actions. We then provide a synopsis of their involvement in various CVDs. Lastly, we emphasize the great potential of circRNAs as biomarkers for the early detection of CVDs, and discuss several patents and recent papers that highlight the utilization of circRNAs as promising biomarkers.