[en] Background: Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output. Methods: This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1,525 patients. Results: Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G. Conclusions: Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required.
Disciplines :
Anesthesia & intensive care Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Davidson, Shaun
Pretty, Christopher ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Uyttendaele, Vincent ; Université de Liège - ULiège > In silico-Model-based therapeutics, Critical Care Basic Sc.
Knopp, Jennifer L.
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Chase, J. Geoffrey
Language :
English
Title :
Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data
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. The Lancet 355 (2000), 773–778.
Finney, S.J., Zekveld, C., Elia, A., Evans, T.W., Glucose control and mortality in critically ill patients. JAMA 290 (2003), 2041–2047.
McCowen, K.C., Malhotra, A., Bistrian, B.R., Stress-induced hyperglycemia. Crit. Care Clin. 17 (2001), 107–124.
Krinsley, J.S., Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clin Proc, 2003, Elsevier, 1471–1478.
Egi, M., Bellomo, R., Stachowski, E., French, C.J., Hart, G., Variability of blood glucose concentration and short-term mortality in critically ill patients. J. Am. Soc. Anesthesiol. 105 (2006), 244–252.
Krinsley, J.S., Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit. Care Med. 36 (2008), 3008–3013.
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.
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.
Krinsley, JS, Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc., 2004, Elsevier, 992–1000.
Chase, J.G., Pretty, C.G., Pfeifer, L, Shaw, G.M., Preiser, J.C., Le Compte, A.J., Lin, J., Hewett, D., Moorhead, K.T., Desaive, T., Organ failure and tight glycemic control in the SPRINT study. Crit. care, 14, 2010, R154.
Krinsley, J.S., Jones, R.L., Cost analysis of intensive glycemic control in critically ill adult patients. Chest 129 (2006), 644–650.
Van den Berghe, G., Wouters, P.J., Kesteloot, K., Hilleman, D.E., Analysis of healthcare resource utilization with intensive insulin therapy in critically ill patients. Crit. Care Med. 34 (2006), 612–616.
Dickson, J.L., Gunn, C.A., Chase, J.G., Humans are horribly variable. Int. J. Clin. Med. Imaging 1 (2014), 1–1000142.
Pretty, C., Le Compte, A., Chase, J.G., Shaw, G., Preiser, J.C., Penning, S., Desaive, T., Variability of insulin sensitivity during the first 4 days of critical illness. Crit. Care, 16, 2012, P167.
Evans, A., Le Compte, A., Tan, C.S., Ward, L., Steel, J., Pretty, C.G., Penning, S., Suhaimi, F., Shaw, G.M., Desaive, T., Stochastic targeted (STAR) glycemic control: design, safety, and performance. J. Diabetes Sci. Technol. 6 (2012), 102–115.
Evans, A., Shaw, G.M., Le Compte, A., Tan, C.S., Ward, L., Steel, J., Pretty, C.G., Pfeifer, L., Penning, S., Suhaimi, F., Pilot proof of concept clinical trials of stochastic targeted (STAR) glycemic control. Ann. Intensive care, 1, 2011, 38.
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.
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.
Finfer, S., Delaney, A., Tight glycemic control in critically ill adults. JAMA 300 (2008), 963–965.
Brunkhorst, F.M., Engel, C., Bloos, F., Meier-Hellmann, A., Ragaller, M., Weiler, N., Moerer, O., Gruendling, M., Oppert, M., Grond, S., Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N. Engl. J. Med. 358 (2008), 125–139.
Griesdale, D.E., de Souza, R.J., van Dam, R.M., Heyland, D.K., Cook, D.J., Malhotra, A., Dhaliwal, R., Henderson, W.R., Chittock, D.R., Finfer, S., Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. Can. Med. Assoc. J. 180 (2009), 821–827.
Treggiari, M.M., Karir, V., Yanez, N.D., Weiss, N.S., Daniel, S., Deem, S.A., Intensive insulin therapy and mortality in critically ill patients. Crit. Care, 12, 2008, R29.
Kalfon, P., Giraudeau, B., Ichai, C., Guerrini, A., Brechot, N., Cinotti, R., Dequin, P.F., Riu-Poulenc, B., Montravers, P., Annane, D., Tight computerized versus conventional glucose control in the ICU: a randomized controlled trial. Intensive Care Med. 40 (2014), 171–181.
Hamimy, W., Khedr, H., Rushdi, T., Zaghloul, A., Hosni, M., Aal, A.A., Application of conventional blood glucose control strategy in surgical ICU in developing countries: is it beneficial?. Egypt. J. Anaesth. 32 (2016), 123–129.
Investigators N-SS. Intensive versus conventional glucose control in critically ill patients. N. Engl. J. Med. 360 (2009), 1283–1297.
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., 2010, Elsevier, 217–224.
Bagshaw, S.M., Bellomo, R., Jacka, M.J., Egi, M., Hart, G.K., George, C., The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit. care, 13, 2009, R91.
Investigators N-SS. Hypoglycemia and risk of death in critically ill patients. N. Engl. J. Med. 367 (2012), 1108–1118.
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., 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.
Langouche, L., Vander Perre, S., Wouters, P.J., D'hoore, A., Hansen, T.K., Van den Berghe, G., Effect of intensive insulin therapy on insulin sensitivity in the critically ill. J. Clin. Endocrinol. Metab. 92 (2007), 3890–3897.
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.
Lin, J., Razak, N.N., Pretty, C.G., Le Compte, A., Docherty, P., Parente, J.D., Shaw, G.M., Hann, C.E., Chase, J.G., A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients. Comput. Methods Programs Biomed. 102 (2011), 192–205.
Chase, J.G., Suhaimi, F., Penning, S., Preiser, J.C., Le Compte, A.J., Lin, J., Pretty, C.G., Shaw, G.M., Moorhead, K.T., Desaive, T., Validation of a model-based virtual trials method for tight glycemic control in intensive care. Biomed. Eng. online, 9, 2010, 84.
Lin, J., Lee, D., Chase, J.G., Shaw, G.M., Hann, C.E., Lotz, T., Wong, J., Stochastic modelling of insulin sensitivity variability in critical care. Biomed. Signal Process. Control 1 (2006), 229–242.
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.
Stewart, K.W., Pretty, C.G., Tomlinson, H., Thomas, F.L., Homlok, J., Noémi, S.N., Illyés, A., Shaw, G.M., Benyó, B., Chase, J.G., Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis. Ann. Intensive care, 6, 2016, 24.
Uyttendaele V, Dickson J, Shaw G, Desaive T, Chase J: Improved Blood Glucose Forecasting Models using Changes in Insulin Sensitivity in Intensive Care Patients. 2017. https://orbi.uliege.be/handle/2268/207913.
Uyttendaele, V., Knopp, J.L., Stewart, K.W., Desaive, T., Benyó, B., Szabó-Némedi, N., Illyés, A., Shaw, G.M., Chase, J.G., A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control. Biomed. Signal Process. Control 46 (2018), 192–200.
Dickson, J.L., Stewart, K.W., Pretty, C.G., Flechet, M., Desaive, T., Penning, S., Lambermont, B.C., Benyó, B., Shaw, G.M., Chase, J.G., Generalisability of a virtual trials method for glycaemic control in intensive care. IEEE Trans. Biomed. Eng. 65 (2018), 1543–1553.
Chase, J.G., Preiser, J.C., Dickson, J.L., Pironet, A., Chiew, Y.S., Pretty, C.G., Shaw, G.M., Benyo, B., Moeller, K., Safaei, S., Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed. Eng. Online, 17, 2018, 24.
Stewart, K.W., Pretty, C.G., Tomlinson, H., Fisk, L., Shaw, G.M., Chase, J.G., Stochastic model predictive (stomp) glycaemic control for the intensive care unit: development and virtual trial validation. Biomed. Signal Process. Control 16 (2015), 61–67.
Docherty, P., Chase, J., David, T., Characterisation of the iterative integral parameter identification method. Med. Biol. Eng. Comput. 50 (2012), 127–134.
Hann, C.E., Chase, J.G., Shaw, G.M., Integral-based identification of patient specific parameters for a minimal cardiac model. Comput. Methods Programs Biomed. 81 (2006), 181–192.
Wong, X., Singh-Levett, I., Hollingsworth, L., Shaw, G., Hann, C., Lotz, T., Lin, J., Wong, O., Chase, J., A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients. Diabetes Technol. Ther. 8 (2006), 174–190.
Docherty, P., Chase, J.G., Lotz, T., Desaive, T., A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity. BioMed. Eng. OnLine, 10, 2011, 39.
Sheather, S.J., Density estimation. Statistical sci., 2004, 588–597.
Ghasemi, A., Zahediasl, S., Normality tests for statistical analysis: a guide for non-statisticians. Int. J. Endocrinol. Metab., 10, 2012, 486.
Kohavi, R, A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, Montreal, Canada, 1995, 1137–1145.