[en] Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control glycaemia has improved outcomes, but have proven difficult to repeat or achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to forecast future distributions of insulin sensitivity (SI) based on its current value, to predict the range of future blood glucose outcomes for a given intervention. This study presents an improved 3D stochastic model, forecasting future distributions of SI based on its current value and prior variation. The percentage difference in the 5th, 50th, and 95th percentiles between the current 2D and new 3D models are compared. Results show the original 2D stochastic model is over-conservative for around 77% of the data, predominantly where prior variability was low. For higher prior variation (more than ±25% change in SI), the 3D stochastic model prediction range of future SI is wider. The new 3D model was found to have overall narrower 5th – 95th prediction ranges in SI, but to retain a similar per-patient (60 – 100%) and overall (92%) percentage of SI outcomes correctly predicted within these ranges. These results suggest the new 3D model is more patient-specific and will enable more optimal dosing, to increase both safety and performance. This improvement in forecasting may result in tighter and safer glycaemic control, improving performance within the STAR framework.
Research center :
GIGA - In Silico Medicine Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Anesthesia & intensive care Endocrinology, metabolism & nutrition
Author, co-author :
Uyttendaele, Vincent ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Dickson, Jennifer L.
Morton, Sophie
Shaw, Geoffrey M.
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Chase, J. Geoffrey
Language :
English
Title :
Changes in Identified, Model-based Insulin Sensitivity can be used to Improve Risk and Variability Forecasting in Glycaemic Control
Publication date :
2018
Event name :
18th IFAC Symposium on System Identification
Event date :
9-11 July 2018
Audience :
International
Journal title :
IFAC-PapersOnLine
ISSN :
2405-8971
eISSN :
2405-8963
Publisher :
IFAC Secretariat, Austria
Pages :
311-316
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture [BE]
Ali, N.A., O'brien, J.M. Jr., Dungan, K., Phillips, G., Marsh, C.B., Lemeshow, S., Connors, A.F. Jr., Preiser, J.C., Glucose variability and mortality in patients with sepsis. Crit Care Med 36 (2008), 2316–2321.
Bagshaw, S.M., Bellomo, R., Jacka, M.J., Egi, M., Hart, G.K., George, C., Committee, A.C.M., The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit Care, 13, 2009, R91.
Capes, S.E., Hunt, D., Malmberg, K., Gerstein, H.C., Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet 355 (2000), 773–778.
Chase, J.G., Le Compte, A.J., Suhaimi, F., Shaw, G.M., Lynn, A., Lin, J., Pretty, C.G., Razak, N., Parente, J.D., Hann, C.E., Preiser, J.C., Desaive, T., Tight glycemic control in critical care–the leading role of insulin sensitivity and patient variability: a review and model-based analysis. Comput Methods Programs Biomed 102 (2011), 156–171.
Chase, J.G., Shaw, G., Le Compte, A., Lonergan, T., Willacy, M., Wong, X.W., Lin, J., Lotz, T., Lee, D., Hann, C., Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Crit Care, 12, 2008, R49.
Chase, J.G., Shaw, G.M., Le Compte, A.J., Lonergan, T., Willacy, M., Wong, X.W., Lin, J., Lotz, T., Lee, D.S., Hann, C.E., Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Critical Care, 12, 2008, R49.
Egi, M., Bellomo, R., Stachowski, E., French, C.J., Hart, G., Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 105 (2006), 244–252.
Egi, M., Bellomo, R., Stachowski, E., French, C.J., Hart, G.K., Taori, G., Hegarty, C., Bailey, M., Hypoglycemia and outcome in critically ill patients. Mayo Clin Proc 85 (2010), 217–224.
Evans, A., Le Compte, A., Tan, C.S., Ward, L., Steel, J., Pretty, C.G., Penning, S., Suhaimi, F., Shaw, G.M., Desaive, T., Chase, J.G., Stochastic targeted (STAR) glycemic control: design, safety, and performance. J Diabetes Sci Technol 6 (2012), 102–115.
Finney, S.J., Zekveld, C., Elia, A., Evans, T.W., Glucose control and mortality in critically ill patients. Jama 290 (2003), 2041–2047.
Fisk, L., Lecompte, A., Penning, S., Desaive, T., Shaw, G., Chase, G., STAR Development and Protocol Comparison. IEEE Trans Biomed Eng 59 (2012), 3357–3364.
Fisk, L.M., Le Compte, A.J., Shaw, G.M., Penning, S., Desaive, T., Chase, J.G., STAR development and protocol comparison. IEEE Trans Biomed Eng 59 (2012), 3357–3364.
Hann, C.E., Chase, J.G., Lin, J., Lotz, T., Doran, C.V., Shaw, G.M., Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Comput Methods Programs Biomed 77 (2005), 259–270.
Krinsley, J.S., Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clin Proc 78 (2003), 1471–1478.
Krinsley, J.S., Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc 79 (2004), 992–1000.
Krinsley, J.S., Glycemic variability and mortality in critically ill patients: the impact of diabetes. J Diabetes Sci Technol 3 (2009), 1292–1301.
Krinsley, J.S., Grover, A., Severe hypoglycemia in critically ill patients: risk factors and outcomes. Crit Care Med 35 (2007), 2262–2267.
Lin, J., Lee, D., Chase, J.G., Shaw, G.M., Le Compte, A., Lotz, T., Wong, J., Lonergan, T., Hann, C.E., Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care. Comput Methods Programs Biomed 89 (2008), 141–152.
Lin, J., Razak, N.N., Pretty, C.G., Le Compte, A., Docherty, P., Parente, J.D., Shaw, G.M., Hann, C.E., Geoffrey Chase, J., A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients. Comput Methods Programs Biomed 102 (2011), 192–205.
Mccowen, K.C., Malhotra, A., Bistrian, B.R., Stressinduced hyperglycemia. Crit Care Clin 17 (2001), 107–124.
Penning, S., Le Compte, A.J., Massion, P., Moorhead, K.T., Pretty, C.G., Preiser, J.C., Shaw, G.M., Suhaimi, F., Desaive, T., Chase, J.G., Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients. Biomed Eng Online, 11, 2012, 58.
Penning, S., Pretty, C.G., Preiser, J.C., Shaw, G.M., Chase, J.G., Desaive, T., Glucose control positively influences patient outcome: a retrospective study. Journal of Critical Care, 2015.
Pretty, C.G., Le Compte, A.J., Chase, J.G., Shaw, G.M., Preiser, J.C., Penning, S., Desaive, T., Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control. Ann Intensive Care, 2, 2012, 17.
Reed, C.C., Stewart, R.M., Sherman, M., Myers, J.G., Corneille, M.G., Larson, N., Gerhardt, S., Beadle, R., Gamboa, C., Dent, D., Cohn, S.M., Pruitt, B.A. Jr., Intensive insulin protocol improves glucose control and is associated with a reduction in intensive care unit mortality. J Am Coll Surg 204 (2007), 1048–1054 discussion 1054-5.
Signal, M., Le Compte, A., Shaw, G.M., Chase, J.G., Glycemic levels in critically ill patients: are normoglycemia and low variability associated with improved outcomes?. J Diabetes Sci Technol 6 (2012), 1030–1037.
Skup, Glucocard X-Meter: Meter and test strips designed for glucose self-measurement manufactered by Arkray, Inc., 2006, University of Bergen, Norway.
Stewart, K., Pretty, C.G., Tomlinson, H., Thomas, F., Shaw, G.M., Benyo, B., Homlok, J., Illyes, A., Szabo Nemedi, N., Chase, J.G., Safety, Efficacy and Clinical Generalization of the STAR Protocol: A Retrospective Analysis. Annals of Intensive Care, 2016 in-press.
Stewart, K.W., Pretty, C.G., Tomlinson, H., Thomas, F.L., Homlok, J., Noemi, S.N., Illyes, A., Shaw, G.M., Benyo, B., Chase, J.G., Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis. Ann Intensive Care, 6, 2016, 24.
Thomas, F., Pretty, C.G., Fisk, L., Shaw, G.M., Chase, J.G., Desaive, T., Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol. Biomed Eng Online, 13, 2014, 43.
Van den Berghe, G., Wilmer, A., Hermans, G., Meersseman, W., Wouters, P.J., Milants, I., Van Wijngaerden, E., Bobbaers, H., Bouillon, R., Intensive insulin therapy in the medical ICU. N Engl J Med 354 (2006), 449–461.
Van den Berghe, G., Wouters, P., Weekers, F., Verwaest, C., Bruyninckx, F., Schetz, M., Vlasselaers, D., Ferdinande, P., Lauwers, P., Bouillon, R., Intensive insulin therapy in critically ill patients. N Engl J Med 345 (2001), 1359–1367.