The prediction of coronary atherosclerosis employing artificial neural networks

Jacob George, Alaa Ahmed, Madhumita Patnaik, Yehuda Adler, Yair Levy, Dror Harats, Boris Gilburd, Jeff Terrybery, Guo Qui Shen, Alex Sagie, Itzhak Herz, Peter Snow, Jeff Brandt, James Peter, Yehuda Shoenfeld*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 (oxLDL), 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 Chlamydia pneumonia, and to β2-glycoprotein I (β2GPI). 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.

Original languageEnglish
Pages (from-to)453-456
Number of pages4
JournalClinical Cardiology
Volume23
Issue number6
DOIs
StatePublished - 2000
Externally publishedYes

Keywords

  • Anticardiolipin
  • Atherosclerosis
  • Autoimmunity
  • Infectious agents
  • Oxidized low-density lipoprotein

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