Citations

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

The incidence of pancreatic neuroendocrine tumors (PNETs) is on the rise primarily due to the increasing use of cross-sectional imaging. Most of these incidentally detected lesions are non-functional PNETs with a small proportion of lesions being hormone-secreting, functional neoplasms. With recent advances in surgical approaches and systemic therapies, the management of PNETs have undergone a paradigm shift towards a more individualized approach. In this manuscript, we review the histologic classification and diagnostic approaches to both functional and non-functional PNETs. Additionally, we detail multidisciplinary approaches and surgical considerations tailored to the tumor’s biology, location, and functionality based on recent evidence. We also discuss the complexities of metastatic disease, exploring liver-directed therapies and the evolving landscape of minimally invasive surgical techniques.

Abstract Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset's dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia (UEA), in addition to a publicly available sepsis dataset and an institutional sepsis dataset from the Mayo Clinic. We employ recall as a metric for evaluation against the baseline algorithm, Dependent Dynamic Time Warping with 1 Nearest Neighbor. Our algorithm outperforms the baseline algorithm for UEA datasets in terms of recall with an average value of 10% across all datasets, and by 38% for an institutional sepsis dataset. For the public sepsis dataset, the performance of our algorithm is comparable to the baseline algorithm.

ABSTRACT Background and hypothesis The MENTOR trial (MEmbranous Nephropathy Trial Of Rituximab) showed that rituximab was noninferior to cyclosporine in inducing complete or partial remission of proteinuria and was superior in maintaining proteinuria remission. However, the cost of rituximab may prohibit first-line use for some patients and health-care payers. Methods A Markov model was used to determine the incremental cost-effectiveness ratio (ICER) of rituximab compared with cyclosporine for the treatment membranous nephropathy from the perspective of a health-care payer with a lifetime time horizon. The model was informed by data from the MENTOR trial where possible; additional parameters including cost and utility inputs were obtained from the literature. Sensitivity analyses were performed to evaluate the impact of reduced-cost biosimilar rituximab. Results Rituximab for the treatment of membranous nephropathy was cost effective (assuming a willingness-to-pay threshold of $50 000 per quality-adjusted life year (QALY) gained; in $US 2021) compared with cyclosporine, with an ICER of $8373/QALY over a lifetime time horizon. The incremental cost of rituximab therapy was $28 007 with an additional 3.34 QALYs compared with cyclosporine. Lower cost of rituximab biosimilars resulted in a more favorable ICER, and in some cases resulted in rituximab being dominant (lower cost and great benefit) compared to cyclosporine. Conclusions Despite the greater cost of rituximab, it may be a cost-effective option for the treatment of membranous nephropathy when compared with cyclosporine. The cost-effectiveness of rituximab is further improved with the use of less expensive biosimilars.

Abstract Multiple myeloma (MM) exhibits significant heterogeneity in its presentation, genetics, and treatment response. Despite therapeutic advances, some patients continue to relapse early (ER, <18‐months) and rapidly cycle through therapies. Myriad prognostic factors have been identified and incorporated into risk stratification models; however, these produce discordant, often three‐tiered outputs that fail to identify many patients destined for ER. Treatment strategies are increasingly focused on disease biology and trials enriched for high‐risk (HR)MM, but consensus on the minimum required testing and a succinct, specific, and clinically meaningful definition for HRMM remains elusive. We review the risk‐factors, definitions, and future directions for HRMM.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

AbstractPulmonary arterial hypertension (PAH) causes pulmonary vascular remodeling, increasing pulmonary vascular resistance (PVR) and leading to right heart failure and death. Matrix stiffening early in the disease promotes remodeling in pulmonary artery smooth muscle cells (PASMCs), contributing to PAH pathogenesis. Our research identified YAP and TAZ as key drivers of the mechanobiological feedback loop in PASMCs, suggesting targeting them could mitigate remodeling. However, YAP/TAZ are ubiquitously expressed and carry out diverse functions, necessitating a cell‐specific approach. Our previous work demonstrated that targeting non‐canonical IKB kinase TBK1 reduced YAP/TAZ activation in human lung fibroblasts. Here, we investigate non‐canonical IKB kinases TBK1 and IKKε in pulmonary hypertension (PH) and their potential to modulate PASMC pathogenic remodeling by regulating YAP/TAZ. We show that TBK1 and IKKε are activated in PASMCs in a rat PH model. Inflammatory cytokines, elevated in PAH, activate these kinases in human PASMCs. Inhibiting TBK1/IKKε expression/activity significantly reduces PAH‐associated PASMC remodeling, with longer‐lasting effects on YAP/TAZ than treprostinil, an approved PAH therapy. These results show that non‐canonical IKB kinases regulate YAP/TAZ in PASMCs and may offer a novel approach for reducing vascular remodeling in PAH.

ABSTRACT Adsorption-based extracorporeal therapies have been subject to technical developments and clinical application for close to five decades. More recently, new technological developments in membrane and sorbent manipulation have made it possible to deliver more biocompatible extracorporeal adsorption therapies to patients with a variety of conditions. There are several key rationales based on physicochemical principles and clinical considerations that justify the application and investigation of such therapies as evidenced by multiple ex vivo, experimental and clinical observations. Accordingly, unspecific adsorptive extracorporeal therapies have now been applied to the treatment of a wide array of conditions from poisoning to drug overdoses, to inflammatory states and sepsis, and acute or chronic liver and kidney failure. In response to the rapidly expanding knowledge base and increased clinical evidence, we convened an Acute Disease Quality Initiative consensus conference dedicated to such treatment. The data show that hemoadsorption has clinically acceptable short-term biocompatibility and safety, technical feasibility and experimental demonstration of specified target molecule removal. Pilot studies demonstrate potentially beneficial effects on physiology and larger studies of endotoxin-based hemoadsorption have identified possible target phenotypes for larger randomized controlled trials. Moreover, in a variety of endogenous and exogenous intoxications, removal of target molecules has been confirmed in vivo. However, some studies have raised concerns about harm, or failed to deliver benefits. Thus, despite many achievements, modern hemoadsorption remains a novel and experimental intervention with limited data, and a large research agenda.