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.

Determinants of Coronary and Carotid Atherosclerosis in Finnish Patients with Clinically Suspected Coronary Artery Disease. A Quantitative Angiography and Ultrasound Study

Background. Cardiovascular disease (CVD) remains the most serious threat to life and health in industrialized countries. Atherosclerosis is the main underlying pathology associated with CVD, in particular coronary artery disease (CAD), ischaemic stroke, and peripheral arterial disease. Risk factors play an important role in initiating and accelerating the complex process of atherosclerosis. Most studies of risk factors have focused on the presence or absence of clinically defined CVD. Less is known about the determinants of the severity and extent of atherosclerosis in symptomatic patients. Aims. To clarify the association between coronary and carotid artery atherosclerosis, and to study the determinants associated with these abnormalities with special regard to novel cardiovascular risk factors. Subjects and methods. Quantitative coronary angiography (QCA) and B-mode ultrasound were used to assess coronary and carotid artery atherosclerosis in 108 patients with clinically suspected CAD referred for elective coronary angiography. To evaluate anatomic severity and extent of CAD, several QCA parameters were incorporated into indexes. These measurements reflected CAD severity, extent, and overall atheroma burden and were calculated for the entire coronary tree and separately for different coronary segments (i.e., left main, proximal, mid, and distal segments). Maximum and mean intima-media thickness (IMT) values of carotid arteries were measured and expressed as mean aggregate values. Furthermore, the study design included extensive fasting blood samples, oral glucose tolerance test, and an oral fat-load test to be performed in each participant. Results. Maximum and mean IMT values were significantly correlated with CAD severity, extent, and atheroma burden. There was heterogeneity in associations between IMT and CAD indexes according to anatomical location of CAD. Maximum and mean IMT values, respectively, were correlated with QCA indexes for mid and distal segments but not with the proximal segments of coronary vessels. The values of paraoxonase-1 (PON1) activity and concentration, respectively, were lower in subjects with significant CAD and there was a significant relationship between PON1 activity and concentration and coronary atherosclerosis assessed by QCA. PON1 activity was a significant determinant of severity of CAD independently of HDL cholesterol. Neither PON1 activity nor concentration was associated with carotid IMT. The concentration of triglycerides (TGs), triglyceride-rich lipoproteins (TRLs), oxidized LDL (oxLDL), and the cholesterol content of remnant lipoprotein particle (RLP-C) were significantly increased at 6 hours after intake of an oral fatty meal as compared with fasting values. The mean peak size of LDL remained unchanged 6 hours after the test meal. The correlations between total TGs, TRLs, and RLP-C in fasting and postprandial state were highly significant. RLP-C correlated with oxLDL both in fasting and in fed state and inversely with LDL size. In multivariate analysis oxLDL was a determinant of severity and extent of CAD. Neither total TGs, TRLs, oxLDL, nor LDL size were linked to carotid atherosclerosis. Insulin resistance (IR) was associated with an increased severity and extent of coronary atherosclerosis and seemed to be a stronger predictor of coronary atherosclerosis in the distal parts of the coronary tree than in the proximal and mid parts. In the multivariate analysis IR was a significant predictor of the severity of CAD. IR did not correlate with carotid IMT. Maximum and mean carotid IMT were higher in patients with the apoE4 phenotype compared with subjects with the apoE3 phenotype. Likewise, patients with the apoE4 phenotype had a more severe and extensive CAD than individuals with the apoE3 phenotype. Conclusions. 1) There is an association between carotid IMT and the severity and extent of CAD. Carotid IMT seems to be a weaker predictor of coronary atherosclerosis in the proximal parts of the coronary tree than in the mid and distal parts. 2) PON1 activity has an important role in the pathogenesis of coronary atherosclerosis. More importantly, the study illustrates how the protective role of HDL could be modulated by its components such that equivalent serum concentrations of HDL cholesterol may not equate with an equivalent, potential protective capacity. 3) RLP-C in the fasting state is a good marker of postprandial TRLs. Circulating oxLDL increases in CAD patients postprandially. The highly significant positive correlation between postprandial TRLs and postprandial oxLDL suggests that the postprandial state creates oxidative stress. Our findings emphasize the fundamental role of LDL oxidation in the development of atherosclerosis even after inclusion of conventional CAD risk factors. 4) Disturbances in glucose metabolism are crucial in the pathogenesis of coronary atherosclerosis. In fact, subjects with IR are comparable with diabetic subjects in terms of severity and extent of CAD. 5) ApoE polymorphism is involved in the susceptibility to both carotid and coronary atherosclerosis.

Major histocompatibility complex and coronary artery disease: Special emphasis on Chlamydia pneumoniae, and periodontitis

Most of the genes in the MHC region are involveed in adaptive and innate immunity, with essential function in inflammatory reactions and in protection against infections. These genes might serve as a candidate region for infection and inflammation associated diseases. CAD is an inflammatory disease. The present set of studies was performed to assess whether the MHC region harbors genetic markers for CAD, and whether these genetic markers explain the CAD risk factors: e.g., C. pneumoniae, periodontitis, and periodontal pathogens. Study I was performed using two separate patient materials and age- and sex-matched healthy controls, categorizing them into two independent studies: the HTx and ACS studies. Both studies consistently showed the HLA-A3– B35– DR1 (35 ancestral haplotype) haplotype as a susceptible MHC genetic marker for CAD. HLA-DR1 alone was associated not only with CAD, but also with CAD risk factor diseases, e.g., diabetes mellitus, and hyperlipidemia. The ACS study further showed the HLA-B*07 and -DRB1*15 -related haplotype as a protective MHC haplotype for CAD. Study II showed that patients with CAD showed signs of chronic C. pneumoniae infection when compared to age- and sex-matched healthy controls. HLA-B*35 or -related haplotypes associated with the C. pneumoniae infection markers. Among these haplotype carriers, males and smokers associated with elevated C. pneumoniae infection markers. Study III showed that CAD patients with periodontitis had elevated serum markers of P. gingivalis and occurrence of the pathogen in saliva. LTA+496C strongly associated with periodontitis, while HLA-DRB1*01 with periodontitis and with the elevated serum antibodies of P. gingivalis. Study IV showed that the increased level of C3/C4 ratio was a new risk factor and was associated with recurrent cardiovascular end-points. The increased C3 and decreased C4 concentrations in serum explained the increased level of the C3/C4 ratio. Both the higher than cut-off value (4.53) and the highest quartile of the C3/C4 ratio were also associated with worst survival, increased end-points, and C4 null alleles. The presence of C4 null alleles associated with decreased serum C4 concentration, and increased C3/C4 ratio. In conclusion, the present studies show that the CAD susceptibility haplotype (HLA-A3− B35− DR1 -related haplotypes, Study I) partially explains the development of CAD in patients possessing several recognized and novel risk factors: diabetes mellitus, increased LDL, smoking, C4B*Q0, C. pneumnoiae, periodontitis, P. gingivalis, and complement C3/C4 ratio (Study II, III, and IV).

Noninvasive techniques in assessing coronary artery disease

Conventional invasive coronary angiography is the clinical gold standard for detecting of coronary artery stenoses. Noninvasive multidetector computed tomography (MDCT) in combination with retrospective ECG gating has recently been shown to permit visualization of the coronary artery lumen and detection of coronary artery stenoses. Single photon emission tomography (SPECT) perfusion imaging has been considered the reference method for evaluation of nonviable myocardium, but magnetic resonance imaging (MRI) can accurately depict structure, function, effusion, and myocardial viability, with an overall capacity unmatched by any other single imaging modality. Magnetocardiography (MCG) provides noninvasively information about myocardial excitation propagation and repolarization without the use of electrodes. This evolving technique may be considered the magnetic equivalent to electrocardiography. The aim of the present series of studies was to evaluate changes in the myocardium assessed with SPECT and MRI caused by coronary artery disease, examine the capability of multidetector computed tomography coronary angiography (MDCT-CA) to detect significant stenoses in the coronary arteries, and MCG to assess remote myocardial infarctions. Our study showed that in severe, progressing coronary artery disease laser treatment does not improve global left ventricular function or myocardial perfusion, but it does preserve systolic wall thickening in fixed defects (scar). It also prevents changes from ischemic myocardial regions to scar. The MCG repolarization variables are informative in remote myocardial infarction, and may perform as well as the conventional QRS criteria in detection of healed myocardial infarction. These STT abnormalities are more pronounced in patients with Q-wave infarction than in patients with non-Q-wave infarctions. MDCT-CA had a sensitivity of 82%, a specificity of 94%, a positive predictive value of 79%, and a negative predictive value of 95% for stenoses over 50% in the main coronary arteries as compared with conventional coronary angiography in patients with known coronary artery disease. Left ventricular wall dysfunction, perfusion defects, and infarctions were detected in 50-78% of sectors assigned to calcifications or stenoses, but also in sectors supplied by normally perfused coronary arteries. Our study showed a low sensitivity (sensitivity 63%) in detecting obstructive coronary artery disease assessed by MDCT in patients with severe aortic stenosis. Massive calcifications complicated correct assessment of the lumen of coronary arteries.

Myoblast transplantation and adenoviral VEGF-C transfer in porcine model of coronary artery disease

Heart failure is a common and highly challenging medical disorder. The progressive increase of elderly population is expected to further reflect in heart failure incidence. Recent progress in cell transplantation therapy has provided a conceptual alternative for treatment of heart failure. Despite improved medical treatment and operative possibilities, end-stage coronary artery disease present a great medical challenge. It has been estimated that therapeutic angiogenesis would be the next major advance in the treatment of ischaemic heart disease. Gene transfer to augment neovascularization could be beneficial for such patients. We employed a porcine model to evaluate the angiogenic effect of vascular endothelial growth factor (VEGF)-C gene transfer. Ameroid-generated myocardial ischemia was produced and adenovirus encoding (ad)VEGF-C or β-galactosidase (LacZ) gene therapy was given intramyocardially during progressive coronary stenosis. Angiography, positron emission tomography (PET), single photon emission computed tomography (SPECT) and histology evidenced beneficial affects of the adVEGF-C gene transfer compared to adLacZ. The myocardial deterioration during progressive coronary stenosis seen in the control group was restrained in the treatment group. We observed an uneven occlusion rate of the coronary vessels with Ameroid constrictor. We developed a simple methodological improvement of Ameroid model by ligating of the Ameroid–stenosed coronary vessel. Improvement of the model was seen by a more reliable occlusion rate of the vessel concerned and a formation of a rather constant myocardial infarction. We assessed the spontaneous healing of the left ventricle (LV) in this new model by SPECT, PET, MRI, and angiography. Significant spontaneous improvement of myocardial perfusion and function was seen as well as diminishment of scar volume. Histologically more microvessels were seen in the border area of the lesion. Double staining of the myocytes in mitosis indicated more cardiomyocyte regeneration at the remote area of the lesion. The potential of autologous myoblast transplantation after ischaemia and infarction of porcine heart was evaluated. After ligation of stenosed coronary artery, autologous myoblast transplantation or control medium was directly injected into the myocardium at the lesion area. Assessed by MRI, improvement of diastolic function was seen in the myoblast-transplanted animals, but not in the control animals. Systolic function remained unchanged in both groups.

Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events.

BACKGROUND: Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. METHODS AND RESULTS: We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("event-replication" group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01). CONCLUSIONS: Metabolite profiles are associated with CAD and subsequent cardiovascular events.