The prediction of coronary atherosclerosis employing artificial neural networks

Background: Atherosclerosis is a complex histopathologic process that is analogous to chronic inflammatory conditions. Several factors have been shown to correlate with the extent of atherosclerosis. Whereas hypertension, obesity, hyperlipidemia, diabetes, smoking, and family history are all well documented, recent literature points to additional associated factors. Thus, antibodies to oxidized low-density lipoprotein (or;LDL), cytomegalovirus (CMV), Chlamydia pneumonia, Helicobacter pylori, as well as homocysteine and C-reactive protein (CRP) levels have all been implicated as independent markers of accelerated atherosclerosis. Hypothesis: In the current study we attempted to formulate a system by which to predict the extent of coronary atherosclerosis as assessed by angiographic Vessel occlusion. Methods: The 81 patients were categorized as having single-, double-, triple-, or no vessel involvement. The clinical data concerning the "classic" risk factors were obtained from clinical records, and sera were drawn from the patients for determination of the various parameters that are thought to be associated with atherosclerosis. Results: Using four artificial neural networks, we have found the most effective parameters predictive of coronary vessel involvement were (in decreasing order of importance) antibodies to oxLDL, to cardiolipin, to CMV, to Chamydia pneumonia, and to beta 2-glycoprotein I(beta 2GPI). Although important in the prediction of vessel occlusion, hyperlipidemia, hypertension, CRP levels, and diabetes were less accurate. Conclusion: The results of the current study, if reproduced in a larger population, may establish an integrated system based on the creation of artificial neural networks by which to predict the extent of atherosclerosis in a given subject fairly and noninvasively.