Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.
INTRODUCTION: Rheumatoid arthritis (RA) patients who have active disease with longer disease duration have been reported to have increased risk of cardiovascular events compared to the normal population.
OBJECTIVE: The primary aim of our study is to ascertain the prevalence of significant asymptomatic coronary artery disease (CAD) in Asian RA patients who are in remission using multi-detector computed tomography (MDCT). The secondary aims of our study are the usage of pulse wave velocity and the biomarkers N-terminal pro-brain natriuretic peptide (NT-proBNP) and high-senstivity C-reactive protein (hs-CRP) to detect subclinical atherosclerosis in RA patients.
METHODS: We performed a comparative cross-sectional study of 47 RA patients who were in remission with a control group of non-RA patients with a history of atypical chest pain in Sarawak General Hospital from November 2008 to February 2009. All patients underwent 64-slice MDCT, assessment of arterial stiffness using the SphygmoCor test and blood analysis for NT-proBNP and hsCRP.
RESULTS: There were 94 patients in our study with a mean age of 50 +/- 8.8 years. The RA and control patients in each group were matched in terms of traditional CV risk factors. Our RA patients had a median disease duration of 3 years (IQR 5.5). MDCT showed evidence of CAD in nine (19.1%) RA patients and three (6.4%) control patients (P = 0.06). There was no significant association between pulse wave velocity (PWV) and presence of CAD in our RA group. There was no significant correlation between PWV with levels of proBNP or hsCRP in our RA patients.
CONCLUSIONS: In our current pilot study with the limitation of small sample size, RA was not associated with an increased risk of CAD in our RA patients who were in remission. Larger studies of CAD in Asian RA patients are needed to confirm our current finding.
Study site: Sarawak General Hospital, Kuching, Sarawak, Malaysia