Aortic Valvular Disease

Broadly speaking, pathology is categorized as being primarily related to valvular stenosis (AS) or regurgitation (AR), but a diseased valve may often exhibit both. The predilection of degenerative disease of the aortic valve, particularly stenosis, for the elderly has resulted in a steadily increasing prevalence as the population ages. As general life expectancy increases in the United States and other western countries, surgery to correct aortic valve disease will increase. As more elderly patients with more comorbidities present for surgery their intraoperative and perioperative care will become more complex. This chapter discusses ways for the practicing intensivist to facilitate identification and treatment in the immediate peri-operative period.

Cardiovascular Disease Prediction Using Data Mining Techniques

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

Interdisciplinary Approach to Cardiovascular Diseases for Research and Everyday Clinical Practice Purposes

Cardiovascular Diseases (CVD) are perceived a leading cause of death globally. Scientists and clinicians still search for more efficient prevention, treatment, rehabilitation and care programs suitable for patients with CVD. Common social awareness and interdisciplinary effort may significantly improve current situation, but the problem is more complex. This chapter, based on research and own experiences of authors, tries to answer the question: how maximize professional resources and optimize outcomes in clinical practice. Aim of this chapter is discuss current issues which may potentially influence efficiency of CVD prevention and therapy, including prevention, modifiable and non-modifiable risk factors, ways of cardiac rehabilitation (CR) and cardiac telerehabilitation (CTR), influence of researcher-subject relationship and patient-therapist relationship as far as placebo effect.

Nanoparticle-Based Drug Delivery Systems for Cardiovascular Applications

Nanomedicine has vastly improved the treatment and diagnosis of many cardiovascular conditions such as atherosclerosis, myocardial ischemia, myocardial infarction, restenosis, and thrombosis. A few nanoparticle drug delivery systems that are currently being tested and used in clinical trials include lipid-based drug delivery, controlled drug release, and specific targeting. The chapter describes the various drug delivery methods, the various nanoparticles, and their application on specific cardiovascular conditions. This chapter compiles examples of specific clinical trials that are being conducted, using nanoparticles for therapy of cardiovascular conditions.

Epidemiology of Cardiac Surgery and the Cardiac Surgical Patient

While the timeline has been relatively abbreviated, there has been significant evolution in the field of cardiac surgery. These changes have been driven by a combination of operative innovation, changing patient demographics, and novel critical care resources, all of which have allowed today's surgeons to treat a myriad of conditions among increasingly higher risk patient cohorts. At the same time, this has forced providers to expand their clinical skill sets, embrace multidisciplinary collaboration, enhance postoperative care, and intensify the rigor by which outcomes and quality are being measured. In spite of this increasing complexity, however, mortality in cardiac surgery continues to improve. In this chapter, we highlight key historical events and describe an unprecedented trajectory and evolution in care practices that have helped shape modern cardiac surgery. We also make an appeal for additional research efforts which are needed to ensure sustained and innovative growth.

Computer Predictive Model for Plaque Formation and Progression in the Artery

In this chapter we described predictive model for plaque formation and progression in the coronary and carotid artery. A full three-dimensional model for plaque formation and progression, coupled with blood flow and LDL concentration is analysed. The Navier-Stokes equations together with the Darcy law for model blood filtration and Kedem-Katchalsky equations are implemented. Additionally, the system of three additional reaction-diffusion equations for simulation of the inflammatory process is coupled with full incremental iterative procedure. We developed hybrid genetic algorithm for fitting parameters of ODE model for oxidized LDL, macrophage, smooth muscle cell and foam cell concentration evolution in time. The animal carotid and coronary artery after 2 month of high fat diet are examined. We compared with CT our computer model of the plaque size for three groups of patients: De-novo, Old-lesions and Control patients. Detailed shear stress distributions for baseline and follow-up for these patients are given. There is a good matching for plaque size and location.

Big Data Analysis for Cardiovascular Diseases

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

Data is generated by the medical industry. Often this data is of very complex nature—electronic records, handwritten scripts, etc.—since it is generated from multiple sources. Due to the Complexity and sheer volume of this data necessitates techniques that can extract insight from this data in a quick and efficient way. These insights not only diagnose the diseases but also predict and can prevent disease. One such use of these techniques is cardiovascular diseases. Heart disease or coronary artery disease (CAD) is one of the major causes of death all over the world. Comprehensive research using single data mining techniques have not resulted in an acceptable accuracy. Further research is being carried out on the effectiveness of hybridizing more than one technique for increasing accuracy in the diagnosis of heart disease. In this article, the authors worked on heart stalog dataset collected from the UCI repository, used the Random Forest algorithm and Feature Selection using rough sets to accurately predict the occurrence of heart disease

Development of a Fuzzy Logic-Based Model for Monitoring Cardiovascular Risk

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.