Different Types Of Heart Disease Research Articles

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

Current Role and Future Perspectives of Cardiac Rehabilitation in Heart Disease.

As a comprehensive secondary prevention program, cardiac rehabilitation (CR) is a beneficial and cost-effective intervention for patients with heart disease, but the participation rate of patients in CR is low globally. In recent years, due to the COVID-19 pandemic and scientific and technological advances, an increasing number of alternative CR modes have been developed, such as remote CR, home-based CR, hybrid CR and virtual CR. These alternative CR modes represent changes and new opportunities for patients with heart disease. In this review, we will discuss in detail the impact of CR on patients with different types of heart disease, review the various alternative CR models, and explore some prospects for the future of CR in the field of heart disease.

Native T1- and T2-mapping of myocardial relaxation times in cardiovascular magnetic resonance: clinical applications

Cardiovascular magnetic resonance is widely used due to its possibilities for noninvasive cardiac tissue characterization. In recent years native myocardial T1- and T2-mapping techniques have been implemented in routine practice. These techniques provide quantitative analysis of myocardium and allow detection and evaluation of myocardial damage without the need for gadolinium-based contrast agents. In this review we would like to discuss application of native T1- and T2-mapping in different types of heart disease.

The influence of arm positions on mechanical dyssynchrony measured by gated myocardial perfusion imaging.

In routine procedures, patient's arms are positioned above their heads to avoid potential attenuation artifacts and reduced image quality during gated myocardial perfusion imaging (G-MPI). However, it is difficult to achieve this action in the acute period following pacemaker implantation. This study aimed to explore the influence of arm positioning on myocardial perfusion imaging (MPI) in different types of heart disease. This study was conducted retrospectively. A total of 123 patients were enrolled and underwent resting G-MPI using a standard protocol with arms positioned above their heads and again with their arms at their sides. All individuals were divided into 3 groups: the normal group, the obstructive coronary artery disease (O-CAD) group, and the dilated cardiomyopathy (DCM) group. The G-MPI data were measured by QGS software and Emory Reconstruction Toolbox, including left ventricular ejection fraction (LVEF), end-diastolic volume (EDV), end-systolic volume (ESV), extent, total perfusion deficit (TPD), summed rest score (SRS), scar burden, phase standard deviation (SD), and phase histogram bandwidth (BW). In total, extent, TPD, EDV, ESV, LVEF, systolic SD, systolic BW, diastolic SD, and diastolic BW were all significantly different between the 2 arm positions (all P<0.01). On the Bland-Altman analysis, both EDV and ESV with the arm-down position were significantly underestimated (P<0.001). Meanwhile, TPD, extent, and LVEF with the arm-down position were significantly overestimated (P<0.05). Systolic SD, systolic BW, diastolic SD, and diastolic BW were systematically overestimated (P<0.001). In the DCM group (n=52), EDV, ESV, systolic SD, systolic BW, diastolic SD, and diastolic BW were identified as significantly different by the paired t-test between the 2 arm positions (P<0.05). In the O-CAD group (n=32), scar burden, ESV, LVEF, and diastolic BW were significantly different between the 2 arm positions (P<0.05). Systolic and diastolic dyssynchrony parameters and most left ventricular (LV) functional parameters were significantly influenced by arm position in both normal individuals and patients with heart failure (HF) with different pathophysiologies. More attention should be given to LV dyssynchrony data during clinical evaluation of cardiac resynchronization therapy (CRT) implantation procedure.

ECG Signal Denoising Method Based on Disentangled Autoencoder

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting.

The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.

Insight into global research on health literacy and heart diseases: A bibliometric analysis.

Health literacy (HL) has shown its important role on reducing the burden of heart diseases. However, no study has provided a comprehensive worldwide view of the data regarding HL and heart diseases. The study aimed to provide insight into: (1) the intellectual structure, (2) research trends, and (3) research gaps on HL and heart diseases; and (4) to explore HL scales commonly utilized in heart studies. Studies related to HL and heart diseases were retrieved from Web of Science, Scopus, and PubMed. All publications published between 2000 and 2021 were included after conducting keyword searches on "heart diseases" in general or on specific types of heart diseases (e.g., "heart failure") and "health literacy". Bibliometric analyses were carried out using the Bibliometrix R package and VOSviewer 1.6.14. A total of 388 original research articles and reviews on HL and heart diseases were included in our study. The studies were primarily conducted in the United States and developed countries. A total of 337 studies (86.9%) focused on heart failure (200 studies, 51.5%) and ischemic heart diseases (137 studies, 35.3%). Sixty-two studies (16.0%) focused on other heart diseases (e.g., valvular diseases and rheumatic heart diseases). The number of interventional studies was limited (52 studies, 13.4%) and fluctuated from 2000 to 2021. The most common questionnaires measuring health literacy among patients with heart diseases were the Test of Functional Health Literacy in Adults (TOFHLA), Short Test of Functional Health Literacy in Adults (STOFHLA), and Brief Health Literacy Screen (BHLS). Use of the eHealth Literacy Scale (eHEALS) has become the latest trend among patients with heart diseases. Health literacy and heart diseases were most often studied in the United States and developed countries. Several HL tools were used; eHEALS has been lately used in this field. These findings suggest the need to conduct more empirical studies on HL and heart diseases in different settings (e.g., developing or poor countries) and with different types of heart diseases (e.g., valvular and rheumatic disorders). Additionally, it is necessary to develop heart disease-specified HL scales for research and practice.

Cardiac fibroblast sub-types in vitro reflect pathological cardiac remodeling in vivo

Many heart diseases are associated with fibrosis, but it is unclear whether different types of heart disease correlate with different subtypes of activated fibroblasts and to which extent such diversity is modeled during in vitro activation of primary cardiac fibroblasts. Analyzing the expression of 82 fibrosis related genes in 65 heart failure (HF) patients, we identified a panel of 12 genes clearly distinguishing HF patients better from healthy controls than measurement of the collagen-related hydroxyproline content. A subcluster enriched in ischemic HF was recognized, but not for diabetes, high BMI, or gender. Single-cell transcriptomic analysis of in vitro activated mouse cardiac fibroblasts distinguished 6 subpopulations, including a contractile Acta2high precursor population, which was predicted by time trajectory analysis to develop into Acta2low subpopulations with high production of extracellular matrix molecules. The 12 gene profile identified in HF patients showed highest similarity to the fibroblast subset with the strongest expression of extracellular matrix molecules. Population markers identified were furthermore able to clearly cluster different disease stages in a murine model for myocardial infarct. These data suggest that major features of cardiac fibroblast activation in heart failure patients, in murine heart disease models, and in cell culture of primary murine cardiac fibroblast are shared.

Involvement of Oxidative Stress in the Development of Subcellular Defects and Heart Disease.

It is now well known that oxidative stress promotes lipid peroxidation, protein oxidation, activation of proteases, fragmentation of DNA and alteration in gene expression for producing myocardial cell damage, whereas its actions for the induction of fibrosis, necrosis and apoptosis are considered to result in the loss of cardiomyocytes in different types of heart disease. The present article is focused on the discussion concerning the generation and implications of oxidative stress from various sources such as defective mitochondrial electron transport and enzymatic reactions mainly due to the activation of NADPH oxidase, nitric oxide synthase and monoamine oxidase in diseased myocardium. Oxidative stress has been reported to promote excessive entry of Ca2+ due to increased permeability of the sarcolemmal membrane as well as depressions of Na+-K+ ATPase and Na+-Ca2+ exchange systems, which are considered to increase the intracellular of Ca2+. In addition, marked changes in the ryanodine receptors and Ca2+-pump ATPase have been shown to cause Ca2+-release and depress Ca2+ accumulation in the sarcoplasmic reticulum as a consequence of oxidative stress. Such alterations in sarcolemma and sarcoplasmic reticulum are considered to cause Ca2+-handling abnormalities, which are associated with mitochondrial Ca2+-overload and loss of myofibrillar Ca2+-sensitivity due to oxidative stress. Information regarding the direct effects of different oxyradicals and oxidants on subcellular organelles has also been outlined to show the mechanisms by which oxidative stress may induce Ca2+-handling abnormalities. These observations support the view that oxidative stress plays an important role in the genesis of subcellular defects and cardiac dysfunction in heart disease.

Levels of C-reactive protein, LDL, IL-1α, and IL-1β as Potential Indicators of Heart Disease

C-reactive protein, interleukin-1α, and interleukin-1β are biomarkers that are important in the early detection of heart disease and atherosclerosis. There are many different types of heart diseases, but coronary artery disease is the most common. Heart disease kills hundreds of thousands of people every year because it is usually detected late and its general complications at the later stages. Often, by the time a patient is diagnosed, the heart disease has progressed to its later stages where treatment is not as effective. One of the current diagnostic tests that are conducted is called an electrocardiogram (EKG), and it is very expensive, reducing cost-effectiveness. In this paper, the relevance of C-reactive protein, interleukin 1α, interleukin 1β, and low-density lipoprotein to the indication of heart diseases and the use of those in early detection is discussed. In addition, there is an in-depth review of the correlations between each of the biomarkers and the severity of atherosclerosis within patients.

Evaluation of the knowledge of dental practitioners regarding the present guidelines for the management of cardiopathic patients during endodontic treatment

Background: Longer lifespans and advances in medicine have been matched by an increase in the prevalence of patients at risk of infection who are likely to receive endodontic treatment. The objective of this study was to assess knowledge of dental practitioners regarding the present guidelines for the management of cardiopathic patients during endodontic treatment. Methods: This is a descriptive online survey, involving interviewing 200 dentists. The questions focused on the different types of heart disease and the indications or contraindications of endodontic therapy for the treatment of irreversible pulpitis. The questionnaire was calibrated and validated by the Scientific Committee. Results: Almost all respondents reported knowledge of Infective Endocarditis (94.5%) and focal infection (95.5%).51.5 percent of respondents were unaware of high-risk heart disease (Group A) and lower-risk heart disease (Group B) 28% of the respondents did not find it necessary to establish a dialogue with the general practitioner. Conclusion: A low level of knowledge of the current guidelines was found among dentists. Therefore, attempts should be made to teach the current guidelines in undergraduate/postgraduate dental education.

Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

Pathogenesis of Diabetic Cardiomyopathy and Role of miRNA.

Diabetic cardiomyopathy is characterized as abnormal function and structure of myocardium associated with diabetes irrespective of other cardiac risk factors like hypertension or coronary artery disease (CAD). The pathogenesis of DCM was not well understood in the past due to its complexity but it has been discovered recently. Various factors are found to be associated with the onset of DCM including impaired calcium handling, remodeling of extracellular matrix (ECM), increased oxidative stress, altered metabolism, mitochondrial dysfunction, and endothelial dysfunction. Micro-RNAs (miRNAs) are also found to be of great importance in the pathogenesis of DCM. Different miRNAs like miR-126, miR-24, miR-1, miR-155, miR-499, and miR-199a are found to be associated with different types of heart diseases like CAD and myocardial infarction. Studies have shown that the miRNA plays a crucial role in the development of DCM and it was found that the expression levels of different miRNAs differ in patients as compared to healthy individuals. This review focuses on the pathogenesis of DCM and various factors involved in the onset of diabetic car-diomyopathy. Moreover, the probable role of miRNA in the pathogenesis of DCM is also discussed.

Targets of cGMP/cGKI in Cardiac Myocytes.

The 3',5'-cyclic guanosine monophosphate (cGMP)-dependent protein kinase type I (cGKI aka PKGI) is a major cardiac effector acting downstream of nitric oxide (NO)-sensitive soluble guanylyl cyclase and natriuretic peptides (NPs), which signal through transmembrane guanylyl cyclases. Consistent with the wide distribution of the cGMP-generating guanylyl cyclases, cGKI, which usually elicits its cellular effects by direct phosphorylation of its targets, is present in multiple cardiac cell types including cardiomyocytes (CMs). Although numerous targets of cGMP/cGKI in heart were identified in the past, neither their exact patho-/physiological functions nor cell-type specific roles are clear. Herein, we inform about the current knowledge on the signal transduction downstream of CM cGKI. We believe that better insights into the specific actions of cGMP and cGKI in these cells will help to guide future studies in the search for predictive biomarkers for the response to pharmacological cGMP pathway modulation. In addition, targets downstream of cGMP/cGKI may be exploited for refined and optimized diagnostic and therapeutic strategies in different types of heart disease and their causes. Importantly, key functions of these proteins and particularly sites of regulatory phosphorylation by cGKI should, at least in principle, remain intact, although upstream signaling through the second messenger cGMP is impaired or dysregulated in a stressed or diseased heart state.

Abidjan dentists’ knowledge of the current guidelines for the management of cardiopathic patie nts during endodontic treatment

Longer lifespans and advances in medicine have been matched by an increase in the prevalence of patients at risk of infection who are likely to receive endodontic treatment. The aim of this study was to evaluate the knowledge of Abidjan practitioners about the current guidelines for the management of cardiopathic patients during endodontic treatment. This is a descriptive cross-sectional survey, which involved interviewing 145 dentists. The questions focused on the different types of heart disease and the indications or contraindications of endodontic therapy for the treatment of irreversible pulpitis. The questionnaire was calibrated and validated by the Scientific Committee of the Faculty of Odontostomatology in Abidjan (Côte d'Ivoire). More than 50% of practitioners are unaware of the contraindications for endodontic treatment in the cardiopath. When endodontic treatment is indicated, practitioners do not apply recommendations such as treatment in one session (more than 50%), and antibiotic prophylaxis limited to 1h before treatment (75.17%). Finally, 26.21% of respondents do not find it necessary to establish a dialogue with the general practitioner. A low level of knowledge of the current guidelines was found among Abidjan dentists. Therefore, attempts should be made to teach the current guidelines in Abidjan undergraduate/postgraduate dental education.

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

Prevalence Rate of Depression among Ischemic Heart Diseases Patients in Al-Diwaneya Province / Iraq

Background: Cardiovascular disease is on the upraise in the industrialized and unindustrialized countries, which characterize at the present time one of the chief challenges for the health systems all the world. Depressed Ischemiac Heart disease patients may have a poorer outcome than non-depressed patients ,so this study was performed with the aim of finding the link between depression and ischemic heart disease and its association with demographic factors in Iraqi patients.Patients and Method: The study was designed to be a case- control study involving 128 case of IHD patients and120 apparently healthy individuals serving as control group, with an age range from 20y-80y in Al-Diwaniyah Teaching Hospital in Al-Diwaniyah province/ Iraq. were examined for the presence of depressive disorder using ICD-10 diagnostic criteria and then assessing the severity of the depression in the depressed group by using Beck Depressive Inventory(BDI ).Results: The prevalence rate of depression was 48.3% in IHD patients while 22.5% in control groups .Conclusion: Because of high rate of depression among patients with different types of heart diseases, early detection and identification of as possible ,to improve outcome .

Percutaneous cardiological intervention and cardiac surgery: patient-centered care. Position statement of the Spanish Society of Cardiology.

The percutaneous treatment of coronary artery disease and some structural cardiovascular diseases has undergone spectacular changes. More and more patients with different types of heart disease are being treated by percutaneous or transcatheter interventions, with no such increase in patients undergoing cardiac surgery. This situation has led to different types of approach, requiring an objective analysis that includes all the factors possibly influencing these changes. This document assesses the 2 scenarios where this problem is most evident: coronary revascularization and the treatment of aortic stenosis. The document analyzes the situation of coronary revascularization in Spain, and the causes that may explain the differences between the number of patients who currently undergo percutaneous revascularization and those who undergo coronary surgery. In contrast, treatment of aortic stenosis through transcatheter aortic valve implantation will lead to a foreseeable reduction in the number of candidates for surgical replacement. Several international scientific societies have published the requirements on training and experience and the necessary operator and center volumes to implement a transcatheter aortic valve implantation program, conditions that the Spanish Society of Cardiology, adopting a patient-centered approach, considers absolutely essential. Given that the 2 forms of intervention (percutaneous and surgical) are complementary, multidisciplinary patient assessment (Heart Team) remains crucial to offer the best treatment option. In this scenario of diverse approaches, a key figure is the clinical cardiologist. Finally, the changes currently occurring in the treatment of structural heart disease will, in future, lead to the performance of procedures requiring the participation of professionals from both specialties. This approach will require a redesign of current training programs.

Intervencionismo percutáneo cardiológico y cirugía cardiaca: el paciente en el centro de los procesos. Documento de posicionamiento de la Sociedad Española de Cardiología

The percutaneous treatment of coronary artery disease and some structural cardiovascular diseases has undergone spectacular changes. More and more patients with different types of heart disease are being treated by percutaneous or transcatheter interventions, with no such increase in patients undergoing cardiac surgery. This situation has led to different types of approach, requiring an objective analysis that includes all the factors possibly influencing these changes. This document assesses the 2 scenarios where this problem is most evident: coronary revascularization and the treatment of aortic stenosis. The document analyzes the situation of coronary revascularization in Spain, and the causes that may explain the differences between the number of patients who currently undergo percutaneous revascularization and those who undergo coronary surgery. In contrast, treatment of aortic stenosis through transcatheter aortic valve implantation will lead to a foreseeable reduction in the number of candidates for surgical replacement. Several international scientific societies have published the requirements on training and experience and the necessary operator and center volumes to implement a transcatheter aortic valve implantation program, conditions that the Spanish Society of Cardiology, adopting a patient-centered approach, considers absolutely essential. Given that the 2 forms of intervention (percutaneous and surgical) are complementary, multidisciplinary patient assessment (Heart Team) remains crucial to offer the best treatment option. In this scenario of diverse approaches, a key figure is the clinical cardiologist. Finally, the changes currently occurring in the treatment of structural heart disease will, in future, lead to the performance of procedures requiring the participation of professionals from both specialties. This approach will require a redesign of current training programs.

Mending a Broken Heart—The Genetics of Heart Disease

Heart disease is a major cause of health problems worldwide. There are many different types of heart disease, but one that is fairly common and can have lots of negative side effects is called cardiomyopathy. We know that humans and many mammals, including dogs, get cardiomyopathy. In dilated cardiomyopathy the heart changes shape and often the electrical signals within the heart change. Because of these changes, the heart is not able to pump blood around the body very well, which means that oxygen and nutrients are not delivered to the body at the optimal levels. This can affect day-to-day life, and it can also cause early death. Understanding cardiomyopathy and finding the mutations in genes that cause it are essential, as this information can help us to diagnose, treat, and prevent this heart disorder. We can try to mend the broken hearts that do not work properly. In dogs with cardiomyopathy, we have discovered genetic mutations and found links between these changes in the dogs’ hearts.