Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System

Heart disease is any disease that affects the normal condition and functionality of heart. Coronary Artery Disease (CAD) is the most common. It is caused by the accumulation of plaques within the walls of the coronary arteries that supply blood to the heart muscles. It may lead to continued temporary oxygen deprivation that will result in the damage of heart muscles. CAD caused more than 7,000,000 deaths every year in the worldwide. It is the second cause of death in Malaysia and the major cause of death in the world. To diagnose CAD, cardiologists usually perform many diagnostic steps. Unfortunately, the results of the diagnostic tests are difficult to interpret which do not always provide defmite answer, but may lead to different opinion. To help cardiologists providing correct diagnosis of CAD in less expensive and non- invasive manner, many researchers had developed decision support system to diagnose CAD. A fuzzy decision support system for the diagnosis of coronary artery disease based on rough set theory is proposed in this thesis. The objective is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. This proposed system is based on evidences or raw medical data sets, which are taken from University California Irvine (UCI) database. The proposed system is designed to be able to handle the uncertainty, incompleteness and heterogeneity of data sets. Artificial Neural Network with Rough Set Theory attribute reduction (ANNRST) is proposed is the imputation method to solve the incompleteness of data sets. Evaluations of ANNRST based on classifiers performance and rule filtering are proposed by comparing ANNRST and other methods using classifiers and during rule filtering process. RST rule inq'u ction is applied to ANNRST imputed data sets. Numerical values are discretized using Boolean reasoning method. Rule selection based on quality and importance is proposed. RST rule importance measure is used to select the most important high quality rules. The selected rules are used to build fuzzy decision support systems. Fuzzification based on discretization cuts and fuzzy rule weighing based on rule quality are proposed. Mamdani inference method is used to provide the decision with centroid defuziification to give numerical results, which represent the possibility of blocking in coronary, arteries. The results show that proposed ANNRST has similar performance to ANN and outperforms k-Nearest Neighbour (k-NN) and Concept Most Common attribute valueFilling (CMCF). ANNRST is simpler than ANN because it has fewer input attributes and more suitable to be applied for missing data imputation problem. ANNRST also provides strong relationship between original and imputed data sets. It is shown that ANNRST provide better RST rule based classifier than CMCF and k-NN during rule filtering process. Proposed RST based rule selection also performs better than other filtering methods. Developed Fuzzy Decision Support System (FOSS) provides better performance compared to multi layer perceptron ANN, k-NN, rule induction method called C4.5 and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) applied on UCI CAD data sets and Ipoh Specialist Hospital's patients. FOSS has transparent knowledge representation, heterogeneous and incomplete input data handling capability. FOSS is able to give the approximate percentage of blocking of coronary artery based on 13 standard attributes based on historical, simple blood test and ECG data, etc, where coronary angiography or cardiologist can not give the percentage. The results of FOSS were evaluated by three local cardiologists and considered to be efficient and useful

The value of myocardial perfusion scintigraphy in the diagnosis and management of angina and myocardial infarction : a probabilistic analysis

Background and Aim. Coronary heart disease (CHD) is the most common cause of death in the United Kingdom, accounting for more than 120,000 deaths in 2001, among the highest rates in the world. This study reports an economic evaluation of single photon emission computed tomography myocardial perfusion scintigraphy (SPECT) for the diagnosis and management of coronary artery disease (CAD). Methods. Strategies involving SPECT with and without stress electrocardiography (ECG) and coronary angiography (CA) were compared to diagnostic strategies not involving SPECT. The diagnosis decision was modelled with a decision tree model and long-term costs and consequences using a Markov model. Data to populate the models were obtained from a series of systematic reviews. Unlike earlier evaluations, a probabilistic analysis was included to assess the statistical imprecision of the results. The results are presented in terms of incremental cost per quality-adjusted life year (QALY). Results. At prevalence levels of CAD of 10.5%, SPECT-based strategies are costeffective; ECG-CA is highly unlikely to be optimal. At a ceiling ratio of _20,000 per QALY, SPECT-CA has a 90% likelihood of being optimal. Beyond this threshold, this strategy becomes less likely to be cost-effective. At more than _75,000 per QALY, coronary angiography is most likely to be optimal. For higher levels of prevalence (around 50%) and more than a _10,000 per QALY threshold, coronary angiography is the optimal decision. Conclusions. SPECTbased strategies are likely to be cost-effective when risk of CAD is modest (10.5%). Sensitivity analyses show these strategies dominated non-SPECT-based strategies for risk of CAD up to 4%. At higher levels of prevalence, invasive strategies may become worthwhile. Finally, sensitivity analyses show stress echocardiography as a potentially costeffective option, and further research to assess the relative cost-effectiveness of echocardiography should also be performed.This article was developed from a Technology Assessment Review conducted on behalf of the National Institute for Clinical Excellence (NICE) and was funded by the Department of Health on a grant administered by the National Coordinating Centre for Health Technology Assessment. The Health Economics Research Unit and the Health Services Research Unit are core funded by the Chief Scientist Office of the Scottish Executive Health Department.Peer reviewedAuthor versio

Heart Failure Anticoagulation Teach-Back Education and Readmissions

abstract: Heart failure affects millions of Americans each year. Treatment of advanced heart failure with reduced ejection fraction and left ventricular failure is sometimes treated with implantation of a left-ventricular assist device. While living with this life-sustaining machine, anticoagulation with Coumadin is necessary. Many of these patients are readmitted within 30-days of being discharged for pump clots, gastro-intestinal bleeds and even strokes. Patients are often discharged without adequate education on Coumadin management, which promotes inadequate self-care and medication non-adherence. In current practice, healthcare providers lecture information in a quick manner without the evaluation of patients’ comprehension. Research suggests implementing the teach-back method during education sessions to assess for comprehension of material to improve medication adherence. Healthcare providers should implement Coumadin teach-back education to heart failure patients with left-ventricular assist devices to improve quality of life, increase medication adherence and decrease 30-day hospital readmission rates

ESC core curriculum for the general cardiologist (2013)

[No abstract available

Type 2 myocardial infarction: the chimaera of cardiology?

The term type 2 myocardial infarction first appeared as part of the universal definition of myocardial infarction. It was introduced to cover a group of patients who had elevation of cardiac troponin but did not meet the traditional criteria for acute myocardial infarction although they were considered to have an underlying ischaemic aetiology for the myocardial damage observed. Since first inception, the term type 2 myocardial infarction has always been vague. Although attempts have been made to produce a systematic definition of what constitutes a type 2 myocardial infarction, it has been more often characterised by what it is not rather than what it is. Clinical studies that have used type 2 myocardial infarction as a diagnostic criterion have produced disparate incidence figures. The range of associated clinical conditions differs from study to study. Additionally, there are no agreed or evidence-based treatment strategies for type 2 myocardial infarction. The authors believe that the term type 2 myocardial infarction is confusing and not evidence-based. They consider that there is good reason to stop using this term and consider instead the concept of secondary myocardial injury that relates to the underlying pathophysiology of the primary clinical condition

Shared decision making in patients with low risk chest pain: prospective randomized pragmatic trial.

OBJECTIVE: To compare the effectiveness of shared decision making with usual care in choice of admission for observation and further cardiac testing or for referral for outpatient evaluation in patients with possible acute coronary syndrome. DESIGN: Multicenter pragmatic parallel randomized controlled trial. SETTING: Six emergency departments in the United States. PARTICIPANTS: 898 adults (aged \u3e17 years) with a primary complaint of chest pain who were being considered for admission to an observation unit for cardiac testing (451 were allocated to the decision aid and 447 to usual care), and 361 emergency clinicians (emergency physicians, nurse practitioners, and physician assistants) caring for patients with chest pain. INTERVENTIONS: Patients were randomly assigned (1:1) by an electronic, web based system to shared decision making facilitated by a decision aid or to usual care. The primary outcome, selected by patient and caregiver advisers, was patient knowledge of their risk for acute coronary syndrome and options for care; secondary outcomes were involvement in the decision to be admitted, proportion of patients admitted for cardiac testing, and the 30 day rate of major adverse cardiac events. RESULTS: Compared with the usual care arm, patients in the decision aid arm had greater knowledge of their risk for acute coronary syndrome and options for care (questions correct: decision aid, 4.2 v usual care, 3.6; mean difference 0.66, 95% confidence interval 0.46 to 0.86), were more involved in the decision (observing patient involvement scores: decision aid, 18.3 v usual care, 7.9; 10.3, 9.1 to 11.5), and less frequently decided with their clinician to be admitted for cardiac testing (decision aid, 37% v usual care, 52%; absolute difference 15%; P CONCLUSIONS: Use of a decision aid in patients at low risk for acute coronary syndrome increased patient knowledge about their risk, increased engagement, and safely decreased the rate of admission to an observation unit for cardiac testing.Trial registration ClinicalTrials.gov NCT01969240