This study aims to assess the global prevalence of kinesiophobia and the potential influencing factors among patients with heart disease. A comprehensive search was conducted in PubMed, Embase, Web of Science, PsycINFO, and Scopus databases to identify studies reporting on the prevalence of kinesiophobia and its influencing factors in heart disease patients up to January 2024. A random-effects model was employed to aggregate prevalence rates. Heterogeneity sources were investigated through subgroup analysis, while differences in the prevalence of kinesiophobia across regions, types of heart disease, and gender were evaluated. Additionally, a qualitative analysis of the factors influencing kinesiophobia was performed. This research incorporated 15 studies from six countries, with 14 providing data on the prevalence of kinesiophobia and nine exploring its potential influencing factors. The findings indicated that the overall prevalence of kinesiophobia among heart disease patients was 61.0% (95% CI 49.4-72.6%). Subgroup analysis revealed that the prevalence in upper-middle-income countries was 71.8% (95% CI 66.2-77.4%), while it stands at 49.9% (95% CI 30.2-69.5%) in high-income countries. The prevalence rates among patients with coronary artery disease, heart failure, and atrial fibrillation were 63.2% (95% CI 45.2-81.3%), 69.2% (95% CI 57.6-80.8%), and 71.6% (95% CI 67.1-76.1%), respectively. Gender-wise, no significant difference was observed in the prevalence of kinesiophobia between men and women (52.2% vs. 51.8%). A total of 24 potential influencing factors of kinesiophobia were identified, with education level, monthly income, anxiety, and exercise self-efficacy being the most recognized. The prevalence of kinesiophobia in patients with heart disease is notably high and is influenced by a multitude of factors. Early implementation of targeted preventive measures is imperative to mitigate the incidence of kinesiophobia in this population.

BACKGROUND: Heart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers.
OBJECTIVE: We aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website.
METHODS: Using the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023.
RESULTS: This study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management.
CONCLUSIONS: These findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.

BACKGROUND: For a decade, despite results from many studies, telemedicine systems have suffered from a lack of recommendations for chronic heart failure (CHF) care because of variable study results. Another limitation is the hospital-based architecture of most telemedicine systems. Some systems use an algorithm based on daily weight, transcutaneous oxygen measurement, and heart rate to detect and treat acute heart failure (AHF) in patients with CHF as early on as possible.
OBJECTIVE: The aim of this study is to determine the efficacy of a telemonitoring system in detecting clinical destabilization in real-life settings (out-of-hospital management) without generating too many false positive alerts.
METHODS: All patients self-monitoring at home using the system after a congestive AHF event treated at a cardiology clinic in France between March 2020 and March 2021 with at least 75% compliance on daily measurements were included retrospectively. New-onset AHF was defined by the presence of at least 1 of the following criteria: transcutaneous oxygen saturation loss, defined as a transcutaneous oxygen measurement under 90%; rise of cardiac frequency above 110 beats per minute; weight gain of at least 2 kg; and symptoms of congestive AHF, described over the phone. An AHF alert was generated when the criteria reached our definition of new-onset acute congestive heart failure (HF).
RESULTS: A total of 111 consecutive patients (n=70 men) with a median age of 76.60 (IQR 69.5-83.4) years receiving the telemonitoring system were included. Thirty-nine patients (35.1%) reached the HF warning level, and 28 patients (25%) had confirmed HF destabilization during follow-up. No patient had AHF without being detected by the telemonitoring system. Among incorrect AHF alerts (n=11), 5 patients (45%) had taken inaccurate measurements, 3 patients (27%) had supraventricular arrhythmia, 1 patient (9%) had a pulmonary bacterial infection, and 1 patient (9%) contracted COVID-19. A weight gain of at least 2 kg within 4 days was significantly associated with a correct AHF alert (P=.004), and a heart rate of more than 110 beats per minute was more significantly associated with an incorrect AHF alert (P=.007).
CONCLUSIONS: This single-center study highlighted the efficacy of the telemedicine system in detecting and quickly treating cardiac instability complicating the course of CHF by detecting new-onset AHF as well as supraventricular arrhythmia, thus helping cardiologists provide better follow-up to ambulatory patients.

BACKGROUND: Occurrence of low blood taurine concentrations (B-TauC) and predisposing factors to taurine deficiency in English Cocker Spaniels (ECS) are incompletely understood.
OBJECTIVE: Investigate the occurrence of low B-TauC in a Swedish population of ECS and evaluate the association between B-TauC and dog characteristics, clinical variables, and diet composition.
METHODS: One-hundred eighty privately owned ECS.
METHODS: Dogs were prospectively recruited and underwent physical examination, blood analyses, and echocardiographic and ophthalmic examinations. Dogs with clinical signs of congestive heart failure (CHF) also underwent thoracic radiography. Taurine concentrations were analyzed in plasma (EDTA and heparin) and whole blood. Diets consumed by the dogs at the time of the examination were analyzed for dietary taurine- (D-TauC), cysteine- (D-CysC), and methionine concentrations (D-MetC).
RESULTS: Fifty-three of 180 dogs (29%) had low B-TauC, of which 13 (25%) dogs had clinical and radiographic signs of CHF, increased echocardiographic left ventricular (LV) dimensions and volumes, and impaired LV systolic function. Five (9%) dogs with low B-TauC had retinal abnormalities. Dietary MetC, dietary animal protein source (red/white meat), and age were associated with B-TauC in the final multivariable regression model (P < .001, R2 adj = .39).
CONCLUSIONS: Low B-TauC suggests that taurine deficiency may play a role in the development of myocardial failure and CHF in ECS. Low D-MetC and diets with red meat as the animal protein source were associated with low B-TauC. Dogs with B-TauC below the normal reference range were older than dogs with normal concentrations.

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BACKGROUND: Observational and retrospective studies suggest that people with narcolepsy may have an increased prevalence of cardiovascular and cardiometabolic comorbidities and may be at greater risk for future cardiovascular events. An expert consensus panel was formed to establish agreement on the risk of hypertension and cardiovascular/cardiometabolic disease in people with narcolepsy and to develop strategies to mitigate these risks.
RESULTS: Experts in sleep medicine and cardiology were selected to participate in the panel. After reviewing the relevant literature, the experts identified key elements, drafted recommendation statements, and developed discussion points to provide supporting evidence for the recommendations. The draft and final recommendations were rated on a scale from 0 (not at all agree) to 4 (very much agree). All experts had an agreement rating of 4.0 for all 14 revised recommendation statements for patients with narcolepsy. These statements comprised 3 themes: (1) recognize the risk of hypertension and cardiovascular/cardiometabolic disease, (2) reduce the risk of hypertension and cardiovascular/cardiometabolic disease, and (3) reduce sodium intake to lower the risk of hypertension and cardiovascular disease.
CONCLUSIONS: These consensus recommendations are intended to increase awareness of potential cardiovascular/cardiometabolic risks in patients with narcolepsy for all clinicians. Early monitoring for, and prevention of, cardiovascular risks in this population are of great importance, especially as narcolepsy usually develops in adolescents and young adults, who will be exposed to adverse effects of the disease for decades. Prospective systematic studies are needed to determine association and causation of narcolepsy with cardiovascular/cardiometabolic disorders.

Oxidative stress is a pivotal factor in the pathogenesis of various cardiovascular diseases. Rhodiola, a traditional Chinese medicine, is recognized for its potent antioxidant properties. Salidroside, a phenylpropanoid glycoside derived from Rhodiola rosea, has shown remarkable antioxidant capabilities. This study aimed to elucidate the potential protective mechanisms of Rhodiola and salidroside against H2O2-induced cardiac apoptosis in H9c2 cardiomyoblast cells. H9c2 cells were exposed to H2O2 for 4 h, and subsequently treated with Rhodiola or salidroside for 24 h. Cell viability and apoptotic pathways were assessed. The involvement of insulin-like growth factor 1 receptor (IGF1R) and the activation of extracellular regulated protein kinases 1/2 (ERK1/2) were investigated. H2O2 (100 μM) exposure significantly induced cardiac apoptosis in H9c2 cells. However, treatment with Rhodiola (12.5, 25, and 50 μg/mL) and salidroside (0.1, 1, and 10 nM) effectively attenuated H2O2-induced cytotoxicity and apoptosis. This protective effect was associated with IGF1R-activated phosphorylation of ERK1/2, leading to the inhibition of Fas-dependent proteins, HIF-1α, Bax, and Bak expression in H9c2 cells. The images from hematoxylin and eosin staining and immunofluorescence assays also revealed the protective effects of Rhodiola and salidroside in H9c2 cells against oxidative damage. Our findings suggest that Rhodiola and salidroside possess antioxidative properties that mitigate H2O2-induced apoptosis in H9c2 cells. The protective mechanisms involve the activation of IGF1R and subsequent phosphorylation of ERK1/2. These results propose Rhodiola and salidroside as potential therapeutic agents for cardiomyocyte cytotoxicity and apoptosis induced by oxidative stress in heart diseases. Future studies may explore their clinical applications in cardiac health.

Medical innovations may lessen the perceived risk of disease which can decrease the take-up of healthy behaviors, a phenomenon known as risk compensation. In contrast, a diagnosis provides updated information about the state of one\'s health which may motivate positive behavior change. In this paper, I consider how behavior changes in response to a diagnosis of cardiovascular disease (CVD) before and after the FDA approval of new classes of drugs to treat high blood pressure and high cholesterol in 1973. I find that individuals diagnosed with CVD are more likely to follow a diet and decrease body-mass index in response to the diagnosis, irrespective of medication approvals. Nonsmoking is a notable exception. Prior to medication availability, there is no change in smoking behavior in response to a CVD diagnosis. Conversely, when medication is available, there is a significant decline in smoking. The empirical complementarity of medication and smoking cessation may be driven by increased exposure to medical professionals (who emphasize the harms of smoking) or because medication decreases the risk of CVD death which heightens the importance of investing in future health.

Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to uncover hidden and unexpected rules that identify critical factors contributing to heart disease. This method is particularly adept at identifying high-risk patterns in individuals who appear healthy but may develop heart disease under certain conditions, thus facilitating early intervention and preventive measures. By integrating these insights with established feature engineering techniques, EPFHD-RARMING enhances its practical utility, enabling medical professionals to proactively manage patient care and tailor interventions to individual risk profiles. This study demonstrates the effectiveness of EPFHD-RARMING in providing a deeper, actionable understanding of the complex dynamics of heart disease. The model\'s ability to identify and interpret rare patterns holds significant promise for advancing medical analytics and improving patient outcomes. Moreover, the applicability of EPFHD-RARMING extends beyond the healthcare domain, offering valuable insights in various fields where the discovery of rare patterns is critical, such as finance, marketing, and cybersecurity. This study conducts a comprehensive evaluation, which demonstrates the superior performance of EPFHD-RARMING compared to traditional predictive models in identifying key factors contributing to heart disease, in terms of interestingness, explainability, and comprehensiveness of insights. The results underscore the potential of this innovative approach to revolutionize our understanding and prediction of heart disease, ultimately contributing to more effective and personalized healthcare solutions. This research emphasizes the importance of rare association rule mining in medical analytics and paves the way for future studies to explore and utilize these techniques across diverse domains.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv + DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv + DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.