Risk stratification in assessing risk in coronary artery bypass surgery

We present the need for risk stratification in the monitoring of cardiac surgical practice and review the frequentist and Bayesian approaches to the problem. Developments in the available databases are described. Enhancements to the Parsonnet and EuroSCORE systems are reviewed. We argue that in the UK, although the use of the Parsonnet system is inappropriate and that the EuroSCORE system is a clear improvement, there are advantages in adopting a system based on a Bayesian model for risk assessment

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM

A Study Of The Efficacy Of Machine Learning For Diagnosing Obstructive Coronary Artery Disease In Non-Diabetic Patients

According to the Centers for Disease Control and Prevention, about 18.2 million adults age 20 and older have Coronary Artery Disease in the United States. Early diagnosis is therefore of crucial importance to help prevent debilitating consequences, and principally death for many patients. In this study we use data containing gene expression values from peripheral blood samples in 198 non-diabetic patients, with the goal of developing an age and sex gene expression model for diagnosis of Coronary Artery Disease. We employ machine learning methods to obtain a classification based on genetic information, age and sex. Our implementation uses feed forward neural networks, support vector machines and random forest classification. The neural network outperforms not only the other two but also an early Ridge Regression algorithm that used age, sex, and 23 genes clustered in a set of six metagenes. Our analysis provides valuable insight into the increasing effectiveness of machine learning applied to CAD diagnosis

Mean field variational Bayesian inference for support vector machine classification

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.Comment: 18 pages, 4 figure

Juice Powder Concentrate and Systemic Blood Pressure, Progression of Coronary Artery Calcium and Antioxidant Status in Hypertensive Subjects: A Pilot Study

Because micronutrients from plants may have beneficial cardiovascular effects, the hypothesis that an encapsulated juice powder concentrate might affect several measures of vascular health was tested in free living adults at low cardiovascular risk. Blood pressure, vascular compliance, lipid and antioxidant markers, and serial electron beam tomography (to calculate a coronary artery calcium score as a measure of atherosclerosis burden), were monitored in 51 pre-hypertensive and hypertensive subjects over 2 years. By the end of follow-up, systolic and diastolic blood pressure decreased significantly (−2.4 ± 1.0 mmHg, P < 0.05 and −2.2 ± 0.6 mmHg, P < 0.001), and large artery compliance improved significantly (1.9 ± 0.6 ml mmHg−1 × 100, P < 0.01). The progression of coronary artery calcium score was smaller than expected compared with a historical database (P < 0.001). Laboratory testing showed a significant decrease in homocysteine (P = 0.05), HDL cholesterol (P = 0.025) and Apo A (P = 0.004), as well as a significant increase in β-carotene, folate, Co-Q10 and α-tocopherol (all P < 0.001). The phytonutrient concentrate we utilized induced several favorable modifications of markers of vascular health in the subjects. This study supports the notion that plant nutrients are important components of a heart healthy diet

Development of a risk adjustment mortality model using the American College of Cardiology–National Cardiovascular Data Registry (ACC–NCDR) experience: 1998–2000

AbstractObjectivesWe sought to develop and evaluate a risk adjustment model for in-hospital mortality following percutaneous coronary intervention (PCI) procedures using data from a large, multi-center registry.BackgroundThe 1998–2000 American College of Cardiology–National Cardiovascular Data Registry (ACC–NCDR) dataset was used to overcome limitations of prior risk-adjustment analyses.MethodsData on 100,253 PCI procedures collected at the ACC–NCDR between January 1, 1998, and September 30, 2000, were analyzed. A training set/test set approach was used. Separate models were developed for presentation with and without acute myocardial infarction (MI) within 24 h.ResultsFactors associated with increased risk of PCI mortality (with odds ratios in parentheses) included cardiogenic shock (8.49), increasing age (2.61 to 11.25), salvage (13.38) urgent (1.78) or emergent PCI (5.75), pre-procedure intra-aortic balloon pump insertion (1.68), decreasing left ventricular ejection fraction (0.87 to 3.93), presentation with acute MI (1.31), diabetes (1.41), renal failure (3.04), chronic lung disease (1.33); treatment approaches including thrombolytic therapy (1.39) and non-stent devices (1.64); and lesion characteristics including left main (2.04), proximal left anterior descending disease (1.97) and Society for Cardiac Angiography and Interventions lesion classification (1.64 to 2.11). Overall, excellent discrimination was achieved (C-index = 0.89) and application of the model to high-risk patient groups demonstrated C-indexes exceeding 0.80. Patient factors were more predictive in the MI model, while lesion and procedural factors were more predictive in the analysis of non-MI patients.ConclusionsA risk adjustment model for in-hospital mortality after PCI was successfully developed using a contemporary multi-center registry. This model is an important tool for valid comparison of in-hospital mortality after PCI

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

Statistical modelling of cardiovascular disease patients using Bayesian approaches

This study focuses on statistical modelling on cardiovascular disease (CVD) patients in Malaysia. A secondary dataset from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006 to 2013 is utilised. Studies have shown that CVD affects males and females differently. Thus, a gender-specific analysis with regard to the risk factors and mortality among ST-Elevation Myocardial Infarction (STEMI) patients is needed. Initially, this study performed the standard multivariate logistic analysis where the aims are to identify risk factors associated with mortality for each gender and to compare differences, if any, among STEMI patients. The results showed that gender differences existed among STEMI patients. Even though females share the same risk factors as males, there are risk factors that relate only to females which may have increased their tendency to develop and increase the risk of mortality of CVD patients. An important contribution of this analysis is that it gives an understanding of possible gender-based differences in baseline characteristics, risk factors, treatments and outcomes which will help cardiac care specialists in improving current management of patients with CVD. Next, Bayesian analysis is proposed to develop a prognostic model of the STEMI patients. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach is applied. Beside that, comparisons of the parameter estimates from the proposed Bayesian and frequentist models are made. The results showed that the proposed Bayesian modelling can deal correctly with the probabilities and provides parameter estimates of the posterior distribution which have natural clinical interpretations. In doing so, several programming codes for the Bayesian model development and convergence diagnostics in the Just Another Gibbs Sampler (JAGS) software in R interface are developed. In the final part of this study, a graphical probabilistic model framework defined using a Bayesian Network (BN) is proposed to identify and interpret the dependence structure between the predictors and health outcomes of STEMI patients. In doing so, the two learning processes are involved in obtaining the BN model from the data namely the structural learning and parameter learning. From the structural learning, 25 and 20 arcs were considered significant for males’ and females’ BN respectively. A few variables namely, Killip class, renal disease and age group were classified as key predictors as they were the most influential variables directly associated with the outcome of patients’ status. Moreover, conditional probabilities for each feature were obtained. The novelty of this study is that it provides an indication on the strength of each arc in the network by exploiting the bootstrap resampling method in the structural learning. A graphical model is developed where the relationships in a diagrammatical form is capable to be displayed and the cause-effect relationships can be illustrated. An important implication of this model is that it identifies dependencies based on the different features of variables. It can also include expert knowledge to improve predictability for data driven research when information or resources regarding the variables are limited