Evaluation of a Model for Glycemic Prediction in Critically Ill Surgical Patients

PLoS ONE

Volumn 8, Issue 7, 2013, Pages

  Pappada, Scott M ^a   Cameron, Brent D ^a,b   Tulman, David B ^c   Bourey, Raymond E ^b,d   Borst, Marilyn J ^d   Olorunto, William ^d   Bergese, Sergio D ^c   Evans, David C ^c   Stawicki, Stanislaw P A ^c   Papadimos, Thomas J ^c  

^a Toledo   (United States)

^b UNIVERSITY OF TOLEDO   (United States)

^c THE OHIO STATE UNIVERSITY WEXNER MEDICAL CENTER   (United States)

^d MEDICAL COLLEGE OF OHIO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GLUCOSE;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD GLUCOSE METER; CLINICAL ARTICLE; COMPUTER MODEL; COMPUTER PREDICTION; COMPUTER SIMULATION; CRITICALLY ILL PATIENT; FEMALE; GLUCOSE BLOOD LEVEL; GLYCEMIC CONTROL; HUMAN; HYPERGLYCEMIA; HYPOGLYCEMIA; INTENSIVE CARE; MALE; PATIENT SAFETY; SURGICAL PATIENT;

ADULT; AGED; AGED, 80 AND OVER; BLOOD GLUCOSE; COMPUTER SIMULATION; CRITICAL ILLNESS; FEMALE; HUMANS; INTENSIVE CARE UNITS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); POSTOPERATIVE PERIOD; PROGNOSIS; SOFTWARE;

EID: 84880398448     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0069475     Document Type: Article

References (43)

1. Stawicki S, Schuster D, Liu J, Kamal J, Erdal S, et al. (2011) The glucogram: a new quantitative tool for glycemic analysis in the surgical intensive care unit. Int J Crit Illn Inj Sci 1: 5-12.
2. Sung J, Bochicchio G, Joshi M, (2005) Admission hyperglycemia is predictive of outcome in critically ill trauma patients. J Trauma 59: 80-83.
3. Bochicchio G, Sung J, Joshi M, (2005) Persistent hyperglycemia is predictive of outcome in critically ill trauma patients. J Trauma 58: 921-924.
4. Collier B, Diaz J Jr, Forbes R, (2005) The impact of a normoglycemic management protocol on clinical outcomes in the trauma intensive care unit. J Parenter Enteral Nutr 29: 353-358.
5. Laird AM, Miller PR, Preston R, Kilgo PD, Meredith JW, et al. (2004) Relationship of early hyperglycemia to mortality in trauma patients. J Trauma 56: 1058-1062.
6. Lipshutz AKM, Gropper MA, (2009) Perioperative glycemic control: an evidence-based review. Anesthesiology 110: 408-421.
7. Doenst T, Wijeysundera D, Karkouti K, Zechner C, Maganti M, et al. (2005) Hyperglycemia during cardiopulmonary bypass is an independent risk factor for mortality in patients undergoing cardiovascular surgery. J Thorac Cardiovasc Surg 130: 1144-1152.
8. Furnary A, Cheek D, Holmes S, Howell WL, Kelly S, (2006) Achieving tight glycemic control in the operating room: lessons learned from 12 years in the trenches of a paradigm shift in anesthetic care. Semin Thorac Cardiovasc Surg 18: 339-345.
9. Furnary AP, Zerr KJ, Grunkemeier GL, (1999) Continuous intravenous insulin infusion reduces the incidence of deep sternal wound infection in diabetic patients after cardiac surgical procedures. Ann Thoracic Surg 67: 352-360.
10. Furnary AP, Zerr KJ, Grunkemeier GL, Heller CA, (1999) Hyperglycemia: a predictor of mortality following CABG in diabetics. Circulation 100: I591.
11. Jessen M, (2003) Glucose control during cardiac surgery: how sweet it is. J Thorac Cardiovasc Surg 125: 985-987.
12. Jones KW, Cain AS, Mitchell JH, Millar RC, Rimmasch HL, et al. (2008) Hyperglycemia predicts mortality after CABG: postoperative hyperglycemia predicts dramatic increases in mortality after coronary artery bypass graft surgery. J Diabetes Complications 22: 365-370.
13. Goldberg PA, Siegel MD, Russell RR, Sherwin RS, Halickman JI, et al. (2004) Experience with the continuous glucose monitoring system in a medical intensive care unit. Diabetes Technol Ther 6: 339-347.
14. Holzinger U, Warszawska J, Kitzberger R, Wewalka M, Miehsler W, et al. (2010) Real-time continuous glucose monitoring in critically ill patients. Diabetes Care 33: 467-472.
15. Van den Berghe G, Wouters PJ, Bouillon R, (2003) Outcome benefit of intensive insulin therapy in the critically ill: insulin dose versus glycemic control. Crit Care Med 31: 359-366.
16. Van den Berghe G, (2004) How does blood glucose control with insulin save lives in intensive care? J Clin Invest 114: 1187-1195.
17. Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, et al. (2006) Intensive insulin therapy in the medical ICU. N Eng J Med 354: 449-461.
18. Pappada SM, Borst MJ, Cameron BD, Bourey RE, Lather JD, et al. (2010) Development of a neural network model for predicting glucose levels in a surgical critical care setting. Patient Saf Surg 4: 15.
19. McShane MJ, Cameron BD, Cote GL, Spiegelman CH, (1999) Improving complex near-IR calibrations using a new wavelength selection algorithm. Applied Spectroscopy 53: 1575-1581.
20. McShane MJ, Cameron BD, Cote GL, Motamadi M, Spiegelman CH, (1999) A novel peak-hopping stepwise feature selection method with application to Raman spectroscopy. Analytical Chimica Acta 388: 251-264.
21. Renner G, Ainko E, (2003) Genetic algorithms in computer aided design. Computer-Aided Design 35: 709-726.
22. Pappada SM (2010) Prediction of glucose for enhancement of treatment and outcome: a neural network model approach. Doctoral Dissertation In: University of Toledo/OhioLINK.
23. Kovatchev BP, Shields D, Breton M, (2009) Graphical and numerical evaluation of continuous glucose sensing time lag. Diabetes Tech Ther 11: 139-143.
24. Filipovsky J, Ducimetiere P, Eschwege E, Richard JL, Rosselin G, et al. (1996) The relationship of blood pressure with glucose, insulin, heart rate, free fatty acids and plasma cortisol levels according to degree of obesity in middle-aged men. J Hypertens 14: 229-235.
25. Molnar GW, Read RC, (1974) Hypoglycemia and body temperature. JAMA 227: 916-921.
26. Klonoff DC, (2005) Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care 28: 1231-1239.
27. Clarke WL, (2005) The original Clarke Error Grid Analysis (EGA). Diabetes Technol Ther 7: 776-779.
28. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL, (1987) Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10: 622-628.
29. Foody GM, McCulloch MB, Yates WB, (1995) The effect of training set size and composition on artificial neural network classification. Int J Remote Sens 16: 1707-1723.
30. Pappada SM, Cameron BD, Rosman PM, (2008) Development of a neural network for prediction of glucose concentration in type 1 diabetes patients. J Diabetes Sci Technol 2: 793-801.
31. Smith SM, Oveson KE, Strauss W, Raven K, Lefevre MC, et al. (2007) Ultradian variation of blood glucose in intensive care unit patients receiving insulin infusions. Diabetes Care 30: 2503-2505.
32. El-Jabali A, (2005) Neural network modeling and control of type 1 diabetes mellitus. Bioprocess Biosyst Eng 27: 75-79.
33. Gogou G, Maglaveras N, Ambrosiadou BV, Goulis D, Pappas C, (2001) A neural network approach in diabetes management by insulin administration. J Med Syst 25: 119-131.
34. Schlotthauer G, Gamero LG, Torres ME, Nicolini GA, (2006) Modeling, identification and nonlinear model predictive control of type I diabetic patient. Med Eng Phys 28: 240-250.
35. Tresp V, Briegel T, Moody J, (1999) Neural network models for blood glucose metabolism of a diabetic. IEEE Trans Neural Netw 10: 1204-1212.
36. Nomura M, Fujimoto K, Higashino A, Denzumi M, Miyagawa M, et al. (2000) Stress and coping behavior in patients with diabetes mellitus. Acta Diabetologia 37: 61-64.
37. Okamura F, Tashiro A, Utumi A, Imai T, Suchi T, et al. (2000) Insulin resistance in patients with depression and its changes during the clinical course of depression: minimal model analysis. Metabolism 49: 1255-1260.
38. Riazi A, Pickup J, Bradley C, (2004) Daily stress and glycemic control in type 1 diabetes: individual differences in magnitude, direction, and timing of stress-reactivity. Diabetes Res Clin Pract 66: 237-244.
39. Trief PM, Aquilino C, Paradies K, Weinstock RS, (1999) Impact of the work environment on glycemic control and adaptation to diabetes. Diabetes Care 22: 569-574.
40. Van Cauter E, Polonsky KS, Scheen A, (1997) Roles of circadian rhythmicity and sleep in human glucose regulation. Endocr Rev 18: 716-738.
41. Pappada SM, Cameron BD, Rosman PM, Bourey RE, Papadimos TJ, et al. (2011) Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol Ther 13: 135-141.
42. Perez-Gandia C, Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, et al. (2010) Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther 12: 81-88.
43. Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, et al. (2001) The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. J Diabetes Complications 15: 80-87.