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Background: Hypertension (HTN), especially masked hypertension, is one of the cardiovascular consequences of nephrotic syndrome. Masked hypertension cannot be identified during routine follow-up visits and adversely effects the patients' cardiac function. The purpose of this study was to use ambulatory blood pressure monitoring (ABPM) to evaluate the blood pressure status of children with nephrotic syndrome. Methods: Ninety children with nephrotic syndrome (NS) participated in this cross-sectional study, which was carried out at Cairo University Children Hospital's nephrology clinic (CUCH). A sphygmomanometer was used in the clinic to measure blood pressure, and a Meditech monitor was used for 24-hour ambulatory blood pressure monitoring (ABPM). Interventricular septum (IVS) was measured, and heart functions were evaluated, using echocardiography. Results: Two groups comprised the included patients: Group1 (n = 70): HTN group included masked and ambulatory hypertension, and Group 2 (n = 20): non-HTN group included normal blood pressure, white coat HTN and well controlled HTN, 35% of the studied cohort (n = 32/90) had masked HTN.The serum urea was significantly higher in HTN group than non-HTN group with p-value: 0.047, while the serum albumin was significantly lower in HTN group than non-HTN group with p-value: 0.017. The cut-off point of 9.9, the sensitivity and specificity of serum urea to predict the occurrence of hypertension in NS patients was 92.9% and 35% respectively, with p-value : 0.024 and 95% CI (0.534–0.798). The z score of IVS is significantly higher in group 1 (2.5 ± 1.2) when compared to group 2 (1.7 ± 2.1) with p-value: 0.025 and Among group 1, it was noticed that 74% (n = 52/70) of them were systolic non-dipper, also it was observed that the mean serum potassium and cholesterol were significantly higher among systolic non-dipper when compared with systolic dipper patients with p-values: 0.045 and 0.005 respectively. Conclusion: Children with nephrotic syndrome are particularly vulnerable to experience ambulatory hypertension and masked hypertension, which may adversely impact their cardiac condition because they are not detectable by standard blood pressure readings at the clinic. [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) is associated with a large clinical and economic burden. However, consensus on the optimal approach to CAD diagnosis is lacking. This study sought to compare downstream healthcare resource utilisation following different cardiac imaging modalities, to inform test selection for CAD diagnosis. Methods: Claims and electronic health records data from the Decision Resources Group Real-World Evidence US Data Repository were analysed for 2.5 million US patients who underwent single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI), positron emission tomography myocardial perfusion imaging (PET MPI), coronary computed tomography angiography (cCTA), or stress echocardiography between January 2016 and March 2018. Patients were stratified into nine cohorts based on suspected or existing CAD diagnosis, pre-test risk, and prior events or interventions. Downstream healthcare utilisation, including additional diagnostic imaging, coronary angiography, and cardiac-related health system encounters, was compared by cohort and index imaging modality. Results: Among patients with suspected CAD diagnosed within 3 months of the index test, PET MPI was associated with lower downstream utilisation; 25–37% of patients who underwent PET MPI required additional downstream healthcare resources compared with 40–49% of patients who received SPECT MPI, 35–41% of patients who underwent cCTA, and 44–47% of patients who received stress echocardiography. Patients who underwent PET MPI experienced fewer acute cardiac events (5.3–9.4%) and generally had lower rates of healthcare encounters (0.8–4.1%) and invasive coronary angiography (ICA, 15.4–24.2%) than those who underwent other modalities. SPECT MPI was associated with more downstream ICA (31.3–38.2%) and a higher rate of cardiac events (9.5–13.2%) compared with PET MPI (5.3–9.4%) and cCTA (6.9–9.9%). Across all cohorts, additional diagnostic imaging was 1.6 to 4.7 times more frequent with cCTA compared with PET MPI. Conclusion: Choice of imaging modality for CAD diagnosis impacts downstream healthcare utilisation. PET MPI was associated with lower utilisation across multiple metrics compared with other imaging modalities studied. [ABSTRACT FROM AUTHOR]

Background: Severely ill patients with coronavirus disease 2019 (COVID‐19) show an increased risk of new‐onset atrioventricular blocks (AVBs), associated with high rates of short‐term mortality. Recent data suggest that the uncontrolled inflammatory activation observed in these patients, specifically interleukin (IL)‐6 elevation, may play an important pathogenic role by directly affecting cardiac electrophysiology. The aim of our study was to assess the acute impact of IL‐6 changes on electrocardiographic indices of atrioventricular conduction in severe COVID‐19. Methods: We investigated (1) the behavior of PR‐interval and PR‐segment in patients with severe COVID‐19 during active phase and recovery, and (2) their association with circulating IL‐6 levels over time. Results: During active disease, COVID‐19 patients showed a significant increase of PR‐interval and PR‐segment. Such atrioventricular delay was transient as these parameters rapidly normalized during recovery. PR‐indices significantly correlated with circulating IL‐6 levels over time. All these changes and correlations persisted also in the absence of laboratory signs of cardiac strain/injury or concomitant treatment with PR‐prolonging drugs, repurposed or not. Conclusions: Our study provides evidence that in patients with severe COVID‐19 and high‐grade systemic inflammation, IL‐6 elevation is associated with a significant delay of atrioventricular conduction, independent of concomitant confounding factors. While transient, such alterations may enhance the risk of severe AVB and associated short‐term mortality. Our data provide further support to current anti‐inflammatory strategies for severe COVID‐19, including IL‐6 antagonists. [ABSTRACT FROM AUTHOR]

In this modern era, heart diseases have surfaced as the leading factor of fatalities, accounting for around 17.9 million lives annually. Global deaths from heart diseases have surged by 60% over the last 30 years, primarily because of limited human and logistical resources. Early detection is crucial for effective management through counseling and medication. Earlier studies have identified key elements for heart disease diagnosis, including genetic predispositions and lifestyle factors such as age, gender, smoking habits, stress, diastolic blood pressure, troponin levels, and electrocardiogram (ECG). This project aims to develop a model that can identify the best machine learning (ML) algorithm for predicting heart diseases with high accuracy, precision, and the least misclassification. Various ML techniques were evaluated using selected features from the heart disease dataset. Among these techniques, a combination of random forest (RF), multi-layer perceptron (MLP), XGBoost, and LightGBM employing an ensemble method with a stacking classifier, along with logistic regression (LR) as a metamodel, achieved the highest accuracy rate of 95.8%. This surpasses the efficiency of other techniques. The suggested method provides an encouraging framework for early prediction, with the overarching goal of reducing global mortality rates associated with these conditions. [ABSTRACT FROM AUTHOR]

Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart's magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart's magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology is currently being used in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Although many researchers have focused on the diagnosis of heart disease, the accuracy of the diagnosis results is low. To address this problem, we present a novel method for predicting heart disease. This paper proposes a novel approach for heart disease detection using long short-term memory networks (LSTM) optimized with Self-improved Jellyfish Optimization. Initially, input data are gathered from the smartwatch and heart monitor device that is attached to the patient to monitor the blood pressure and electrocardiogram (ECG). Then the input data are Pre-processed using the Principal Component Analysis (PCA) algorithm. The African vulture's optimization algorithm (AVOA) is employed for feature selection. Selected features are provided to LSTM for classifying the received sensor data into normal and abnormal. The proposed approach aims to optimize LSTM hyperparameters the Self-improved Jellyfish Optimization (SIJO) is utilized. In addition, the proposed algorithm incorporates a self-improvement mechanism. By then, the proposed approach's performance has been tested on the MATLAB platform and its results have been compared to those of other approaches. Thus, the results demonstrate the effectiveness of the Jellyfish Optimization algorithm in enhancing LSTM for heart disease detection. [ABSTRACT FROM AUTHOR]

Objectives: To compare the diagnostic performance of machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Methods: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. Results: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). Conclusion: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. Clinical relevance statement: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. Key Points: • Both machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis. [ABSTRACT FROM AUTHOR]

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Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging (MRI) and Electrocardiogram (ECG). Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network (TCGN) for ECG feature extraction, combining these through an attention mechanism to emphasize relevant features. Classification is performed using Optimized TCGN. This approach improves early detection, reduces diagnostic errors, and supports personalized risk assessments and continuous health monitoring. The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models, achieving accuracies of 92.56% for Heartnet Dataset I (HNET-DSI), 93.45% for Heartnet Dataset II (HNET-DSII), and 91.89% for Heartnet Dataset III (HNET-DSIII), mitigating the impact of apparatus faults and image quality issues. These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a clinical syndrome characterized by well-defined signs and symptoms due to structural and/or myocardial functional impairment, resulting in raised intracardiac pressures and/or inadequate cardiac stroke volume at rest or during exercise. This could derive from direct ischemic myocardial injury or other chronic pathological conditions, including valvular heart disease (VHD) and primary myocardial disease. Early identification of HF etiology is essential for accurate diagnosis and initiation of early and appropriate treatment. Thus, the presence of accurate means for early diagnosis of HF symptoms or subclinical phases is fundamental, among which echocardiography being the first line diagnostic investigation. Echocardiography could be performed at rest, to identify overt structural and functional abnormalities or during physical or pharmacological stress, in order to elicit subclinical myocardial function impairment e.g. wall motion abnormalities and raised ventricular filling pressures. Beyond diagnosis of ischemic heart disease, stress echocardiography (SE) has recently shown its unique value for the evaluation of diastolic heart failure, VHD, non-ischemic cardiomyopathies and pulmonary hypertension, with recommendations from international societies in several clinical settings. All these features make SE an important additional tool, not only for diagnostic assessment, but also for prognostic stratification and therapeutic management of patients with HF. In this review, the unique value of SE in the evaluation of HF patients will be described, with the objective to provide an overview of the validated methods for each setting, particularly for HF management. [ABSTRACT FROM AUTHOR]

Although intracoronary electrocardiography (IC-ECG) offers direct electrophysiological insights into myocardial ischemia caused by insufficient coronary blood supply, compared to common diagnostic methods like electrocardiography (ECG), it lacks widespread adoption and robust clinical research. To analyze the value and accuracy of intracoronary electrocardiogram in myocardial ischemia diagnosis in coronary heart disease patients. Three hundred patients treated at our hospital were included in the study. Patients were categorized into non-ischemic group A (Fraction Flow Reserve [FFR] > 0.8) and ischemic group B (FFR < 0.75) based on FFR examination results. Both groups underwent IC-ECG examination. The ischemic group received percutaneous coronary intervention (PCI) treatment followed by another FFR examination, dividing them into non-ischemic subgroup B1 (FFR > 0.8) and ischemic subgroup B2 (FFR < 0.75). Both subgroups underwent IC-ECG examination. Receiver operating curves were constructed using FFR to assess the clinical utility of different IC-ECG parameters. Group A patients showed a significant decrease in ST-segment shift at J-point, ST-segment integral, T-peak, T-wave integral, and T-peak to end-time, while the Corrected Q-T interval (QTc-time) was significantly higher in the B group (p< 0.05). The parameters, including ST-segment shift at J-point, ST-segment integral, T-wave integral, T-peak, T-peak to end-time, and QTc-time, were found to have clinical significance in predicting the occurrence of myocardial ischemia (p< 0.05). Intracoronary electrocardiogram QT interval dispersion and Q-T peak (QTp) interval dispersion have a high diagnostic accuracy for myocardial ischemia in coronary heart disease. [ABSTRACT FROM AUTHOR]

Background: Higher odds of survival have been reported in European infants compared to Indigenous Māori and Pasifika infants with critical congenital heart disease in New Zealand. We therefore aimed to understand how to mitigate this disparity by investigating the parent and healthcare professional experiences' of critical congenital heart disease healthcare in New Zealand. Methods: A prospective qualitative study utilising semi-structured interviews was conducted on a cohort of purposefully sampled parents and health professionals with experience of critical congenital heart disease healthcare in New Zealand. Parents were recruited after a fetal critical congenital heart disease diagnosis and offered two interviews at least three months apart, whilst multidisciplinary fetal and cardiosurgical health professionals were interviewed once. Interviews were recorded and transcribed verbatim before coding, categorization and qualitative analysis. Results: During 2022 and 2023, 45 people participated in 57 interviews (25 parents: 19 mothers, 6 fathers; Indigenous Māori, n = 5; Pasifika, n = 6; Asian, n = 4; European, n = 10; and 20 healthcare professionals: European n = 17). The three lessons learned from participants were: (1) Minoritized groups experience disparate healthcare quality; (2) healthcare systems are under-resourced to provide equitable support for the differential needs of grieving parents; and (3) healthcare systems could engage minoritized families more optimally in shared decision-making. Conclusions: According to the experiences of parents and healthcare professionals, persisting inequities in CCHD healthcare quality occur by ethnic group, with the New Zealand healthcare system privileging European families. The concepts from this study could be translated by healthcare leaders, policymakers, and professionals into evidence-based healthcare system improvements to enhance experiences for non-European families more broadly. [ABSTRACT FROM AUTHOR]

Aim: To determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD). Methods and results: Two-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7–96) and a specificity of 76.3% (72.2–80) compared to exercise-ECG with 54.9% (47.9–61.7) and 60.9% (53.4–66.3), SPECT with 72.9% (65–79.6) and 44.9% (36.8–53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%. Conclusion: In patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability. Systematic review registration: PROSPERO Database for Systematic Reviews—CRD42012002780. Critical relevance statement: In symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD. Key Points: Coronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT. The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%. Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%. [ABSTRACT FROM AUTHOR]

Highlights: Photoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy. Enhanced gas sensitive property was achieved by visible light modulation. Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite. Diagnosis of coronary heart disease was achieved with the help of machine learning. Early non-invasive diagnosis of coronary heart disease (CHD) is critical. However, it is challenging to achieve accurate CHD diagnosis via detecting breath. In this work, heterostructured complexes of black phosphorus (BP) and two-dimensional carbide and nitride (MXene) with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy. A light-activated virtual sensor array (LAVSA) based on BP/Ti3C2Tx was prepared under photomodulation and further assembled into an instant gas sensing platform (IGSP). In addition, a machine learning (ML) algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD. Due to the synergistic effect of BP and Ti3C2Tx as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine Ti3C2Tx, with a response value 26% higher than that of pristine Ti3C2Tx. In addition, with the help of a pattern recognition algorithm, LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols, ketones, aldehydes, esters, and acids. Meanwhile, with the assistance of ML, the IGSP achieved 69.2% accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients. In conclusion, an immediate, low-cost, and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD, which provided a generalized solution for diagnosing other diseases and other more complex application scenarios. [ABSTRACT FROM AUTHOR]

Background: Kawasaki disease (KD) has been considered as the most common required pediatric cardiovascular diseases among the world. However, the molecular mechanisms of KD were not fully underlined, leading to a confused situation in disease management and providing precious prognosis prediction. The disorders of gut microbiome had been identified among several cardiovascular diseases and inflammation conditions. Therefore, it is urgent to elucidate the characteristics of gut microbiome in KD and demonstrate its potential role in regulating intravenous immunoglobulin (IVIG) resistance and coronary artery injuries. Methods: A total of 96 KD children and 62 controls were enrolled in the study. One hundred forty fecal samples had been harvested from KD patients, including individuals before or after IVIG treatment, with or without early coronary artery lesions and IVIG resistance. Fecal samples had been collected before and after IVIG administration and stored at -80°C. Then, metagenomic analysis had been done using Illumina NovaSeq 6000 platform. After that, the different strains and functional differences among comparisons were identified. Results: First, significant changes had been observed between KD and their controls. We found that the decrease of Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides uniformis, and Bacteroides ovatus and the increase of pathogenic bacteria Finegoldia magna, Abiotrophia defectiva, and Anaerococcus prevotii perhaps closely related to the incidence of KD. Then, metagenomic and responding functional analysis demonstrated that short-chain fatty acid pathways and related strains were associated with different outcomes of therapeutic efficacies. Among them, the reduction of Bacteroides thetaiotaomicron, the enrichment of Enterococcus faecalis and antibiotic resistance genes had been found to be involved in IVIG resistance of KD. Moreover, our data also revealed several potential pathogenetic microbiome of that KD patients with coronary artery lesions Conclusion: These results strongly proved that distinct changes in the gut microbiome of KD and the dysfunction of gut microbiomes should be responsible for the pathogenesis of KD and significantly impact the prognosis of KD. [ABSTRACT FROM AUTHOR]

Background: The objective of our study was to assess the prenatal course, associated anomalies and postnatal outcome and the predictive value of various prenatal parameters for survival in prenatally diagnosed cases of truncus arteriosus communis (TAC). Methods: We evaluated cases from four centers between 2008 and 2021. Results: In 37/47 cases (78.7%), classification into a Van Praagh sbtype was possible, most had TAC type A1 (18/37 = 48.6%). In 33/47 (70.2%) with available valve details on common trunk valve, most presented with tricuspid valves (13/33 = 39.4%). In the overall sample, 14/47 (29.8%) had relevant insufficiency, and 8/47 (17%) had stenosis. In total, 37/47 (78.7%) underwent karyotyping, with 15/37 (40.5%) showing abnormal results, mainly 22q11.2 microdeletion (9/37 = 24.3%). Overall, 17/47 (36.2%) had additional extracardiac anomalies (17/47 = 36.2%). Additional intracardiac anomalies were present in 30/47 (63.8%), or 32/47 (68.1%) if coronary anomalies were included. Four (8.5%) had major defects. Two (4.3%) intrauterine deaths occurred, in 10 (21.3%) cases, the parents opted for termination, predominantly in non-isolated cases (8/10 = 80.0%). A total of 35/47 (74.5%) were born alive at 39 (35–41) weeks. Three (8.6%) pre-surgical deaths occurred in non-isolated cases. In 32/35 (91.4%), correction surgery was performed. The postoperative survival rate was 84.4% (27/32) over a median follow-up of 51.5 months. Initial intervention was performed 16 (1–71) days postpartum, and 22/32 (68.8%) required re-intervention. Regarding prenatal outcome-predicting parameters, no significant differences were identified between the survivor and non-survivor groups. Conclusions: There exist limited outcome data for TAC. To our knowledge, this is the largest multicenter, prenatal cohort with an intention-to-treat survival rate of almost 85%. [ABSTRACT FROM AUTHOR]

Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs. [ABSTRACT FROM AUTHOR]

Echocardiography is a non-invasive and gold standard imaging tool for diagnosing dilated cardiomyopathy (DCM) in dogs. This study aimed to compare the echocardiographic parameters between healthy and DCM-affected dogs. A total of 52 client-owned dogs, comprising 38 males and 14 females, were included. Among these, 24 dogs (46.15%) were classified as healthy controls and 28 dogs (53.85%) were part of DCM group. On breed-wise prevalence, it was reported that Labrador Retriever breeds showed a higher incidence of DCM than the others. The comparative studies of echocardiographic parameters showed that DCM-affected dogs had significantly higher values in left ventricular long axis length at -end diastole (LVLdA4C) and -end systole (LVLsA4C), end diastolic volume (EDV), end systolic volume (ESV), left atrium (LA)/aorta diameter (Ao) ratio, left ventricular internal dimension at systole (LVIDs), and end point septal separation (EPSS), as well as significantly lower values in left ventricular contractibility indices such as fractional shortening (FS) and ejection fraction (EF) compared to healthy dogs. Also, receiver operating characteristic curves were made to determine the optimal cut-off points for each echocardiographic parameterwith specificity and sensitivity fordiagnosingDCM. Significant areas under the curvewere observed for parameters such as LVIDs, EF, FS, LA/Ao, EPSS, LVLdA4C, LVLsA4C, left ventricular EDV, left ventricular ESV, and ESV for DCM-affected dogs. This cut-off value can be used as an early diagnosis of DCM through echocardiography, facilitating timely clinical interventions and managementstrategies for improved quality of life in dogs. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm. [ABSTRACT FROM AUTHOR]

Introduction: Early morning is characterized by an increased risk of cardiovascular events, a sudden rise in blood pressure, impaired endothelial function, and exacerbated hemodynamic changes during physical activity. The study aims to examine whether the time of day of physical activity is associated with incident cardiovascular disease (CVD). Methods: We prospectively analyzed 83,053 participants in the UK Biobank with objectively measured physical activity and initially free of CVD. Based on the diurnal patterns of physical activity, participants were categorized into 4 groups: early morning (n = 15,908), late morning (n = 22,371), midday (n = 24,764), and evening (n = 20,010). Incident CVD was defined as the first diagnosis of coronary heart disease or stroke. Results: During 197.4 million person-years of follow-up, we identified 3454 CVD cases. After adjusting for the overall acceleration average, the hazard ratios and 95% confidence intervals were 0.95 (0.86–1.07) for late morning, 1.15 (1.03–1.27) for midday, and 1.03 (0.92–1.15) for evening, as compared with the early morning group. In the joint analyses, higher levels of physical activity were associated with a lower risk of incident CVD in a similar manner across the early morning, late morning, and evening groups. However, the beneficial association was attenuated in the midday group. Conclusion: In conclusion, early morning, late morning, and evening are all favorable times of day to engage in physical activity for the primary prevention of CVD, while midday physical activity is associated with an increased risk of CVD compared with early morning physical activity after controlling for the levels of physical activity. [ABSTRACT FROM AUTHOR]

Heart Disease (HD) has become a major disease that leads to death worldwide. Here, identifying heart disease in the early stage is essential for the patient's survival. While diagnosing heart disease with the ECG signal, it may cause an error due to the small amplitude variation. The highly accurate recognition process is the only option to save human lives. Initially, the gathered signals are decomposed using Adaptive DWT. Then, these decomposed signals are fed to the Deep Convolutional Neural Networks (DCNN) features to get the first set of features. Secondly, the R-R interval is analyzed and taken from the gathered signals and subjected to a deep feature extraction stage, where the DCNN gets the second set of features from R-R intervals. Thirdly, QRS waves are analyzed from the gathered signals and given to the feature extraction stage, where the third set of features are gathered from QRS waves using DCNN. Finally, all these three sets of features are fused for processing heart disease detection, where the ERBF is utilized for getting the classified outcomes. From the overall analysis, the F1-score of the designed approach is 96.03%. Thus, the experimental outcome has ensured the performance with accuracy, F1-score, and AUC. [ABSTRACT FROM AUTHOR]

ardiovascular imaging has become the foundation of heart failure diagnostic studies. The most crucial technique for clinically diagnosing cardiac diseases is echocardiography. Depending on the positioning and angles of the probe, different cardiac views can be obtained during echocardiography. Therefore, the automatic classification of echo views, especially for computer systems and even automatic diagnosis in later stages, is the first step for echocardiogram diagnosis. In addition, the classification of heart views allows the tagging of echo videos to be done on a high scale and the possibility of database management and collection is provided. However, deep learning is an advanced machine learning method that is used to analyze both natural and medical images. But so far, it has not been widely used on cardiac ultrasound, the reason is the complexity of formats with multiple views and multi-view formats of echocardiogram. The proposed topic of this research is to provide novel and effective architectures for cardiac view classification. The aim of this study is to overcome the challenges in processing, categorizing and recognizing echo views stored as videos and images. In particular, in order to extract features, automatic methods and deep networks have replaced manual methods. In the presented solution, by using the transfer learning and the 3d-cnn method in image and video classification, we have improved the accuracy of echo views classification. [ABSTRACT FROM AUTHOR]

We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data. [ABSTRACT FROM AUTHOR]

This article, published in the journal Circulation, discusses the past, present, and future of coronary computed tomography angiography (CCTA) as a diagnostic tool for cardiovascular disease. The essay traces the historical milestones and technological advancements that have shaped cardiac CT, highlighting its ability to provide noninvasive assessment of coronary artery disease and other cardiovascular conditions. The article also discusses the role of CCTA in the evaluation of acute and stable chest pain, as well as its potential for risk stratification and guiding preventive treatments. The authors conclude that ongoing advancements in technology and clinical research will continue to enhance the role of cardiac CT in modern cardiology. [Extracted from the article]

The prenatal diagnosis of fetal heart disease potentially influences parental decision-making regarding pregnancy termination. Existing literature indicates that the severity, whether in complexity or lethality, significantly influences parental decisions concerning abortion. However, questions remain as to how fetal heart disease severity impacts parental decisions, given recent advancements in postsurgical outcomes. Therefore, we investigated risk factors associated with parents' decision-making regarding abortion following a prenatal diagnosis of fetal heart disease. Our analysis included 73 (terminated: n = 37; continued: n = 36) pregnancies with a fetal heart disease diagnosed before 22 weeks of gestation. Increased gestational age at diagnosis reduced the likelihood of parents' decision on termination (Model 1: adjusted odds ratio, 0.94; 95% confidence interval 0.89–0.99; Model 2: 0.95 0.90–0.997). Critical disease (5.25; 1.09–25.19) and concurrent extracardiac or genetic abnormalities (Model 1: 4.19, 1.21–14.53; Model 2: 5.47, 1.50–19.96) increased the likelihood of choosing abortion. Notably, complex disease did not significantly influence parental decisions (0.56; 0.14–2.20). These results suggest that parental decision-making regarding abortion may be influenced by earlier gestational age at diagnosis, the lethality of heart disease, and extracardiac or genetic abnormalities, but not its complexity if prenatal diagnosis and parental counseling are provided at a cardiovascular-specialized facility. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine. [ABSTRACT FROM AUTHOR]

Objectives: Fortilin is a multifunctional protein that protects cells against apoptosis. We aimed to investigate the levels of fortilin in patients with heart failure. Methods: Patients with ejection fraction (EF) below 40% were divided into two groups according to coronary angiography results: those with ischemic heart failure (Group 1) and those with non-ischemic heart failure (Group 2). Patients with normal anatomy and EF over 50% were included in the control group (Group 3). Results: A total of 119 patients were prospectively included in the study. A total of 81 patients (41 patients with ischemic heart failure and 40 patients with non-ischemic heart failure) were included in the heart failure group. 38 patients with EF >50 and normal coronary anatomy were included in the control group. There was no significant difference in serum fortilin levels between the study groups (Group 1: 5.5 ± 2.6 ng/mL, Group 2: 6.1 ± 3.8 ng/mL, and Group 3: 5.6 ± 3.6 ng/mL; P=0.693). Fortilin did not show a correlation with any other variables. Conclusion: In our study, there was no significant difference in fortilin levels between the groups, and no relationship was found between coronary ischemia and fortilin levels in heart failure. [ABSTRACT FROM AUTHOR]

Identifying patients with left ventricular ejection fraction (EF), either reduced [EF < 40% (rEF)], mid-range [EF 40–50% (mEF)], or preserved [EF > 50% (pEF)], is considered of primary clinical importance. An end-to-end video classification using AutoML in Google Vertex AI was applied to echocardiographic recordings. Datasets balanced by majority undersampling, each corresponding to one out of three possible classifications, were obtained from the Standford EchoNet-Dynamic repository. A train–test split of 75/25 was applied. A binary video classification of rEF vs. not rEF demonstrated good performance (test dataset: ROC AUC score 0.939, accuracy 0.863, sensitivity 0.894, specificity 0.831, positive predicting value 0.842). A second binary classification of not pEF vs. pEF was slightly less performing (test dataset: ROC AUC score 0.917, accuracy 0.829, sensitivity 0.761, specificity 0.891, positive predicting value 0.888). A ternary classification was also explored, and lower performance was observed, mainly for the mEF class. A non-AutoML PyTorch implementation in open access confirmed the feasibility of our approach. With this proof of concept, end-to-end video classification based on transfer learning to categorize EF merits consideration for further evaluation in prospective clinical studies. [ABSTRACT FROM AUTHOR]

Background: The existing diagnostic methods for coronary artery disease (CAD), such as coronary angiography and fractional flow reserve (FFR), have limitations regarding their invasiveness, cost, and discomfort. We explored a novel diagnostic approach, coronary contrast intensity analysis (CCIA), and conducted a comparative analysis between it and FFR. Methods: We used an in vitro coronary-circulation-mimicking system with nine stenosis models representing various stenosis lengths (6, 18, and 30 mm) and degrees (30%, 50%, and 70%). The angiographic brightness values were analyzed for CCIA. The in vivo experiments included 15 patients with a normal sinus rhythm. Coronary angiography was performed, and arterial movement was tracked, enabling CCIA derivation. The CCIA values were compared with the FFR (n = 15) and instantaneous wave-free ratio (iFR; n = 11) measurements. Results: In vitro FFR showed a consistent trend related to the length and severity of stenosis. The CCIA was related to stenosis but had a weaker correlation with length, except for with 70% stenosis (6 mm: 0.82 ± 0.007, 0.68 ± 0.007, 0.61 ± 0.004; 18 mm: 0.78 ± 0.052, 0.69 ± 0.025, 0.44 ± 0.016; 30 mm: 0.80 ± 0.018, 0.64 ± 0.006, 0.40 ± 0.026 at 30%, 50%, and 70%, respectively). In vitro CCIA and FFR were significantly correlated (R = 0.9442, p < 0.01). The in vivo analysis revealed significant correlations between CCIA and FFR (R = 0.5775, p < 0.05) and the iFR (n = 11, R = 0.7578, p < 0.01). Conclusions: CCIA is a promising alternative for diagnosing stenosis in patients with CAD. The initial in vitro validation and in vivo confirmation in patients demonstrate the feasibility of applying CCIA during coronary angiography. Further clinical studies are warranted to fully evaluate the diagnostic accuracy and potential impact of CCIA on CAD management. [ABSTRACT FROM AUTHOR]

Objective: Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18–22‐week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA. Methods: We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four‐chamber and three‐vessel views were included in a logistic regression‐based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver‐operating‐characteristics (ROC) curves. Results: At the 18–22‐week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences, with Z‐scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (n = 73) and healthy controls (n = 7300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%. Conclusions: The integration of AI technology with automated cardiac biometric measurements obtained during the 18–22‐week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. Linked article: There is a comment on this article by Drukker. Click here to view the Editorial. [ABSTRACT FROM AUTHOR]

"Eliminate rheumatic fever (RF) and minimize the burden of rheumatic heart disease by 2025" is the goal of World Heart Federation (WHF). The most important step to achieve the goal of WHF is the implementation of the prevention and control of RF in India. The program can be implemented with minimal fund allocation from government making use of the existing manpower in the government and private health sector and schools with the concurrence of National Health Mission, Ministry of Health and Family Welfare, Ministry of Public Education and under the guidance of Cardiological Society of India, National Rheumatic Heart Consortium, Rheumatic Heart Club India, Association of Physicians of India, Indian Academy of Pediatrics, and Association of Otolaryngologists of India. By the successful implementation of this program, the children of 5--15 years in India can be protected from RF. India eradicated smallpox in 1980 and Polio 2012. With this program, we can start our efforts to eliminate RF by 2025. [ABSTRACT FROM AUTHOR]

This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model's focus on critical segments of ECG data, ensuring a nuanced analysis that highlights the most informative features for arrhythmia classification. Evaluated on a comprehensive dataset of 12-lead ECG recordings, our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias, with an accuracy of 98.4%, a precision of 98.1%, a recall of 98%, and an F-score of 98%. This novel combination of convolutional and recurrent neural networks, supplemented by attention-driven mechanisms, advances automated ECG analysis, contributing significantly to healthcare's machine learning applications and presenting a step forward in developing non-invasive, efficient, and reliable tools for early diagnosis and management of heart diseases. [ABSTRACT FROM AUTHOR]

Heart disease, encompassing a range of conditions affecting the heart, remains a leading cause of morbidity and mortality worldwide. The urgent need for precise diagnostic techniques is crucial for improving patient outcomes, as early and accurate diagnosis can significantly influence the effectiveness of treatment and management strategies. This study introduces an innovative approach to diagnosing heart disease by combining classifiers in a meta‐learning‐based approach and utilizing advanced feature selection methods in a hybrid model. Using the extensive heart disease dataset provided by the IEEE DataPort, our method aims to enhance the accuracy of diagnosis by gradually refining the selection of relevant features at two separate stages. Initially, in level 0, a collection of filter‐based algorithms, such as mutual information, relief, ANOVA, systematic uncertainty, and minimum redundancy‐maximum relevance, is utilized to identify a subset of relevant features. Subsequently, the Sequential Forward Selection (SFS) wrapper method is employed in level 1 to further fine‐tune the feature set. The model utilizes a meta‐learning‐based ensemble strategy for classification, combining basic classifiers such as K‐nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), and extreme gradient boost (XGBoost) in level 0. In addition, a logistic regression (LR) meta‐classifier is incorporated at level 1. The proposed hybrid technique has been tested on the heart disease dataset, and the results show that the meta‐learning‐based hybrid feature selection approach performs exceptionally well in terms of performance metrics. The model produces impressive results with only eight precisely chosen features, including an accuracy of 96.2185%, precision of 96.0317%, recall of 96.8%, F1‐score of 96.4143%, and an area under the curve (AUC) of 0.9619. Furthermore, our approach significantly outperforms state‐of‐the‐art techniques, indicating its potential to revolutionize heart disease diagnosis and improve patient care. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) are abundant and closely related to the occurrence and development of human diseases. LncRNAs are known to play a key role in many cardiovascular diseases. The purpose of this study was to investigate the effect of the RNA component of mitochondrial RNA-processing endoribonuclease (RMRP) on the degree of coronary artery lesions and prognosis in patients with coronary artery disease (CAD). Methods: Patients who underwent coronary angiography (CAG) and dynamical-single photon emission computed tomography (D-SPECT) were selected as study subjects, and the results of CAG were reviewed, and the patients were grouped according to SYNTAX score. Evaluate the factors affecting SYNTAX scores. The follow-up analysis was conducted, and the endpoint events were major adverse cardiovascular events (MACEs). Kaplan–Meier method was used to estimate the survival rate, and multivariate Cox regression was used to analyze the relationship between RMRP and MACEs. Results: The expression level of serum RMRP in patients with CAD was significantly higher than that in healthy people. Multivariate Logistic regression analysis showed that age, low-density lipoprotein cholesterol (LDL-C), RMRP and rest left ventricular ejection fraction (LVEF) were independent factors that affected SYNTAX scores. There were 19 cases of MACEs in the high RMRP group and 9 cases in the low RMRP group, and there was a significant difference in the MACE free survival curve between the two groups. Multivariate Cox regression analysis showed that age, SYNTAX score, rest LVEF and RMRP were risk factors for MACEs. Conclusions: Serum RMRP is a key factor affecting the degree of coronary artery disease and prognosis in CAD patients. [ABSTRACT FROM AUTHOR]

Background and Aims Many adult patients with congenital heart disease (ACHD) are still afflicted by premature death. Previous reports suggested natriuretic peptides may identify ACHD patients with adverse outcome. The study investigated prognostic power of B-type natriuretic peptide (BNP) across the spectrum of ACHD in a large contemporary cohort. Methods The cohort included 3392 consecutive ACHD patients under long-term follow-up at a tertiary ACHD centre between 2006 and 2019. The primary study endpoint was all-cause mortality. Results A total of 11 974 BNP measurements were analysed. The median BNP at baseline was 47 (24–107) ng/L. During a median follow-up of 8.6 years (29 115 patient-years), 615 (18.1%) patients died. On univariable and multivariable analysis, baseline BNP [hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.15–1.18 and HR 1.13, 95% CI 1.08–1.18, respectively] and temporal changes in BNP levels (HR 1.22, 95% CI 1.19–1.26 and HR 1.19, 95% CI 1.12–1.26, respectively) were predictive of mortality (P <.001 for both) independently of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. Patients within the highest quartile of baseline BNP (>107 ng/L) and those within the highest quartile of temporal BNP change (>35 ng/L) had significantly increased risk of death (HR 5.8, 95% CI 4.91–6.79, P <.001, and HR 3.6, 95% CI 2.93–4.40, P <.001, respectively). Conclusions Baseline BNP and temporal BNP changes are both significantly associated with all-cause mortality in ACHD independent of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. B-type natriuretic peptide levels represent an easy to obtain and inexpensive marker conveying prognostic information and should be used for the routine surveillance of patients with ACHD. [ABSTRACT FROM AUTHOR]

Introduction: Objective: Prenatal detection of congenital heart disease (CHD) using fetal echocardiography (FE) helps in early diagnosis leading to prompt management and treatment. FE provides a highly accurate non-invasive modality to improve the survival or quality of life of CHD patients. The aim of this study was to evaluate the antenatal detection of CHD by FE and compare it with the results of postnatal echocardiography. Methods: A prospective cohort study of pregnant women referred to a tertiary center Imam-Reza Hospital, Mashhad, Iran, for performing FE in the hands of an experienced pediatric cardiologist between 2012 and 2021. Cardiac echocardiography was performed by GE Vivid 7 color Doppler and Mindray Resona 7 color Doppler with convex probe 5-7 megahertz during late first trimester or early second trimester and after birth until 2 months later. Data were analyzed using SPSS and MedCalc software, and agreement was assessed using kappa. Results: Out of 261 studied fetuses, 101 normal cases were detected in full agreement with postnatal echo diagnosis. Acceptable diagnosis was found for septal defects; VSDs were highly statistically detected (sensitivity= 90%, specificity= 93%). Complex CHDs were found to be the most accurate prenatal diagnosis. Right arch anomalies, aortic stenosis, hypoplastic left heart syndrome and cardiac masses were perfectly acceptable, but detection of coarctation of the aorta faced with over-diagnosis. Prenatally diagnosed arrhythmias without structural defects, mostly premature beats, shifted to normal postnatal echo. Conclusion: FE is a safe and sensitive modality in prenatal diagnosis of CHDs. The study showed the effectiveness accuracy of early first trimester; also complete detection in both sides of the defect spectrum. [ABSTRACT FROM AUTHOR]

Globally, cardiovascular diseases stand as the primary cause of mortality. In response to the imperative to enhance operational efficiency and reduce expenses, healthcare organizations are currently undergoing a transformation. The incorporation of analytics into their IT strategy is vital for the successful execution of this transition. The approach involves consolidating data from various sources into a data lake, which is then leveraged with analytical models to revolutionize predictive analytics. The deployment of IoT-based predictive systems is aimed at diminishing mortality rates, particularly in the domain of coronary heart disease prognosis. However, the abundant and diverse nature of data across various disciplines poses significant challenges in terms of data analysis, extraction, management, and configuration within these large-scale data technologies and tools. In this context, a multi-level fuzzy rule generation approach is put forward to identify the characteristics necessary for heart disease prediction. These features are subsequently trained using an optimized recurrent neural network. Medical professionals assess and categorize the features into labeled classes based on the perceived risk. This categorization allows for early diagnosis and prompt treatment. In comparison to conventional systems, the proposed method demonstrates superior performance. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the prevalence of children aged 0–19 years who have a parent with a history of heart disease and investigate their sociodemographic characteristics. A national register-based study. From the Danish Fertility Register and the Danish National Patient Register information on children of parents with ischemic heart disease, arrhythmia, heart failure and heart valve disease in the period 1981–2018 were obtained. Statistical analyses including descriptive statistics, logistic and linear regression were used to illuminate associations between parental heart disease and sociodemographic characteristics. The study population consisted of 142,480 children aged 0–19 years with at least one parent diagnosed with heart disease, corresponding to every 9th child in Denmark in 2018. The number increased from 4.5% in 2002 to 11.1% in 2018. In the study population most had a father with heart disease (57.8%) and 4.6% had two parents with heart disease. Parents with heart disease had significantly higher odds of being out of work (OR 1.68, 95% CI 1.64; 1.72), in a single-parent household (OR 1.09, 95% CI 1.07; 1.11), divorced or widowed (OR: 1.10, 95% CI 1.08; 1.12), having a lower educational level (OR 1.35, 95% CI 1.33; 1.37), and a lower family income (−42,410 DKR, 95% CI -50,306; −34,514, P < 0.0001) compared to those without heart disease. Children affected by parental heart disease comprise a substantial part of the Danish population. These have significantly different sociodemographic characteristics than children in families without parental heart disease, which might affect social heritage and parental capacity. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we aimed to analyze the layer-specific strain values obtained by speckle tracking echocardiography (STE) method in the determination of subclinical cardiac dysfunction in rheumatoid arthritis (RA) patients. Patients and methods: Between February 2019 and October 2019, a total of 63 female RA patients (mean age: 51.82±6.07 years; range, 40 and 65 years) who had a confirmed diagnosis were included. Thirty-one age-matched female healthy individuals (mean age: 50.71±5.37 years; range, 40 and 65 years) were selected as the control group. The patients were divided into three groups according to the duration of disease as <5 years, 5-10 years and >10 years. The Disease Activity Score in 28 joint - C-reactive protein (CRP) was used to determine disease activation. The standard assessment included complete serum CRP, anti-cyclic citrullinated peptide, rheumatoid factor, N-terminal pro B-type natriuretic peptide (NT-proBNP), and homocysteine. Global longitudinal strain (GLS) analysis was performed with STE. Results: The NT-proBNP values were found to be higher in RA patients compared to the control group (p=0.044). In terms of conventional echocardiographic parameters, a significant difference between E/A and E/E' ratios was observed (p<0.001 and p=0.015). Endocardium, transmural, and epicardium GLS values obtained by STE were found to be lower in RA patients (p<0.05). The left ventricular (LV) GLS values worsened, as the duration of disease increased (p<0.05). There was a significant correlation between RA disease activity and LV GLS values, showing that increasing levels of disease activity was associated with worse LV GLS (r=0.583, p<0.01 and r=0.681, p<0.01 and r=0.689, p<0.01 for endocardium, transmural and epicardium, respectively). Conclusion: Our study results suggest that the layer-specific GLS values obtained by STE decrease in RA patients. [ABSTRACT FROM AUTHOR]

Aims: (a) To investigate support for caregivers of people diagnosed with stroke, cancer, chronic obstructive pulmonary disease (COPD), dementia, or heart disease provided across healthcare settings in Denmark; (b) to assess differences in caregiver support across diagnoses and settings. Methods: A cross-sectional nationwide survey among professionals representing healthcare settings at municipalities (n = 479) and hospital wards and outpatient clinics (n = 425). The survey assessed identification of caregivers and support initiatives. Results: The response rate was 81% for municipalities and 49% for hospitals. Identification of caregivers was frequent in dementia care (81% and 100%) and less frequent in COPD care (58% and 64%) in municipalities and hospitals, respectively. Caregiver support differed significantly across diagnoses within municipalities (p = 0.009) and hospitals (p < 0.001). Systematic identification of vulnerable caregivers was <25% for all diagnoses except dementia. The most common support initiatives involving caregivers were primarily aimed at the ill person and included guidance about the disease and consequences for everyday life and lifestyle changes. Caregivers were least involved in support initiatives addressing physical training, work retention, sexuality, or cohabitation. Conclusions: Disparities and significant differences across diagnoses exist in the identification of caregivers and the provision of support initiatives. Support initiatives involving caregivers primarily targeted patients. Future studies should investigate how caregivers' needs can be met across different diagnoses and healthcare settings and investigate potential changes in caregivers' needs during disease trajectories. In clinical practice, identification of vulnerable caregivers should be a major focus, and disease-specific clinical guidelines may be required to ensure sufficient support for caregivers. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imperceptible to human clinicians. By leveraging a comprehensive dataset encompassing various patient profiles, the ML model is poised to significantly enhance the precision, speed, and efficiency of heart disease detection. The findings of this paper hold promise for fostering more effective intervention strategies and improving patient care outcomes in the realm of cardiovascular health. [ABSTRACT FROM AUTHOR]

Background: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. Methods: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. Results: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. Conclusions: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features—age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality—and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately. [ABSTRACT FROM AUTHOR]

The incidence rate and mortality rate of coronary heart disease are rising, which seriously threatens people's health. Effective diagnosis of coronary heart disease can improve the quality of prognosis for patients. To analyze the diagnostic value of 24-hour dynamic electrocardiogram in the intervention of coronary heart disease, arrhythmia, and ischemic myocardium, this study recruited 64 patients with coronary heart disease who underwent treatment from April 2021 to April 2022 in the hospital. The random classification method was adopted to randomly divide 64 patients into research and control groups with 32 people in each group. The research group applied 24-hour dynamic electrocardiogram for diagnosis, while the control group received routine electrocardiogram for diagnosis. The detection rates of arrhythmia, myocardial ischemia, ST downshift amplitude, maintenance time, and satisfaction of different diagnostic methods were compared. The results showed that, after applying different diagnostic methods, the 24-hour dynamic electrocardiogram used by the research group demonstrated significantly higher detection rates for arrhythmia and myocardial ischemia compared to that of conventional electrocardiogram in control group (P < 0.05). At the same time, the difference of the detection rate for different types of arrhythmias was also observed with significant difference (P < 0.05). In terms of ST downshift amplitude and maintenance time, 24-hour dynamic electrocardiogram outperformed conventional method (P < 0.05). In addition, the satisfaction with 24-hour dynamic electrocardiogram exceeded conventional detection method (P < 0.05). The results confirmed that 24-hour dynamic electrocardiogram had significant diagnostic effect on coronary heart disease, arrhythmia, and myocardial ischemia with high detection rate and low missed detection rate. The operation is simple and flexible, which has great promotional value. [ABSTRACT FROM AUTHOR]

Coronary artery three‐dimensional reconstruction is essential for preventing, diagnosing, and treating coronary heart disease. This study introduces SegUnet, a lightweight hybrid CNN‐Transformer network for pixel‐level segmentation of coronary artery computed tomography angiography (CTA) slices to enhance the precision of coronary artery reconstruction. The overall SegUnet adopts a U‐shaped encoder‐decoder structure, with a hierarchical Transformer structure serving as the encoder to extract global contextual information; the decoder is enhanced with spatial attention mechanism (SAM) and residual structure to improve its perception and adaptive adjustment abilities for coronary artery vessels. In contrast to other methods, we propose an interpolation‐based multi‐feature fusion bridge to integrate multi‐scale features between encoder levels, capturing their semantic dependencies. Experimental results show that SegUnet achieves a prediction accuracy of 97.23% on the test set of coronary arteries 2D slices. Moreover, the network exhibits excellent performance on Synapse Multi‐Organ Segmentation, highlighting the superiority, effectiveness, and robustness of our proposed method. The outstanding performance of SegUnet in coronary artery segmentation has elevated the quality of three‐dimensional reconstruction of the coronary arteries, exerting a positive impact on the enhancement of diagnosis and treatment of coronary artery diseases. [ABSTRACT FROM AUTHOR]

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis. [ABSTRACT FROM AUTHOR]