Diagnosis Of Heart Disease Research Articles

Progress And Challenges of Coronary Angiography in The Diagnosis and Treatment of Coronary Heart Disease

Progress And Challenges of Coronary Angiography in The Diagnosis and Treatment of Coronary Heart Disease

The Influence of Various Types of Physical Activity on Blood Pressure. A Literature Review

Blood pressure is a key indicator of cardiovascular health, playing a significant role in the diagnosis, prevention, and treatment of heart and vascular diseases. In recent years, interest in the influence of physical activity on blood pressure has increased among researchers and health-conscious individuals. Among the studies presented, researchers examined the effects of various types of physical activity on blood pressure in individuals with hypertension and the level of hypertension control, as well as other components affecting the overall health of patients, such as blood lipid levels, arterial pressure variability over 24 hours, and BMI. The conclusions of the presented studies seemed to be consistent regarding the positive impact of engaging in physical activity on blood pressure and thus the better well-being of patients.

Ancestry specific distribution of LPA Kringle IV-Type-2 genetic variants highlight associations to apo(a) copy number, glucose, and hypertension.

High Lp(a) levels contribute to atherosclerotic cardiovascular disease and are tightly regulated by the LPA gene. Lp(a) levels have an inverse correlation with LPA Kringle IV Type-2 (KIV-2) copy number (CN). Black (B) and Hispanic (H) individuals exhibit higher levels of Lp(a), and rates of CVD compared to non-Hispanic Whites (NHW). Therefore, we investigated genetic variations in the LPA KIV-2 region across three ancestries and their associations with metabolic risk factors. Using published pipelines, we analyzed a multi-ethnic whole exome dataset comprising 3,817 participants from the Washington Heights and Inwood Columbia Aging Project (WHICAP): 886 [NHW (23%), 1,811 Caribbean (C) H (47%), and 1,120 B individuals (29%). Rare and common variants (alternative allele carrier frequency, CF < 0.01 or > 0.99 and 0.01 < CF < 0.99, respectively) were identified and KIV-2 CN estimated. The associations of variants and CN with history of heart disease, hypertension (HTN), stroke, lipid levels and clinical diagnosis of Alzheimer's disease (AD) was assessed. A small pilot provided in-silico validation of study findings. We report 1421 variants in the LPA KIV-2 repeat region, comprising 267 exonic and 1154 intronic variants. 61.4% of the exonic variants have not been previously described. Three novel exonic variants significantly increase the risk of HTN across all ethnic groups: 4785-C/A (frequency = 78%, odds ratio [OR] = 1.45, p = 0.032), 727-T/C (frequency = 96%, OR = 2.11, p = 0.032), and 723-A/G (frequency = 96%, OR = 1.97, p = 0.038). Additionally, six intronic variants showed associations with HTN: 166-G/A, 387-G/C, 402-G/A, 4527-A/T, 4541-G/A, and 4653-A/T. One intronic variant, 412-C/T, was associated with decreased blood glucose levels (frequency = 72%, β = -14.52, p = 0.02). Three of the associations were not affected after adjusting for LPA KIV-2 CN: 412-C/T (β = -14.2, p = 0.03), 166-G/A (OR = 1.41, p = 0.05), and 387-G/C (OR = 1.40, p = 0.05). KIV CN itself was significantly associated with 314 variants and was negatively correlated with plasma total cholesterol levels. In three ancestry groups, we identify novel rare and common LPA KIV-2 region variants. We report new associations of variants with HTN and Glucose levels. These results underscore the genetic complexity of the LPA KIV-2 region in influencing cardiovascular and metabolic health, suggesting potential genetic regulation of pathways that can be studied for research and therapeutic interventions. Lp(a) levels are mostly controlled by the LPA gene and are higher in Blacks and Hispanics. Novel LPA KIV-2 variants found in three ancestry groups, including data on Caribbean Hispanics, show strong positive associations to hypertension and negative associations to glucose levels. Further characterization of these variants and identifying links to disease can help precision medicine efforts to understand disease mechanisms in all populations.

Impact of preanaesthetic electrocardiogram on decision making and modification of anaesthetic protocols in dogs.

This retrospective observational study explored the impact of preanaesthetic electrocardiogram (ECG) assessment on preoperative echocardiography requests and modifications to a standardised anaesthetic protocol in healthy dogs. A total of 228 healthy dogs with no previously diagnosed heart disease that underwent general anaesthesia at Complutense Veterinary Teaching Hospital from December 2017 to June 2018 were included. Preanaesthetic ECGs were assessed for abnormalities, and the findings were documented. The number of dogs requiring echocardiography, based on ECG findings, and the echocardiography results were recorded. All anaesthesia-related decisions were documented. Overall, 72 dogs (31.6%) exhibited ECG abnormalities. Echocardiography was requested for five dogs (2.2%). The anaesthetic protocol was changed in 11 dogs (15.3% of those with ECG abnormalities). P wave disturbances, ventricular premature complexes and impulse conduction issues were abnormalities that prompted echocardiography. Bradycardia and electrical impulse conduction abnormalities influenced protocol modifications. The limited sample size meant that it was not possible to investigate potentialcorrelations between demographics and ECG alterations. Preanaesthetic ECG screening was useful forpromoting echocardiography and influencing anaesthesia plans in a subset of dogs. Despite this, further assessment of the impact of routine use of non-targeted preoperative ECG on anaesthesia-related outcomes is warranted.

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy. This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets. The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.

Cloud-Connected Patch-Worn Auscultation Device for Chest Sound Monitoring.

AbstractIn recent years, wearable electrocardiograms have risen in popularity as a solution for personal monitoring of heart activity. However, this technology has limitations in diagnostic capability and structural function monitoring. Meanwhile, auscultation of the heart remains a fundamental tool for physicians in diagnosis and monitoring of heart disease largely unaddressed in a convenient wearable format. The present work outlines a promising system currently under investigation, allowing user-initiated 10-second chest-sound recordings to be transmitted over Bluetooth-Low-Energy, with an innovative package design providing inherent noise reduction and a high signal-to-noise ratio. The device has been tested on healthy individuals, and system response has been validated against calibrated electrocardiogram recording equipment to analyze signal capture fidelity.Abstract Figure

Rancang Bangun Aplikasi Sistem Pakar Diagnosis Penyakit Jantung: Studi Kasus pada Klinik Siaga Menggunakan Metode Case-Based Reasoning (CBR) Berbasis Web

In the era of modernization, technological advancements are developing rapidly and play an essential role in various aspects of life, including healthcare. Technology enables people to easily access information in fields such as business, education, and health. In the medical field, computer-based expert systems assist professionals in diagnosing diseases and solving specific problems. This study focuses on developing an expert system application for diagnosing heart diseases using the Case-Based Reasoning (CBR) method, implemented as a web-based system with PHP and MySQL. The heart, a vital organ responsible for pumping blood, is slightly larger than a fist (around 200–425 grams). According to Dr. Helmanu Kurniadi and Ulfa Nurrahmani, S.Kep., smoking is identified as one of the contributing factors to heart disease. The developed application consists of five primary elements, with several menus such as the homepage, user data, disease information, disease list, and login page. Based on black box testing, the application has been thoroughly tested to ensure it functions correctly and is free from coding errors. Additionally, acceptance testing was conducted eight times, resulting in a 100% accuracy rate, aligning with the intended design. Thus, the application is ready to be deployed and is expected to assist both the public and healthcare professionals in diagnosing heart diseases effectively.

Cohort study of prediction of venous thromboembolism in emergency department patients with extremity symptoms.

Despite diagnostic algorithms, identification of venous thromboembolism (VTE) in emergency departments (ED) remains a challenge. We evaluated symptoms, background, and laboratory data in 27,647 ED patients presenting with pain, swelling, or other symptoms from the extremities, and identified predictors of VTE diagnosis within one year. Predictors of a clinical decision to perform phlebography, ultrasound, or computer tomography (CT) angiography of pelvic, lower, or upper extremity veins, CT of pulmonary arteries, or pulmonary scintigraphy at the ED or within 30days, and the results of such investigations were also evaluated. A total of 3195 patients (11.6%) were diagnosed with VTE within one year. In adjusted analysis of patients in whom all laboratory data were available, a d-dimer value ≥ 0.5mg/l (odds ratio [OR]: 2.602; 95% confidence interval [CI] 1.894-3.575; p < 0.001) at the ED and a previous diagnosis of VTE (OR: 6.037; CI 4.465-8.162; p < 0.001) independently predicted VTE within one year. Of diagnosed patients, 2355 (73.7%) had undergone imaging within 30days after the ED visit and 1730 (54.1%) were diagnosed at this examination. Lower age (OR: 0.984; CI 0.972-0.997; p = 0.014), higher blood hemoglobin (OR: 1.023; CI 1.010-1.037; p < 0.001), C-reactive protein (OR: 2.229; CI 1.433-3.468; p < 0.001), d-dimer (OR: 8.729; CI 5.614-13.574; p < 0.001), and previous VTE (OR: 7.796; CI 5.193-11.705; p < 0.001) predicted VTE on imaging within 30days, whereas female sex (OR 0.602 [95% CI 0.392-0.924]; p = 0.020) and a previous diagnosis of ischemic heart disease (OR 0.254 [95% CI 0.113-0.571]; p = 0.001) were negative predictors of VTE. In conclusion, analysis of 27,647 ED patients with extremity symptoms confirmed the importance of well-established risk factors for VTE. Many patients developing VTE within one year had initial negative imaging, highlighting the importance of continued symptom vigilance.

Association between parental decisions regarding abortion and severity of fetal heart disease

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.

Lower Income, Smoking, Cardiopulmonary Comorbidities, and Higher Symptom Burden Influence the Occurrence of Cough in Patients Receiving Chemotherapy.

To identify subgroups of patients with distinct cough occurrence profiles and evaluate for differences among these subgroups. Outpatients receiving chemotherapy (N = 1,338) completed questionnaires six times over two chemotherapy cycles. Occurrence of cough was assessed using the Memorial Symptom Assessment Scale. Latent class analysis was used to identify subgroups with distinct cough occurrence profiles. Parametric and nonparametric tests were used to evaluate for differences. Four distinct cough profiles were identified (None, Decreasing, Increasing, and High). Risk factors associated with membership in the High class included lower annual household income; history of smoking; self-reported diagnoses of lung disease, heart disease, and back pain; and having lung cancer. Clinicians need to assess all patients with cancer for cough and provide targeted interventions.

A proposed heart disease diagnosis based on Deep learning.

A proposed heart disease diagnosis based on Deep learning.

Enhancing Heart Disease Classification: Performance Evaluation of the Machine Learning Model

Heart disease, mainly caused by coronary artery disease, poses a significant health challenge by impeding oxygen-rich blood flow to the heart muscle. Addressing this, our study utilizes a dataset of 1,026 cases from Kaggle, focusing on the application of MultiLayer Perceptron (MLP) in diagnosing heart conditions. This dataset, rich in attributes indicative of heart disease, comprises 14 variables, with the class variable determined by 13 attributes acting as the focal point for disease classification. Our methodology involved applying various machine learning algorithms, with a particular emphasis on the MLP model, to categorize cases as diseased or non-diseased. The MLP model demonstrated superior performance, achieving an accuracy of 92.39% (AUC=0.923), and outperforming several comparative techniques in terms of precision, recall, and mean squared error (MSE). The analysis of the dataset under this model revealed insightful patterns and trends, underscoring the effectiveness of MLP in heart disease diagnosis and potentially guiding future research in medical diagnostics.

Advances in Diagnosis and Management of Fetal Heart Disease

Advances in Diagnosis and Management of Fetal Heart Disease

Cirrhotic cardiomyopathy – Clinically fact or academic curiosity? Review. Part 2: ECG, functional tests, images, biomarkers, screening for coronary heart disease and differentianting diagnosis

The diagnosis of Cirrhotic Cardiomyopathy is based on severe hepatic cirrosis with deterioration of cardiac function without previous cardiopathy, but this is subclinical during a long time. In this second part we review the non-invasive diagnostic methods and their prognostic value in patients with or without hepatic transplant, from ECG to cardiac images of magnetic resonance.

Serum cystatin C, monocyte/high-density lipoprotein-C ratio, and uric acid for the diagnosis of coronary heart disease and heart failure

BACKGROUND Coronary heart disease (CHD) and heart failure (HF) are the major causes of morbidity and mortality worldwide. Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis. However, conventional diagnostic methods such as electrocardiography, echocardiography, and cardiac biomarkers have certain limitations, such as low sensitivity, specificity, availability, and cost-effectiveness. Therefore, there is a need for simple, noninvasive, and reliable biomarkers to diagnose CHD and HF. AIM To investigate serum cystatin C (Cys-C), monocyte/high-density lipoprotein cholesterol ratio (MHR), and uric acid (UA) diagnostic values for CHD and HF. METHODS We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023. The patients were divided into CHD (n = 20), HF (n = 20), CHD + HF (n = 20), and control groups (n = 20). The serum levels of Cys-C, MHR, and UA were measured using immunonephelometry and an enzymatic method, respectively, and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Serum levels of Cys-C, MHR, and UA were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group. The serum levels of Cys-C, MHR, and UA were significantly higher in the CHD + HF group than those in the CHD or HF group. The ROC curve analysis showed that serum Cys-C, MHR, and UA had good diagnostic performance for CHD and HF, with areas under the curve ranging from 0.78 to 0.93. The optimal cutoff values of serum Cys-C, MHR, and UA for diagnosing CHD, HF, and CHD+HF were 1.2 mg/L, 0.9 × 109, and 389 µmol/L; 1.4 mg/L, 1.0 × 109, and 449 µmol/L; and 1.6 mg/L, 1.1 × 109, and 508 µmol/L, respectively. CONCLUSION Serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF, and CHD+HF. These can provide information for decision-making and risk stratification in patients with CHD and HF.

Feline aortic thromboembolism with and without congestive heart failure did not exhibit hypercoagulability using a novel viscoelastic coagulation monitor.

To demonstrate hypercoagulability with a benchtop viscoelastic monitor in cats with congestive heart failure (CHF) and/or aortic thromboembolism (ATE) compared to controls. 97 cats were enrolled throughout this prospective observational cohort study from September 2022 through October 2023. Cats were grouped by diagnosis of CHF, ATE, ATE plus CHF, or controls. Enrollment required diagnosis of heart disease and no previous antithrombotic therapy. The results of viscoelastic testing with the benchtop viscoelastic coagulation monitor (VCM Vet [VCM]; Entegrion) were compared between groups using factorial analysis of variance. Cats with heart disease had significantly higher clot times when compared to controls (control: mean, 285.3 [SD, 172.6]; CHF: mean, 391.7 [SD, 106.8]; ATE: mean, 415.9 [SD, 109.2]; and ATE plus CHF: mean, 368.6 [SD, 232.6]). Heart disease cats were noted to have significantly lower 45-minute lysis index values (control: median, 100 [range, 93 to 100]; CHF: median, 99 [range, 89 to 100]; ATE: median, 98 [range, 88 to 100]; and ATE plus CHF: range, 98 [91 to 100]). Age was a covariate to this variable, and when applied to analysis, statistical significance was lost. No significant difference in any other variables were noted. The hypercoagulability of ATE and CHF cats was not detected by the VCM. Further research with other coagulation monitors is required in this population.

Diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease

Aims/Background Coronary heart disease is a common disease in the elderly and has a complex pathogenesis, which complicates the clinical diagnostic process. Thus, enhancing the diagnostic efficiency for coronary heart disease is imperative to improve the life expectancy of the elderly. This study aimed to explore the diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease. Methods The medical records of 421 patients with suspected coronary heart disease obtained from the geriatric department of the First Affiliated Hospital of Hebei North University from February 2020 to February 2023 were retrospectively analysed. After excluding 10 patients who did not meet the inclusion criteria, the remaining 411 patients were included in this study. The included subjects had undergone coronary computed tomography angiography and were divided into coronary heart disease group (n=208) and non-coronary heart disease group (n=203) according to the diagnostic results. Multimodal cardiovascular imaging (coronary computed tomography angiography and echocardiography) and detection of serum biomarkers such as small dense low-density lipoprotein, lipoprotein a, and gamma-glutamyl transferase were performed in both groups. The clinical indicators of the two groups were compared, and the combined diagnostic efficacy of multimodal cardiovascular imaging and biomarker detection was evaluated. Results Compared to the non-coronary heart disease group, the coronary heart disease group had significantly higher levels of maximum area stenosis, total plaque volume, total plaque burden and fibrotic plaque volume (p &lt; ..001), and lower left ventricular ejection fraction level (p &lt; ..001). Additionally, the coronary heart disease group exhibited higher levels of left ventricular end-diastolic volume, left ventricular end-systolic volume and stroke volume than the non-coronary heart disease group (p &lt; ..001), and had higher levels of small dense low-density lipoprotein, lipoprotein a and gamma-glutamyl transferase (p &lt; ..001). Our results demonstrated that combined diagnosis had better diagnostic efficacy than individual approaches, marked by higher area under the curve and sensitivity of the former (p &lt; ..001). Conclusion Multimodal cardiovascular imaging technology combined with biomarker detection can distinctly improve the accuracy of coronary heart disease diagnosis in elderly patients.

Fuzzy rules-based Data Analytics and Machine Learning for Prognosis and Early Diagnosis of Coronary Heart Disease

Fuzzy rules-based Data Analytics and Machine Learning for Prognosis and Early Diagnosis of Coronary Heart Disease

Advancing Precision Medicine with Nanosensor: Emerging Technologies for Early Diagnosis of Heart Disease

Abstract: Precision medicine has emerged as a promising approach for personalized healthcare, particularly in the field of cardiovascular diseases (CVDs). Diagnostic approach in early basis plays a crucial impact in improving patient outcomes, as it permits timely intervention and tailored treatment strategies. Nanosensors, with their unique properties and capabilities, offer groundbreaking opportunities for detection in the primary phase and as well as disease monitoring. The basic fundamental principles of Nanosensors are important to understand the mechanism of working and utilization of the technique. Cardiac diseases are the foremost origin of illness and mortality all over the globe. The physical, biological and pathophysiology of CVDs can be precisely identified by appropriate and exact detection of pertinent biomarkers and function limits. Nanosensors basically incorporate the benefit of Nanomaterials for detection methods. Nanosensors generally exposed a great possible approach for speedy detection of CVDs, specifically in primary prophecy. Advancement of current therapy in Nanosensors for recognition of cardiac disease are briefed, including different types of sensors such as optical, electrochemical, paper-based and pressure Nanosensors. Though Nanoparticles raise the impact on health, there are some limitations in scalability, and standardization of the Nanosensor having good selectivity, efficacy, sensitivity, biocompatibility and limit of detection as compared to the conventional way. Project tactics for various Nanosensors via conforming identifying Nanomaterials, with their mechanistic approach, highlight the potential of precision medicine as well as Nanosensor.

A Coupling Physics Model for Real-Time 4D Simulation of Cardiac Electromechanics

Cardiac simulators can assist in the diagnosis of heart disease and enhance human understanding of this leading cause of mortality. The coupling of multiphysics, such as electrophysiology and active–passive mechanics, in the simulation of the heart poses challenges in utilizing existing methodologies for real-time applications. The low efficiency of physically-based simulation is mostly caused by the need for electrical-stress conduction to use tiny time steps in order to prevent numerical instability. Additionally, the mechanical simulation experiences sluggish convergence when dealing with significant deformation and stiffness, and there are also concerns regarding volume inversion. We provide a coupling physics model that transforms the active–passive dynamics into multiphysics solving constraints, aiming at boosting the real-time efficiency of the cardiac electromechanical simulation. The multiphysics processes are initially divided into two levels: cell-level electrical stimulation and organ-level electrical-stress diffusion/conduction. This separation is achieved by employing operator splitting in combination with the quasi-steady-state method, which simplifies the system equations. Next, utilizing spatial discretization, we employ the matrix-free conjugate gradient approach to solve the electromechanical model, therefore improving the efficiency of the simulation. The experimental results illustrate that our simulation model is capable of replicating intricate cardiac physiological phenomena, including 3D spiral waves and rhythmic contractions. Moreover, our model achieves a significant advancement in real-time computation while maintaining a comparable level of accuracy to current methods. This improvement is advantageous for interactive medical applications.