A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease

Diabetes and Vascular Disease Research

Volumn 6, Issue 2, 2009, Pages 62-70

  Anselmino, Matteo ^a   Malmberg, Klas ^a   Rydén, Lars ^a   Öhrvik, John ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

Artificial neural network; Classification; Coronary artery disease; Cross validation; Diabetes mellitus; Event free survival; Oral glucose tolerance test; Ordinal logistic regression

Indexed keywords

GLUCOSE; HEMOGLOBIN A1C; HIGH DENSITY LIPOPROTEIN CHOLESTEROL;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY MASS; CARDIOVASCULAR RISK; CONTROLLED STUDY; CORONARY ARTERY DISEASE; FASTING GLUCOSE LEVEL; FEMALE; FOLLOW UP; GLUCOMETABOLIC RISK INDEX; GLUCOSE BLOOD LEVEL; GLUCOSE METABOLISM; HEART INFARCTION; HIGH RISK PATIENT; HUMAN; MAJOR CLINICAL STUDY; MALE; METABOLIC SYNDROME X; MORTALITY; NERVE CELL NETWORK; ORAL GLUCOSE TOLERANCE TEST; RISK ASSESSMENT; SCORING SYSTEM; STROKE;

AGE FACTORS; AGED; BIOLOGICAL MARKERS; BLOOD GLUCOSE; CHOLESTEROL, HDL; CORONARY ARTERY DISEASE; DISEASE-FREE SURVIVAL; FASTING; FEMALE; GLUCOSE TOLERANCE TEST; HUMANS; KAPLAN-MEIERS ESTIMATE; LOGISTIC MODELS; MALE; METABOLIC SYNDROME X; MIDDLE AGED; MYOCARDIAL INFARCTION; NEURAL NETWORKS (COMPUTER); PROGNOSIS; PROPORTIONAL HAZARDS MODELS; RISK ASSESSMENT; RISK FACTORS; STROKE; TIME FACTORS;

EID: 68349093770     PISSN: 14791641     EISSN: None     Source Type: Journal    
DOI: 10.1177/1479164109336052     Document Type: Article

References (40)

1. Reaven, GM (1988) Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37: 1595-1607
2. Feinberg, MS, Schwartz, R, Tanne, D, et al. (2007) Impact of the metabolic syndrome on the clinical outcomes of non-clinically diagnosed diabetic patients with acute coronary syndrome. Am J Cardiol 99: 667-672 (Pubitemid 46298543)
3. Kahn, R, Buse, J, Ferrannini, E, Stern, M (2005) The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 28: 2289-2304 (Pubitemid 41242486)
4. Rydén, L, Standl, E, Bartnik, M, et al. (2007) Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: executive summary: The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of Diabetes (EASD). Eur Heart J 28(1): 88-136. (Pubitemid 46137814)
5. Bartnik, M, Rydén, L, Ferrari, R, et al. (2004) The prevalence of abnormal glucose regulation in patients with coronary artery disease across Europe. The Euro Heart Survey on diabetes and the heart. Eur Heart J 25: 1880-1890 (Pubitemid 39452604)
6. Lenzen, M, Rydén, L, Ohrvik, J, et al. (2006) Diabetes known or newly detected, but not impaired glucose regulation, has a negative influence on 1-year outcome in patients with coronary artery disease: a report from the Euro Heart Survey on diabetes and the heart. Eur Heart J 27: 2969-2974 (Pubitemid 44950440)
7. Bartnik, M, Malmberg, K, Norhammar, A, Tenerz, A, Ohrvik, J, Rydén, L (2004) Newly detected abnormal glucose tolerance: an important predictor of long-term outcome after myocardial infarction. Eur Heart J 25: 1990-1997 (Pubitemid 39486017)
8. Basheer, IA, Hajmeer, M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1): 3-31. (Pubitemid 30838717)
9. The DECODE study group on behalf of the European Diabetes Epidemiology Group. (2003) Is the current definition for diabetes relevant to mortality risk from all causes and cardiovascular and noncardiovascular diseases? Diabetes Care 26: 688-696
10. Alberti, KG, Zimmet, PZ (1998) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 15: 539-553
11. Altman, D Practical Statistics for Medical Research. London: Chapman & Hall. 1991.
12. Grundy, SM, Cleeman, JI, Daniels, SR, et al. (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112: 2735-2752 (Pubitemid 41532612)
13. Norhammar, A, Tenerz, A, Nilsson, G, et al. (2002) Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study. Lancet 359: 2140-2144 (Pubitemid 34694020)
14. Bartnik, M, Rydén, L, Malmberg, K, et al. (2007) Euro Heart Survey Investigators. Oral glucose tolerance test is needed for appropriate classification of glucose regulation in patients with coronary artery disease: a report from the Euro Heart Survey on Diabetes and the Heart. Heart 93(1): 72-77.
15. St Clair, L, Ballantyne, CM (2007) Biological surrogates for enhancing cardiovascular risk prediction in type 2 diabetes mellitus. Am J Cardiol 99(4A): 80-88 (Pubitemid 46251371)
16. Isomaa, B, Almgren, P, Tuomi, T, et al. (2001) Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 24: 683-689 (Pubitemid 32893993)
17. Liu, J, Grundy, SM, Wang, W, et al. (2007) Ten-year risk of cardiovascular incidence related to diabetes, prediabetes, and the metabolic syndrome. Am Heart J 153: 552-558 (Pubitemid 46440680)
18. Gaede, P, Vedel, P, Larsen, N, Jensen, GV, Parving, HH, Pedersen. O. (2003) Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N Engl J Med 348: 383-393
19. Anselmino, M, Öhrvik, J, Bartnik, M, Rydén, L (2008) The use of evidence based medicine and revascularization - highly rewarding in diabetic patients with coronary artery disease - a report from the Euro Heart Survey on diabetes and the heart. Eur J Cardiovasc Prev Rehabil 15: 216-223
20. Wang, J, Ruotsalainen, S, Moilanen, L, Lepisto, P, Laakso, M, Kuusisto, J (2007) The metabolic syndrome predicts cardiovascular mortality: a 13-year follow-up study in elderly non-diabetic Finns. Eur Heart J 28: 857-864 (Pubitemid 47351113)
21. Mineo, C, Deguchi, H, Griffin, JH, Shaul, PW (2006) Endothelial and antithrombotic actions of HDL. Circ Res 98: 1352-1364
22. Rohrer, L, Hersberger, M, von Eckardstein, A (2004) High density lipoproteins in the intersection of diabetes mellitus, inflammation and cardiovascular disease. Curr Opin Lipidol 15: 269-278 (Pubitemid 38716559)
23. Kontush, A, Chapman, MJ (2006) Antiatherogenic small, dense HDL - guardian angel of the arterial wall? Nat Clin Pract Cardiovasc Med 3: 144-153 (Pubitemid 43307515)
24. Ma, L, Khorasani, K (2004) New training strategies for constructive neural networks with application to regression problems. Neural Networks 17: 589-609. (Pubitemid 38510341)
25. Ciampi, A, Zhang, F (2002) A new approach to training back-propagation artificial neural networks: empirical evauation on ten data sets from clinical studies. Stat Med 21: 1309-1330 (Pubitemid 34467236)
26. Snow, PB, Kerr, DJ, Brandt, JM, Rodvold, DM (2001) Neural network and regression predictions of 5-year survival after colon carcinoma treatment. Cancer 91: 1673-1678
27. Matsui, Y, Egawa, S, Tsukayama, C, et al. (2002) Artificial neural network analysis for predicting pathological stage of clinically localized prostate cancer in the Japanese population. Jpn J Clin Oncol 32: 530-535 (Pubitemid 43102759)
28. Ahmed, FE (2005) Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer 4: 29.
29. Wu, Y, Giger, ML, Doi, K, Vyborny, CJ, Schmidt, RA, Metz, CE (1993) Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 187(1): 81-87 (Pubitemid 23097863)
30. El-Solh, AA, Hsiao, CB, Goodnough, S, Serghani, J, Grant, BJ (1999) Predicting active pulmonary tuberculosis using an artificial neural network. Chest 116: 968-973 (Pubitemid 29491867)
31. Dawes, RM, Faust, D, Meehl, PE (1989) Clinical versus actuarial judgment. Science 243: 1668-1674
32. Gurney, JW, Swensen, SJ (1995) Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. Radiology 196: 823-829
33. Dreiseitl, S, Ohno-Machado, L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35: 352-359 (Pubitemid 36951935)
34. Sargent, DJ (2001) Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 91: 1636-1642
35. Schwarzer, G, Vach, W, Schumacher, M (2000) On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 19: 541-561 (Pubitemid 30126883)
36. Butlin, RK (1998) Book Review. Davidson, AC, Hinkley, DV (1997) Bootstrap Methods and their Application. Cambridge: Cambridge University Press. J Anim Ecol 67: 842.
37. Ennett, CM, Frize, M, Charette, E (2004) Improvement and automation of artificial neural networks to estimate medical outcomes. Med Eng Phys 26: 321-328 (Pubitemid 38581040)
38. Mitchell, RJ, Bishop, JM, Minchinton, PR (1996) Optimising memory usage in n-tuple neural networks. Math Comput Simul 40: 549-563
39. Pesarin, F Multivariate Permutation Tests with Applications in Biostatistics. Chichester John Wiley, 2001.
40. Dayhoff, JE, DeLeo, JM (2001) Artificial neural networks: opening the black box. Cancer; 91: 1615-1635 (Pubitemid 32319239)