Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Genetic analysis for a shared biological basis between migraine and coronary artery disease

Objective: To apply genetic analysis of genome-wide association data to study the extent and nature of a shared biological basis between migraine and coronary artery disease (CAD). Methods: Four separate methods for cross-phenotype genetic analysis were applied on data from 2 large-scale genome-wide association studies of migraine (19,981 cases, 56,667 controls) and CAD (21,076 cases, 63,014 controls). The first 2 methods quantified the extent of overlapping risk variants and assessed the load of CAD risk loci in migraineurs. Genomic regions of shared risk were then identified by analysis of covariance patterns between the 2 phenotypes and by querying known genome-wide significant loci. Results: We found a significant overlap of genetic risk loci for migraine and CAD. When stratified by migraine subtype, this was limited to migraine without aura, and the overlap was protective in that patients with migraine had a lower load of CAD risk alleles than controls. Genes indicated by 16 shared risk loci point to mechanisms with potential roles in migraine pathogenesis and CAD, including endothelial dysfunction (PHACTR1) and insulin homeostasis (GIP). Conclusions: The results suggest that shared biological processes contribute to risk of migraine and CAD, but surprisingly this commonality is restricted to migraine without aura and the impact is in opposite directions. Understanding the mechanisms underlying these processes and their opposite relationship to migraine and CAD may improve our understanding of both disorders.

Percutaneous coronary intervention with drug-eluting stents or coronary artery bypass surgery in subjects with type 2 diabetes : evaluation of: Farkouth ME, Domanski M, Sleeper LA, et al. Strategies for multivessel revascularization in patients with diabetes. N Engl J Med 2012;367(25):2375-84, and Contini GA, Nicolini F, Fortuna D, et al. Five-year outcomes of surgical or percutaneous myocardial revascularization in diabetic patients. Int J Cardiol 2012. [Epub ahead of print]

There is debate as to whether percutaneous coronary intervention (PCI) with drug-eluting stents or coronary artery bypass surgery (CABG) is the best procedure for subjects with type 2 diabetes and coronary artery disease requiring revascularization. There is some evidence that following these procedures there is less further revascularization with CABG than PCI in subjects with diabetes. Two recent studies; the FREEDOM (Future Revascularization Evaluation in patients with Diabetes mellitus: Optimal Management of Multivessel Disease) trial, and a trial using a real world diabetic population from a Registry, have shown that the benefits of CABG over PCI in subjects with type 2 diabetes extends to lower rates of death and myocardial infarct, in addition to lower rates of revascularization. However, the rates of stroke may be higher with CABG than PCI with drug-eluting stents in this population. Thus, if CABG is going to be preferred to PCI in subjects with type 2 diabetes and multivessel coronary disease, consideration should be given to how to reduce the rates of stroke with CABG.

Psychological symptoms : a review of screening practices for patients with coronary heart disease (CHD)

Background/Aims: Coronary heart disease (CHD) and coronary events have been strongly linked to psychological symptoms in patients during hospitalisation and post-discharge. Within Australia CHD average length of stay is decreasing and symptoms often do not present until discharge. Early screening and treatment of psychological symptoms has been recommended to reduce mortality and identify anxiety and depression. This literature review was undertaken to evaluate and describe current screening practices to identify psychological symptoms in these patients.

Comparison of the inflammatory burden of truly asymptomatic carotid atheroma with atherosclerotic plaques in patients with asymptomatic carotid stenosis undergoing coronary artery bypass grafting: An ultrasmall superparamagnetic iron oxide enhanced magnetic resonance study

Introduction: Inflammation is a recognized risk factor for the vulnerable atherosclerotic plaque. The aim of this study was to explore whether there is a difference in the degree of Magnetic Resonance (MR) defined inflammation using Ultra Small Super-Paramagnetic Iron Oxide (USPIO) particles, within carotid atheroma in completely asymptomatic individuals and the asymptomatic carotid stenosis in a cohort of patients undergoing coronary artery bypass grafting (CABG). Methods: 10 patients awaiting CABG with asymptomatic carotid disease and 10 completely asymptomatic individuals with no documented coronary artery disease underwent multi-sequence MR imaging before and 36 hours post USPIO infusion. Images were manually segmented into quadrants and signal change in each quadrant, normalised to adjacent muscle signal, was calculated following USPIO administration. Results: The mean percentage of quadrants showing signal loss was 94% in the CABG group, compared to 24% in the completely asymptomatic individuals (p < 0.001). The carotid plaques from the CABG patients showed a significant mean signal intensity decrease of 16.4% after USPIO infusion (95% CI 10.6% to 22.2%; p < 0.001). The truly asymptomatic plaques showed a mean signal intensity increase (i.e. enhancement) after USPIO infusion of 8.4% (95% CI 2.6% to 14.2%; p = 0.007). The mean signal difference between the two groups was 24.9% (95% CI 16.7% to 33.0%; p < 0.001). Conclusions: These findings are consistent with the hypothesis that inflammatory atheroma is a systemic disease. The carotid territory is more likely to take up USPIO if another vascular territory is symptomatic.

A history of heart interventions moderates the relationship between psychological variables and the presence of chest pain in older women with self-reported coronary heart disease

Objectives: This study examines the hypothesis that a past history of heart interventions will moderate the relationship between psychosocial factors (stressful life events, social support, perceived stress, having a current partner, having a past diagnosis of depression or anxiety over the past 3 years, time pressure, education level, and the mental health index) and the presence of chest pain in a sample of older women. Design: Longitudinal survey over a 3-year period. Methods: The sample was taken from a prospective cohort study of 10,432 women initially aged between 70 and 75 years, who were surveyed in 1996 and then again in 1999. Two groups of women were identified: those reporting to have heart disease but no past history of heart interventions (i.e., coronary artery bypass graft/angioplasty) and those reporting to have heart disease with a past history of heart interventions. Results: Binary logistic regression analysis was used to show that for the women with self-reported coronary heart disease but without a past history of heart intervention, feelings of time pressure as well as the number of stressful life events experienced in the 12 months prior to 1996 were independent risk factors for the presence of chest pain, even after accounting for a range of traditional risk factors. In comparison, for the women with self-reported coronary heart disease who did report a past history of heart interventions, a diagnosis of depression in the previous 3 years was the significant independent risk factor for chest pain even after accounting for traditional risk factors. Conclusion: The results indicate that it is important to consider a history of heart interventions as a moderator of the associations between psychosocial variables and the frequency of chest pain in older women. Statement of Contribution: What is already known on this subject? Psychological factors have been shown to be independent predictors of a range of health outcomes in individuals with coronary heart disease, including the presence of chest pain. Most research has been conducted with men or with small samples of women; however, the evidence does suggest that these relationships exist in women as well as in men. What does this study add? Most studies have looked at overall relationships between psychological variables and health outcomes. The few studies that have looked at moderators have mainly examined gender as a moderator. To our knowledge, this is the first published study to examine a history of heart interventions as a moderator of the relationship between psychological variables and the presence of chest pain.

New drugs for the treatment of coronary artery syndromes

Background: Acute coronary syndromes are a major cause of mortality and morbidity. Objectives/Methods: The objective of this evaluation is to review the clinical trials of two new drugs being developed for the treatment of acute coronary syndromes. The first drug is the anti-coagulant otamixaban, and the trial compared otamixaban with unfractionated heparin and eptifibatide in acute coronary syndromes. The second drug is the anti-platelet ticagrelor, and the trial compared ticagrelor with clopidogrel in acute coronary syndromes. Results: In the SEPIA-ACS1 TIMI 42 trial, the primary efficacy endpoint occurred in 6.2% of subjects treated with unfractionated heparin and eptifibatide, and to a significantly lesser extent with otamixaban. In the PLATO trial, the primary efficacy endpoint had occurred less in the ticagrelor group (9.8%) than in the clopidogrel group (11.7%) at 12 months. Conclusions: Two new drugs for acute coronary syndromes, otamixaban and ticagrelor, have recently been shown to have benefits in subjects undergoing percutaneous interventions compared to the present standard regimens for this condition.