A Multiscale Approach for Modeling Atherosclerosis Progression

Progression of atherosclerotic process constitutes a serious and quite common condition due to accumulation of fatty materials in the arterial wall, consequently posing serious cardiovascular complications. In this paper, we assemble and analyze a multitude of heterogeneous data in order to model the progression of atherosclerosis (ATS) in coronary vessels. The patient's medical record, biochemical analytes, monocyte information, adhesion molecules, and therapy-related data comprise the input for the subsequent analysis. As indicator of coronary lesion progression, two consecutive coronary computed tomography angiographies have been evaluated in the same patient. To this end, a set of 39 patients is studied using a twofold approach, namely, baseline analysis and temporal analysis. The former approach employs baseline information in order to predict the future state of the patient (in terms of progression of ATS). The latter is based on an approach encompassing dynamic Bayesian networks whereby snapshots of the patient's status over the follow-up are analyzed in order to model the evolvement of ATS, taking into account the temporal dimension of the disease. The quantitative assessment of our work has resulted in 93.3% accuracy for the case of baseline analysis, and 83% overall accuracy for the temporal analysis, in terms of modeling and predicting the evolvement of ATS. It should be noted that the application of the SMOTE algorithm for handling class imbalance and the subsequent evaluation procedure might have introduced an overestimation of the performance metrics, due to the employment of synthesized instances. The most prominent features found to play a substantial role in the progression of the disease are: diabetes, cholesterol and cholesterol/HDL. Among novel markers, the CD11b marker of leukocyte integrin complex is associated with coronary plaque progression.

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[3]  J. Lekakis,et al.  Ankle-brachial index as a predictor of the extent of coronary atherosclerosis and cardiovascular events in patients with coronary artery disease. , 2000, The American journal of cardiology.

[4]  M. Woodward,et al.  Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC) , 2005, Heart.

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[7]  J.L. Semmlow,et al.  Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods , 1993, IEEE Transactions on Biomedical Engineering.

[8]  Wei Wang,et al.  TreeQA: quantitative genome wide association mapping using local perfect phylogeny trees. , 2008, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[9]  I. Guler,et al.  Automated Diagnostic Systems With Diverse and Composite Features for Doppler Ultrasound Signals , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[11]  A. Jurka,et al.  Circulating adhesion molecules, matrix metalloproteinase-9, plasminogen activator inhibitor-1, and myeloperoxidase in coronary artery disease patients with stable and unstable angina. , 2012, Clinica chimica acta; international journal of clinical chemistry.

[12]  Dimitrios I. Fotiadis,et al.  Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling , 2008, IEEE Transactions on Information Technology in Biomedicine.

[13]  Ron Kohavi,et al.  Wrappers for performance enhancement and oblivious decision graphs , 1995 .

[14]  Norberto F. Ezquerra,et al.  Constraining and summarizing association rules in medical data , 2006, Knowledge and Information Systems.

[15]  Deborah A Nickerson,et al.  Soluble P-Selectin, SELP Polymorphisms, and Atherosclerotic Risk in European-American and African-African Young Adults: The Coronary Artery Risk Development in Young Adults (CARDIA) Study , 2008, Arteriosclerosis, thrombosis, and vascular biology.

[16]  A M Zeiher,et al.  Prognostic impact of coronary vasodilator dysfunction on adverse long-term outcome of coronary heart disease. , 2000, Circulation.

[17]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[18]  R. Frye,et al.  Association of Risk Factor Variables and Coronary Artery Disease Documented with Angiography , 1981, Circulation.

[19]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[20]  K. Lewenstein,et al.  Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test , 2001, Medical and Biological Engineering and Computing.

[21]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[22]  V. Kučinskas,et al.  Atherosclerosis: alterations in cell communication , 2007 .

[23]  G. Howard,et al.  Cigarette smoking and progression of atherosclerosis: The Atherosclerosis Risk in Communities (ARIC) Study. , 1998, JAMA.

[24]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[25]  S. Paredes,et al.  Cardiovascular risk and status assessment , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[26]  Roded Sharan,et al.  Prediction of Phenotype Information from Genotype Data , 2010, Commun. Inf. Syst..

[27]  Giorgio Iervasi,et al.  Stress/Rest Myocardial Perfusion Abnormalities by Gated SPECT: Still the Best Predictor of Cardiac Events in Stable Ischemic Heart Disease , 2009, Journal of Nuclear Medicine.

[28]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[29]  Carlos Ordonez.  Comparing association rules and decision trees for disease prediction , 2006, HIKM '06.

[30]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[31]  A Ziegler,et al.  Detecting SNP‐expression associations: A comparison of mutual information and median test with standard statistical approaches , 2009, Statistics in medicine.

[32]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[33]  Christopher Meek,et al.  Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.

[34]  E. Boerwinkle,et al.  Contribution of regulatory and structural variations in APOE to predicting dyslipidemia Published, JLR Papers in Press, November 29, 2005. , 2006, Journal of Lipid Research.

[35]  H. Soltanian-Zadeh,et al.  Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine , 2004, IEEE Transactions on Nuclear Science.

[36]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[37]  J. Mckenney,et al.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). , 2001, JAMA.