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Proprotein Convertase Subtilisin/Kexin type-9 (PCSK-9) inhibitors have recently used in the management of different cardiac complications. Several clinical trials demonstrated their effectiveness in patients with hypercholesterolemia. However, the effectiveness of these medications in patients with heart diseases is still controversial. To review and summarize the clinical trials pertaining to the use and effectiveness of PCSK-9 inhibitors in heart diseases and to discuss the pharmacotherapy of these agents. A review was conducted of all clinical trials with PCSK-9 inhibitors for heart diseases registered at ClinicalTrials.gov since inception up to and including January 19th, 2024. These trials were retrieved. Data from these trials were extracted manually, categorized and analyzed. The number of identified clinical trials was 25,371. After screening and excluding irrelevant studies, 12 studies met the search criteria. The majority of these studies were conducted in the US. The total number of patients in these studies was 27,700. Alirocumab and Evolocumab were the most frequently used PCSK-9 inhibitors. This review identified only a few clinical trials on PCSK-9 inhibitors in heart disease patients. Therefore, it is recommended to conduct more randomized controlled clinical trials on PCSK-9 inhibitors in this patient population.

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INTRODUCTION: Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the urgent need for accurate and efficient diagnostic tools. Echocardiography, a non-invasive imaging technique, plays a central role in the diagnosis of heart diseases, yet the potential impact of artificial intelligence (AI) on its accuracy and speed has not yet been reviewed and summarized. This scoping review aims to address this research gap by synthesizing existing evidence on AI-assisted echocardiography's. METHODS: The study followed Arksey and O'Malley's six-stage model for scoping reviews and searched the databases PubMed, Web of Science and Livivo. Inclusion criteria encompassed studies from cardiology utilizing AI for heart diseases diagnosis in adults, published from 2018 to 2023. Data extraction focused on study characteristics, AI models employed, accuracy metrics, and diagnostic speed. RESULTS: From 1059 identified studies, nine records met the inclusion criteria, categorized into view classification, left ventricular ejection fraction (LVEF) quantification, and diseases classification. Convolutional Neural Networks (CNN) were commonly used. While 44% of studies compared AI with cardiologists, those studies indicated AI's high diagnostic accuracy, with mean accuracy ranging from 87% to 92%. Three studies assessed AI's speed, demonstrating significant time savings. DISCUSSION: The review highlights AI's potential in enhancing diagnostic accuracy and efficiency in echocardiography, particularly in regions with limited access to specialized cardiologists. However, further research is needed to assess AI's specific added value compared to cardiologists, optimize training data quality, and enable real-time image processing.

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PURPOSE: This study aimed to develop a Quality of Life (QOL) assessment scale for older patients with Neuro-co-Cardiological Diseases (NCCD) and to evaluate the reliability and validity of the scale. METHOD: The study participants were derived from the Elderly Individuals with NCCD Registered Cohort Study (EINCCDRCS), a multicenter registry of patients with NCCD. The preliminary testing of the questionnaire was conducted among 10 older individuals aged 65 years and older who had NCCD and were recruited from the registry. Other patients who met the inclusion criteria participated in the field testing. After verifying the unidimensionality, local independence, and monotonicity assumptions of the scale, we employed the Rasch model within Item Response Theory framework to assess the quality of the scale through methods including internal consistency, criterion validity, Wright map, and item functioning differential. Subsequently, we assessed the construct validity of the scale by combining exploratory factor analysis with confirmatory factor analysis. RESULTS: Based on well-validated scales such as the short-form WHOQOL-OLD, HeartQOL, IQCODE, and SF-36, an original Neuro-co-Cardiological Diseases Quality of Life scale (NCCDQOL) was developed. 196 individuals from the EINCCDRCS were included in the study, with 10 participating in the preliminary testing and 186 in the field testing. Based on the results of the preliminary testing, the original questionnaire was refined through item deletion and adjustment, resulting in an 11-item NCCDQOL questionnaire. The Rasch analysis of the field testing data led to the removal of 21 misfitting individuals. The NCCDQOL demonstrated a four-category structure, achieved by combining two response categories. This structure aligned with the assumptions of unidimensionality, local independence, and monotonicity. The NCCDQOL also exhibited good validity and reliability. CONCLUSION: The revised NCCDQOL questionnaire demonstrated good reliability and validity in the Rasch model, indicating promising potential for clinical application.

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BACKGROUND: Ball thrombus is rare and life-threatening. The correct diagnosis and timely management are key to improving patient prognosis. Here, we present a case report and literature review of ball thrombus. CASE PRESENTATION: A 75-year-old woman presented to our outpatient clinic because of palpitations and chest distress for 8 months. She was diagnosed mitral stenosis, and transthoracic echocardiography (TTE) showed a round mass attached to the left atrial (LA) wall. Before anesthesia induction, TTE found that the mass has dropped from the LA wall, and was spinning in the LA causing intermittent obstruction of the valve. Anesthesia induction was then carried out under TTE monitoring, and transesophageal echocardiograph found another mass in the LA appendage after intubation. She underwent LA mass removal and mitral valve replacement, and was discharged uneventfully. Histopathology confirmed the diagnosis of thrombus. Our literature review identified 19 cases of ball thrombus between 2015 and 2024. The average age was 54.8 (range 3-88) years. Heart failure was present as the initial symptom in 11 cases, and most patients had mitral valve disease or concomitant with atrial fibrillation. 12 cases received surgery, and 7 received medical treatment only. 2 deaths occurred, one due to the obstruction of left ventricular inflow tract and the other due to the worsening of heart failure. CONCLUSION: Ball thrombus is rare in clinical settings. Urgent thrombectomy should be performed as soon as possible, and echocardiography can be used for real-time monitoring during surgery.

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BACKGROUND: Cardiac surgery is considered a contraindication in patients with advanced liver cirrhosis (LC) due to increased mortality and morbidity. There are limited data on the treatment strategy and management of this population. We aimed to present our strategy and evaluate the clinical outcome of cardiac surgery in patients with LC. METHODS: Our strategy was (i) to list patients for liver transplant (LT) at the time of cardiac surgery; (ii) to maintain high cardiopulmonary bypass (CPB) flow (index up to 3.0 L/min/m2) based on hyper-dynamic states due to LC; and (iii) to proceed to LT if patients' liver function deteriorated with an increasing model for end-stage liver disease Na (MELD-Na) score after cardiac surgery. Thirteen patients (12 male and 1 female [mean age, 63.0]) with LC who underwent cardiac surgery between 2017 and 2024 were retrospectively analyzed. RESULTS: Six patients were listed for LT. Indications for cardiac surgery included coronary artery disease (N = 7), endocarditis (N = 2), and tricuspid regurgitation (N = 1), tricuspid stenosis (N = 1), mitral regurgitation (N = 1), and hypertrophic obstructive cardiomyopathy (N = 1). The Child-Pugh score was A in five, B in six, and C in one patient. The procedure included coronary artery bypass grafting (N = 6), single valve surgery (mitral valve [N = 2] and tricuspid valve [N = 1]), concomitant aortic and tricuspid valve surgery (N = 2), and septal myectomy (N = 1). Two patients had a history of previous sternotomy. The perfusion index during CPB was 3.1 ± 0.5 L/min/m2. Postoperative complications include pleural effusion (N = 6), bleeding events (N = 3), acute kidney injury (N = 1), respiratory failure requiring tracheostomy (N = 2), tamponade (N = 1), and sternal infection (N = 1). There was no in-hospital death. There was one remote death due to COVID-19 complication. Preoperative and postoperative highest MELD-Na score among listed patients was 15.8 ± 5.1 and 19.3 ± 5.3, respectively. Five patients underwent LT (1, 5, 8, 16, and 24 months following cardiac surgery) and one patient remains on the list. Survival rates at 1 and 3 years are 100% and 75.0%, respectively. CONCLUSION: Cardiac surgery maintaining high CPB flow with LT backup is a feasible strategy in an otherwise inoperable patient population with an acceptable early and midterm survival when performed in a center with an experienced cardiac surgery and LT program.

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BACKGROUND AND OBJECTIVES: Ischemic stroke, a leading cause of mortality, necessitates understanding its mechanism for effective prevention. Echocardiography, especially transesophageal echocardiography (TEE), is the gold standard for detection of cardiac sources of stroke including left atrial thrombus, although its invasiveness, operator skill dependence, and limited availability in some centers prompt exploration of alternatives, such as cardiac CT (CCT). We conducted a systematic review and meta-analysis assessing the ability of CCT in the detection of intracardiac thrombus compared with echocardiography. METHODS: We searched 4 databases up through September 8, 2023. Major search terms included a combination of the terms "echocardiograph," "CT," "TEE," "imaging," "stroke," "undetermined," and "cryptogenic." The current systematic literature review of the English language literature was reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We assessed risk of bias using the QUADAS-2 tool and used random-effects meta-analysis to calculate different diagnostic metrics. RESULTS: The meta-analysis investigating CCT vs echocardiography for intracardiac thrombus detection yielded a total of 43 studies of 9,552 patients. Risk-of-bias assessment revealed a predominantly low risk of bias in the flow and timing, index test, and patient selection domains and a predominantly unclear risk of bias in the reference standard domain. The analysis revealed an overall sensitivity of 98.38% (95% CI 89.2-99.78) and specificity of 96.0% (95% CI 92.55-97.88). Subgroup analyses demonstrated that delayed-phase, electrocardiogram-gated CCT had the highest sensitivity (100%; 95% CI 0-100) while early-phase, nongated CCT exhibited a sensitivity of 94.31% (95% CI 28.58-99.85). The diagnostic odds ratio was 98.59 (95% CI 44.05-220.69). Heterogeneity was observed, particularly in specificity and diagnostic odds ratio estimates. DISCUSSION: CCT demonstrates high sensitivity, specificity, and diagnostic odds ratios in detecting intracardiac thrombus compared with traditional echocardiography. Limitations include the lack of randomized controlled studies, and other cardioembolic sources of stroke such as valvular disease, cardiac function, and aortic arch disease were not examined in our analysis. Large-scale studies are warranted to further evaluate CCT as a promising alternative for identifying intracardiac thrombus and other sources of cardioembolic stroke.

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Myocardial injury after non-cardiac surgery is a major issue with a rate of almost 20%, as suggested by the literature. Guidelines recommend screening patients undergoing non-cardiac surgery who have at least one cardiovascular risk factor. Clinical trials are characterized by a high degree of heterogeneity. Myocardial injury definitions vary among studies, and multiple troponin assays with different cut-offs are utilized. Myocardial injury has a poorly understood pathophysiology. While some studies only include troponin elevations that are thought to be caused by ischemia, others do not. Troponin elevation can be a result of patient-related factors and comorbidities, including age, chronic renal failure, and inflammatory status. Currently, there is no effective strategy to prevent perioperative myocardial injury, and there are no therapeutic options that significantly improve the outcome of patients with myocardial injury. We have focused on this topic and on perioperative myocardial injury to highlight the areas of research that remain unexplored.

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BACKGROUND: Helsmoortel-Van der Aa syndrome was officially documented in 2014. Helsmoortel-Van der Aa syndrome is an extremely rare complex neurodegenerative disorder characterized by reduced intellectual capacity, motor dysfunction, facial dysmorphism, impaired development, and an increased predisposition to autism spectrum disorder. In addition, many patients also present with neuropsychiatric disorders, including attention deficit hyperactivity disorder, anxiety disorders, and various behavioral abnormalities. Helsmoortel-Van der Aa syndrome is challenging to identify solely on the basis of symptoms, and genetic investigations, including exome sequencing, may facilitate diagnosis. CASE PRESENTATION: We report a case of 13-year-old Saudi patient who presented with dysmorphic features as illustrated in Fig. 1, severe mental retardation, autism spectrum disorder, and attention deficit hyperactivity disorder. Initial genetic testing was unremarkable; thus, a clinical exome analysis was performed to identify the genetic basis of the condition. CONCLUSIONS: Clinical exome analysis indicated an autosomal dominant Helsmoortel-Van der Aa syndrome with a likely pathogenic de novo variant within the activity-dependent neuroprotector homeobox (ADNP) gene not previously reported in Helsmoortel-Van der Aa syndrome. The patient had a right-sided solitary kidney and polycystic ovaries, conditions that were not previously associated with HVDAS.

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In this study, we analyzed pooled data from two prospective population-based cohorts-the Health Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA)-to explore the association between trajectories of depressive symptoms and the risk of cardiac events. Depressive symptoms were assessed using the 8-item CES-D scale and categorized into somatic and cognitive-affective subtypes. Trajectories were tracked for four surveys from baseline. Heart disease was identified based on self-reported physician-diagnosed conditions. Hazard ratios and 95% confidence intervals were calculated with Cox proportional risk models that adjusted for potential confounders. In total, 17,787 subjects (59.7% female, median age 63 years) were enrolled at baseline. During a 10-year follow-up, 2409 cases of heart disease were identified. Participants with fluctuating (HR = 1.13, 95% CI: 1.06-1.20), increasing (HR = 1.43, 95% CI: 1.25-1.64), and consistently high (HR = 1.64, 95% CI: 1.45-1.84) depressive symptom trajectories exhibited an increased risk of heart disease compared to those with consistently low depressive symptoms, while a decreasing (HR = 1.07, 95% CI: 0.96-1.19) depressive symptom trajectory did not significantly affect the risk of heart disease. Moreover, the association between heart disease and somatic depressive symptoms was found to be stronger than with cognitive-affective symptoms. These findings suggest a significant link between depressive symptom trajectories and heart disease, with particular emphasis on stronger associations with somatic symptoms. It is recommended that the identification and management of depressive symptoms be incorporated into heart disease prevention strategies.

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Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.

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SETANTA (Study of HEarT DiseAse and ImmuNiTy After COVID-19 in Ireland) study aimed to investigate symptom burden and incidence of cardiac abnormalities after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 and to correlate these results with biomarkers of immunological response and coagulation. SETANTA was a prospective, single-arm observational cross-sectional study condcuted in a primary practice setting, and prospectively registered with ClinicalTrials.gov (identifier: NCT04823182). Patients with recent COVID-19 infection (≥ 6 weeks and ≤ 12 months) were prospectively enrolled. Primary outcomes of interest were markers of cardiac injury detected by cardiac magnetic resonance imaging (CMR), which included left ventricular ejection fraction, late gadolinium enhancement and pericardial abnormalities, as well as relevant biomarkers testing immunological response and coagulopathy. 100 patients (n = 129 approached) were included, amongst which 64% were female. Mean age of the total cohort was 45.2 years. The median (interquartile range) time interval between COVID-19 infection and enrolment was 189 [125, 246] days. 83% of participants had at least one persistent symptom, while 96% had positive serology for prior SARS-CoV-2 infection. Late gadolinium enhancement, pericardial effusion, was present in 2.2% and 8.3% respectively, while left ventricular ejection fraction was below the normal reference limit in 17.4% of patients. Von Willebrand factor antigen was elevated in 32.7% of patients and Fibrinogen and D-Dimer levels were found to be elevated in 10.2% and 11.1% of patients, respectively. In a cohort of primary practice patients recently recovered from SARS-CoV-2 infection, prevalence of persistent symptoms and markers of abnormal coagulation were high, despite a lower frequency of abnormalities on CMR compared with prior reports of patients assessed in a hospital setting.Trial Registration: Clinicaltrials.gov, NCT04823182 (prospectively registered on 30th March 2021).

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BACKGROUND: The Revised Cardiac Risk Index (RCRI) and the American Society of Anaesthesiologists (ASA-PS) classification system are two commonly used tools for preoperative risk assessment. This study aimed to assess the accuracy of RCRI compared to the ASA-PS classification system in preoperative risk assessment for pulmonary and cardiac problems among non-cardiothoracic surgery patients admitted at Muhimbili National Hospital (MNH). METHODS: This was a prospective cohort study design conducted from August 2022 to April 2023 among 184 patients of 18 years and above admitted at MNH for elective non-cardiothoracic surgery. Data Analysis was conducted using STATA software version 16. Means and standard deviations were used to summarize continuous data. Frequencies and percentages were used to summarize categorical data. The logistic regression and ROC curve analysis were used to determine the correlation between variables. RESULTS: The majority of patients (43.3%) had an RCRI score of 1 point, and 39.9% were classified as ASA class 1. Patients in ASA classes 3 and 4 had higher odds of developing cardiac and pulmonary complications (AUC = 0.75 and 0.77, respectively). Patients with an RCRI score of 2 or ≥ 3 points were also more likely to experience cardiac and pulmonary complications (AUC = 0.73 and 0.72, respectively). There was no significant difference in the predictive ability of the two tools. Both RCRI and ASA-PS classification systems were equally effective in predicting these complications. CONCLUSION: Both the RCRI and the ASA-PS classification system demonstrated good predictive ability for cardiac and pulmonary complications among patients undergoing non-cardiothoracic surgery.

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The endothelium is a cell monolayer that lines vessels and separates tissues from blood flow. Endothelial cells (ECs) have a multitude of functions, including regulating blood flow and systemic perfusion through changes in vessel diameter. When an injury occurs, the endothelium is affected by altering its functions and structure, which leads to endothelial dysfunction, a characteristic of many vascular diseases. Understanding the role that the endothelium plays in pulmonary vascular and cardiopulmonary diseases, and exploring new therapeutic strategies is of utmost importance to advance clinically. Currently, there are several treatments able to improve patients' quality of life, however, none are effective nor curative. This review examines the critical role of the endothelium in the pulmonary vasculature, investigating the alterations that occur in ECs and their consequences for blood vessels and potential molecular targets to regulate its alterations. Additionally, we delve into promising non-pharmacological therapeutic strategies, such as exercise and diet. The significance of the endothelium in cardiopulmonary disorders is increasingly being recognized, making ECs a relevant target for novel therapies aimed at preserving their functional and structural integrity.

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PURPOSE OF REVIEW: Speckle tracking echocardiography (STE)-derived measures of myocardial mechanics, referred to herewithin as strain measurements, directly assess myocardial contractility and provide a nuanced assessment of ventricular function. This review provides an overview of strain measurements and their current clinical value and utility in decision making in pediatric cardiology. RECENT FINDINGS: Strain measurements are advancing understanding of how cardiac dysfunction occurs in children with acquired and congenital heart disease (CHD). Global strain measurements can detect early changes in cardiac function and are reliable methods of serially monitoring systolic function in children. Global strain measurements are increasingly reported in echocardiographic assessment of ventricular function alongside ejection fraction. Research is increasingly focused on how strain measurements can help improve clinical management, risk stratification, and prognostic insight. Although more research is needed, preliminary studies provide hope that there will be clinical benefit for strain in pediatric cardiology management. SUMMARY: Strain measurements provide a more detailed assessment of ventricular function than conventional measures of echocardiographic functional assessment. Strain measurements are increasingly being used to advance understanding of normal and abnormal myocardial contractility, to increase sensitivity to detect early cardiac dysfunction, and to improve prognostic management in children with acquired and CHD.

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BACKGROUND: Myocardial fibrosis, a hallmark of heart disease, is closely associated with macrophages, yet the genetic pathophysiology remains incompletely understood. In this study, we utilized integrated single-cell transcriptomics and bulk RNA-seq analysis to investigate the relationship between macrophages and myocardial fibrosis across omics integration. METHODS: We examined and curated existing single-cell data from dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocardial infarction (MI), and heart failure (HF), and analyzed the integrated data using cell communication, transcription factor identification, high dimensional weighted gene co-expression network analysis (hdWGCNA), and functional enrichment to elucidate the drivers of macrophage polarization and the macrophage-to-myofibroblast transition (MMT). Additionally, we assessed the accuracy of single-cell data from the perspective of driving factors, cell typing, anti-fibrosis performance of left ventricular assist device (LVAD). Candidate drugs were screened using L1000FWD. RESULTS: All four heart diseases exhibit myocardial fibrosis, with only MI showing an increase in macrophage proportions. Macrophages participate in myocardial fibrosis through various fibrogenic molecules, especially evident in DCM and MI. Abnormal RNA metabolism and dysregulated transcription are significant drivers of macrophage-mediated fibrosis. Furthermore, profibrotic macrophages exhibit M1 polarization and increased MMT. In HF patients, those responding to LVAD therapy showed a significant decrease in driver gene expression, M1 polarization, and MMT. Drug repurposing identified cinobufagin as a potential therapeutic agent. CONCLUSION: Using integrated single-cell transcriptomics, we identified the drivers of macrophage-mediated myocardial fibrosis in four heart diseases and confirmed the therapeutic effect of LVAD on improving HF with single-cell accuracy, providing novel insights into the diagnosis and treatment of myocardial fibrosis.

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OBJECTIVE: To analyze the influencing factors of postoperative thrombocytopenia in critically ill patients with heart disease and construct a nomogram prediction model. METHODS: From October 2022 to October 2023, 319 critically ill patients with heart disease who visited our hospital were collected and separated into postoperative thrombocytopenia group (n = 142) and no postoperative thrombocytopenia group (n = 177) based on their postoperative thrombocytopenia, Logistic regression analysis was applied to screen risk factors for postoperative thrombocytopenia in critically ill patients with heart disease; R software was applied to construct a nomogram for predicting postoperative thrombocytopenia in critically ill patients with heart disease, and ROC curves, calibration curves, and Hosmer-Lemeshow goodness of fit tests were applied to evaluate nomogram. RESULTS: A total of 142 out of 319 critically ill patients had postoperative thrombocytopenia, accounting for 44.51%. Logistic regression analysis showed that gender (95% CI 1.607-4.402, P = 0.000), age ≥ 60 years (95% CI 1.380-3.697, P = 0.001), preoperative antiplatelet therapy (95% CI 1.254-3.420, P = 0.004), and extracorporeal circulation time > 120 min (95% CI 1.681-4.652, P = 0.000) were independent risk factors for postoperative thrombocytopenia in critically ill patients with heart disease. The area under the ROC curve was 0.719 (95% CI: 0.663-0.774). The slope of the calibration curve was close to 1, and the Hosmer-Lemeshow goodness of fit test was χ2 = 6.422, P = 0.491. CONCLUSION: Postoperative thrombocytopenia in critically ill patients with heart disease is influenced by gender, age ≥ 60 years, preoperative antiplatelet therapy, and extracorporeal circulation time > 120 min. A nomogram established based on above multiple independent risk factors provides a method for clinical prediction of the risk of postoperative thrombocytopenia in critically ill patients with heart disease.

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Background: As hypertensive heart disease (HHD) presents a significant public health challenge globally, we analysed its global, regional, and national burdens and trends from 1990 to 2019. Methods: We used data from the Global Burden of Disease (GBD) 2019 study, focussing on the age-standardised prevalence rates (ASPRs) of HHD prevalence, age-standardised disability-adjusted life year (DALY) rates, average annual percentage change (AAPC), and risk factor attributions. We compared the HHD burden across sociodemographic index (SDI) strata, gender, age groups, and 204 countries and territories. Results: In 2019, the global prevalence of HHD was estimated at 18 598 thousand cases, with DALYs reaching 21 508 thousand. From 1990 to 2019, the ASPRs increased (AAPC = 0.21; 95% confidence interval (CI) = 0.17, 0.24), while the age-standardised DALY rates decreased (AAPC = -0.45; 95% CI = -1.23, -0.93). We observed the highest increase in ASPRs in high-middle SDI quantile countries, and an overall negative correlation between age-standardised DALY rates and SDI. Individuals above 70 years of age were the most affected, particularly elderly women. There has been a significant increase in HHD burden attributed to high body mass index (BMI) since 1990. The burden of HHD is concentrated in the middle SDI quintile, with population ageing and growth being major drivers for the increase in DALYs. We identified opportunities for reducing age-standardised DALY rates in the middle SDI quintile or lower. Conclusion: Despite a declining trend in the age-standardised DALY rates, the ASPRs of HHD continue to rise, especially in high-middle SDI regions. Meanwhile, countries in middle and lower SDI quintiles face a higher burden of age-standardised DALY rates. Targeted attention towards elderly women and controlling high BMI, alongside enhancing hypertension and HHD management awareness, is crucial for reducing the global burden of HHD.

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In therapeutic diagnostics, early diagnosis and monitoring of heart disease is dependent on fast time-series MRI data processing. Robust encryption techniques are necessary to guarantee patient confidentiality. While deep learning (DL) algorithm have improved medical imaging, privacy and performance are still hard to balance. In this study, a novel approach for analyzing homomorphivally-encrypted (HE) time-series MRI data is introduced: The Multi-Faceted Long Short-Term Memory (MF-LSTM). This method includes privacy protection. The MF-LSTM architecture protects patient's privacy while accurately categorizing and forecasting cardiac disease, with accuracy (97.5%), precision (96.5%), recall (98.3%), and F1-score (97.4%). While segmentation methods help to improve interpretability by identifying important region in encrypted MRI images, Generalized Histogram Equalization (GHE) improves image quality. Extensive testing on selected dataset if encrypted time-series MRI images proves the method's stability and efficacy, outperforming previous approaches. The finding shows that the suggested technique can decode medical image to expose visual representation as well as sequential movement while protecting privacy and providing accurate medical image evaluation.

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