[en] Background
Insulin therapy for glycaemic control (GC) in critically ill patients may improve outcomes by reducing
hyperglycaemia and glycaemic variability, which are both associated with increased morbidity and mortality.
However, initial positive results have proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted)
is a model-based glycaemic control protocol using a risk-based dosing approach. STAR uses a 2D stochastic
model to predict distributions of likely future changes in model-based insulin sensitivity (SI) based on
its current value, and determines the optimal intervention.
Objectives
This study investigates the impact of a new 3D stochastic model on the ability to predict more accurate
future SI distributions, which would allow more aggressive insulin dosing and improved glycaemic control.
Methods
The new 3D stochastic model is built using both current SI and its prior variation to predict future SI distribution
from 68,629 h of clinical data (819 GC episodes). The 5th-95th percentile range of predicted SI distribution
are calculated and compared with the 2D model.
Results
Results show the 2D model is over-conservative compared to the 3D case for more than 77% of the data,
predominantly where SI is stable (|%ΔSI| ≤ 25%). These formerly conservative prediction ranges are now
~30% narrower with the 3D model, which safely enables more aggressive insulin dosing for these patient
hours. In addition, distributions of predicted SI within the 5th-95th percentile range are much closer to the ideal
value of 90% for more patients with the 3D model.
Conclusions
The new 3D model better characterises patient specific metabolic variability and patient specific response
to insulin, allowing more optimal insulin dosing to increase performance and safety.
Research Center/Unit :
Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand GIGA-In Silico Medicine, Université de Liège, Belgique
Uyttendaele, Vincent ; Université de Liège - ULiège > Form. doct. sc. ingé. & techno. (aéro. & mécan. - Paysage)
Knopp, Jennifer L.
Stewart, Kent W.
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Balazs, Benyo
Szabo-Nemedi, Noemi
Illyes, Attila
Shaw, Geoffrey M.
Chase, J Geoffrey ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Language :
English
Title :
A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control
Publication date :
September 2018
Journal title :
Biomedical Signal Processing and Control
ISSN :
1746-8094
eISSN :
1746-8108
Publisher :
Elsevier, Netherlands
Volume :
45
Pages :
192-200
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture NZ National Science Challenge 7, Science for Technology and Innovation MedTech CoRE Erasmus+
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
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.
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. 78 (2003), 1471–1478.
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 ACM, The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit. Care, 13, 2009, R91.
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.
Krinsley, J.S., Glycemic variability and mortality in critically ill patients: the impact of diabetes. J. Diab. 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.
Reed, C.C., Stewart, R.M., Sherman, M., Myers, J.G., Corneille, M.G., Larson, N., Gerhardt, S., Beadle, R., Gamboa, C., Dent, D., et al. 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 1054-1045.
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.
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.
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.
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.
Krinsley, J., Glucose control reduces ICU stay and mortality. Perform. Improv. Advis., 9(4-6), 2005, 1.
Krinsley, J.S., Jones, R.L., Cost analysis of intensive glycemic control in critically ill adult patients. Chest 129 (2006), 644–650.
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.
Penning, S., Pretty, C., Preiser, J.C., Shaw, G.M., Desaive, T., Chase, J.G., Glucose control positively influences patient outcome: a retrospective study. J. Crit. Care 30 (2015), 455–459.
Finfer, S., Chittock, D.R., Su, S.Y., Blair, D., Foster, D., Dhingra, V., Bellomo, R., Cook, D., Dodek, P., Henderson, W.R., et al. Intensive versus conventional glucose control in critically ill patients. N. Engl. J. Med. 360 (2009), 1283–1297.
Finfer, S., Liu, B., Chittock, D.R., Norton, R., Myburgh, J.A., McArthur, C., Mitchell, I., Foster, D., Dhingra, V., Henderson, W.R., et al. Hypoglycemia and risk of death in critically ill patients. N. Engl. J. Med. 367 (2012), 1108–1118.
Brunkhorst, F.M., Engel, C., Bloos, F., Meier-Hellmann, A., Ragaller, M., Weiler, N., Moerer, O., Gruendling, M., Oppert, M., Grond, S., et al. Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N. Engl. J. Med. 358 (2008), 125–139.
Preiser, J.C., Devos, P., Ruiz-Santana, S., Melot, C., Annane, D., Groeneveld, J., Iapichino, G., Leverve, X., Nitenberg, G., Singer, P., et al. A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the glucontrol study. Intensive Care Med. 35 (2009), 1738–1748.
Wiener, R.S., Wiener, D.C., Larson, R.J., Benefits and risks of tight glucose control in critically ill adults: a meta-analysis. JAMA 300 (2008), 933–944.
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., Talmor, D., Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. CMAJ 180 (2009), 821–827.
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.
Finfer, S., Chittock, D.R., Su, S.Y., Blair, D., Foster, D., Dhingra, V., Intensive versus conventional glucose control in critically ill patients. N. Engl. J. Med., 360, 2009.
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., et al. 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.M., Wong, X.W., Lotz, T., Lin, J., Hann, C.E., Model-based glycaemic control in critical care - a review of the state of the possible. Biomed. Signal. Process. Control 1 (2006), 3–21.
Van Herpe, T., De Moor, B., Van den Berghe, G., Towards closed-loop glycaemic control. Best Pract. Res. Clin. Anaesthesiol. 23 (2009), 69–80.
Amrein, K., Ellmerer, M., Hovorka, R., Kachel, N., Parcz, D., Korsatko, S., Smolle, K., Perl, S., Bock, G., Doll, W., et al. Hospital glucose control: safe and reliable glycemic control using enhanced model predictive control algorithm in medical intensive care unit patients. Diab. Technol. Ther. 12 (2010), 405–412.
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. Diab. Sci. Technol. 6 (2012), 102–115.
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.
Evans, A., Shaw, G.M., Le Compte, A., Tan, C.S., Ward, L., Steel, J., Pretty, C.G., Pfeifer, L., Penning, S., Suhaimi, F., et al. Pilot proof of concept clinical trials of stochastic targeted (STAR) glycemic control. Ann. Intensive Care, 1, 2011, 38.
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.
Evans, A., Shaw, G.M., Compte, A., Tan, C.S., Ward, L., Steel, J., Pilot proof of concept clinical trials of stochastic targeted (STAR) glycemic control. Ann. Intensive Care, 1, 2011.
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.
Pretty, C.G., Signal, M., Fisk, L., Penning, S., Le Compte, A., Shaw, G.M., Desaive, T., Chase, J.G., Impact of sensor and measurement timing errors on model-based insulin sensitivity. Comput. Methods Programs Biomed. 114 (2014), e79–86.
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.
Docherty, P.D., Chase, J.G., David, T., Characterisation of the iterative integral parameter identification method. Med. Biol. Eng. Comput. 50 (2012), 127–134.
Docherty, P.D., Chase, J.G., Lotz, T.F., 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.
Dickson, J., Floyd, R.P., Le Compte, A., Fisk, L., Chase, J.G., External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts. Biomed. Signal. Process. Control 8 (2013), 409–419.
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. Diab. Sci. Technol. 6 (2012), 1030–1037.
Dickson, J.L., Stewart, K.W., Pretty, C.G., Flechet, M., Desaive, T., Penning, S., Lambermont, B.C., Benyo, B., Shaw, G.M., Chase, G., Generalisability of a virtual trials method for glycaemic control in intensive care. IEEE Trans. Biomed. Eng., 2017, 1.
Uyttendaele, V., Dickson, J.L., Shaw, G.M., Desaive, T., Chase, J.G., Untangling glycaemia and mortality in critical care. Crit. Care, 21, 2017, 152.
Stewart, K.W., Pretty, C.G., Tomlinson, H., Thomas, F.L., Homlok, J., Noemi, S.N., Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis. Ann. Intensive Care, 6, 2016.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
