In-silico assessment of longer measurement intervals in glycaemic control to match clinical practice.

[en] BACKGROUND AND OBJECTIVE: STAR is a patient-specific glycaemic control (GC) framework accounting for both inter- and intra- patient variability to modulate insulin and nutrition in ICU patients. While providing safe, effective control to all patient, the workload induced by STAR represents a clinical burden in some ICUs. This study aims at extending the treatment interval of STAR from 1-3 hourly to 1-6 hourly to reduce the workload associated with STAR and assessing the impact on GC outcomes using virtual trials. METHODS: Retrospective data form 606 patients are used to create virtual patients. Insulin sensitivity is identified for each patient using a physiological model and used to build and validate the new stochastic models to provide up to 6-hourly predictions using five-fold cross-validation. Virtual trials are performed and safety, performance, nutrition intake and workload are compared and analysed. RESULTS: The extended STAR protocol 1-6 hourly measurement interval still provided high control safety and efficacy. Results showed slightly reduced %BG within the safe target band 4.4-8.0 mmol/L (from 83.8 to 81.4 %) as the measurement interval increased. It also resulted in an increased risk of hyper- (from 14.5 to 16.9 %BG > 8.0 mmol/L) and severe hypo- (from 0.03 to 0.05 %BG < 2.2 mmol/L) glycaemia. Insulin and nutrition rates decreased (from 3.5 [2.0 5.0] to 2.5 [1.7 3.0] U/h and from 100 [85 100] to 89 [71 100] % goal feed (GF) respectively). The workload was significantly reduced from 12 to 8 measurements per day. CONCLUSIONS: The workload was successfully reduced by extending the measurement interval, approaching clinical practice. High performance and safety are achieved. However, the results also highlight a clear risk and reward trade-off in glycaemic control with the increased risk of hyper- and hypo- glycaemia and the reduced nutrition rates. Choosing an intermediate measurement interval could be an interesting solution. Clinical trials should be conducted to further confirm those results and consider the adoption of longer treatment intervals in STAR GC framework.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others
Human health sciences: Multidisciplinary, general & others
Anesthesia & intensive care

Author, co-author :

Seret, Marie ; Université de Liège - ULiège > GIGA > GIGA Molecular & Computational Biology - Model-based therapeutics

Uyttendaele, Vincent ; Université de Liège - ULiège > GIGA > GIGA Molecular & Computational Biology - Model-based therapeutics

Chase, J Geoffrey ; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand

Desaive, Thomas ; Université de Liège - ULiège > GIGA > GIGA Molecular & Computational Biology - Model-based therapeutics

Language :

English

Title :

In-silico assessment of longer measurement intervals in glycaemic control to match clinical practice.

Publication date :

25 April 2025

Journal title :

Computer Methods and Programs in Biomedicine

ISSN :

0169-2607

eISSN :

1872-7565

Publisher :

Elsevier, Ireland

Volume :

267

Pages :

108806

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.sciencedirect.com/science/article/pii/S0169260725002238

Funders :

SPF BOSA - Service Public Fédéral Stratégie et Appui
EU - European Union

Funding number :

DIGITWIN4PH; 872488 – DCPM

Funding text :

The authors acknowledge the support of the Service Public Fédéral Stratégie et Appui (BOSA) - Project DIGITWIN4PH; and the EU H2020 R&I programme (MSCA-RISE-2019 call) agreement #872488 – DCPM

Available on ORBi :

since 12 May 2025

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Bibliography

Vedantam, D., Poman, D.S., Motwani, L., Asif, N., Patel, A., Anne, K.K., Stress-induced hyperglycemia: consequences and management. Cureus., 14, 2022, e26714, 10.7759/cureus.26714.
Annetta, M.G., Ciancia, M., Soave, M., Proietti, R., Diabetic and nondiabetic hyperglycemia in the ICU. Curr. Anaesth. Crit. Care 17 (2006), 385–390, 10.1016/j/cacc.2006.12.001.
Hsu, C.W., Glycemic control in critically ill patients. World J. Crit. Care Med. 1 (2012), 31–39, 10.5492/wjccm.v1.i1.31.
Van Cromphaut, S.J., Hyperglycaemia as part of the stress response: the underlying mechanisms. Best. Pract. Res. Clin. Anaesthesiol. 23 (2009), 375–386, 10.1016/j.bpa.2009.08.005.
Dungan, K.M., Braithwaite, S.S., Preiser, J.C., Stress hyperglycaemia. Lancet 373 (2009), 1798–1807, 10.1016/S0140-6736(09)60553-5.
Godinjak, A., Iglica, A., Burekovic, A., Jusufovic, S., Ajanovic, A., Tancica, I., Kukuljac, A., Hyperglycemia in critically ill patients: management and prognosis. Med. Arch. 69 (2015), 157–160, 10.5455/medarh.2015.69.157-160.
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, 10.1097/CCM.0b013e3181810378.
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, 10.1186/cc7921.
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, 10.1186/cc9224.
Krinsley, J.S., Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit. Care Med. 36 (2008), 3008–3013, 10.1097/CCM.0b013e31818b38d2.
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, 10.1177/193229681200600506.
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, 10.1016/j.bspc.2006.03.002.
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, 10.1016/j.jcrc.2014.12.013.
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, 10.1056/nejmoa011300.
Brunkhorst, F.M., Engel, C., Bloos, F., Meier-Hellmann, A., Ragaller, M., Weiler, N., Moerer, O., Gruendling, M., Oppert, M., Grond, S., Olthoff, D., Jaschinski, U., John, S., Rossaint, R., Welte, T., Schaefer, M., Kern, P., Kuhnt, E., Kiehntopf, M., Hartog, C., Natanson, C., Loeffler, M., Reinhart, K., German, S., Competence Network, intensive insulin therapy and pentastarch resuscitation in severe sepsis. N. Engl. J. Med. 358 (2008), 125–139, 10.1056/NEJMoa070716.
De La Rosa, C., Donado, J.H., Restrepo, A.H., Quintero, A.M., Gonzalez, L.G., Saldarriaga, N.E., Bedoya, M., Toro, J.M., Velasquez, J.B., Valencia, J.C., Arango, C.M., Aleman, P.H., Vasquez, E.M., Chavarriaga, J.C., Yepes, A., Pulido, W., Cadavid, C.A., Strict glycaemic control in patients hospitalised in a mixed medical and surgical intensive care unit: a randomised clinical trial. Crit. Care, 12, 2008, R120, 10.1186/cc7017.
Finfer, S., Chittock, D.R., Su, S.Y., Blair, D., Foster, D., Dhingra, V., Bellomo, R., Cook, D., Dodek, P., Henderson, W.R., Hebert, P.C., Heritier, S., Heyland, D.K., McArthur, C., McDonald, E., Mitchell, I., Myburgh, J.A., Norton, R., Potter, J., Robinson, B.G., Ronco, J.J., Intensive versus conventional glucose control in critically ill patients. N. Engl. J. Med. 360 (2009), 1283–1297, 10.1056/NEJMoa0810625.
Kalfon, P., Giraudeau, B., Ichai, C., Guerrini, A., Brechot, N., Cinotti, R., Dequin, P.F., Riu-Poulenc, B., Montravers, P., Annane, D., Dupont, H., Sorine, M., Riou, B., Group, C.-R.S., Tight computerized versus conventional glucose control in the ICU: a randomized controlled trial. Intensive Care Med. 40 (2014), 171–181, 10.1007/s00134-013-3189-0.
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, 10.1016/j.jamcollsurg.2006.12.047 discussion 1054-1055.
Egi, M., Bellomo, R., Reducing glycemic variability in intensive care unit patients: a new therapeutic target?. J. Diabetes. Sci. Technol. 3 (2009), 1302–1308, 10.1177/193229680900300610.
Preiser, J.C., Devos, P., Ruiz-Santana, S., Melot, C., Annane, D., Groeneveld, J., Iapichino, G., Leverve, X., Nitenberg, G., Singer, P., Wernerman, J., Joannidis, M., Stecher, A., Chiolero, R., 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, 10.1007/s00134-009-1585-2.
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, 10.1186/s13054-017-1725-y.
Uyttendaele, V., Knopp, J.L., Shaw, G., Desaive, T., Chase, J.G., Is intensive insulin therapy the scapegoat for or cause of hypoglycaemia and poor outcome?. IFAC J Syst Control, 9, 2019, 10.1016/j.ifacsc.2019.100063.
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, 10.4065/mcp.2009.0394.
Finfer, S., Liu, B., Chittock, D.R., Norton, R., Myburgh, J.A., McArthur, C., Mitchell, I., Foster, D., Dhingra, V., Henderson, W.R., Ronco, J.J., Bellomo, R., Cook, D., McDonald, E., Dodek, P., Hebert, P.C., Heyland, D.K., Robinson, B.G., Hypoglycemia and risk of death in critically ill patients. N. Engl. J. Med. 367 (2012), 1108–1118, 10.1056/NEJMoa1204942.
Krinsley, J.S., Grover, A., Severe hypoglycemia in critically ill patients: risk factors and outcomes. Crit. Care Med. 35 (2007), 2262–2267, 10.1097/01.CCM.0000282073.98414.4B.
Krinsley, J.S., Schultz, M.J., Spronk, P.E., Harmsen, R.E., van Braam Houckgeest, F., van der Sluijs, J.P., Melot, C., Preiser, J.C., Mild hypoglycemia is independently associated with increased mortality in the critically ill. Crit. Care, 15, 2011, R173, 10.1186/cc10322.
Lacherade, J.C., Jacqueminet, S., Preiser, J.C., An overview of hypoglycemia in the critically ill. J. Diabetes. Sci. Technol. 3 (2009), 1242–1249, 10.1177/193229680900300603.
Chase, J.G., Desaive, T., Bohe, J., Cnop, M., De Block, C., Gunst, J., Hovorka, R., Kalfon, P., Krinsley, J., Renard, E., Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas. Crit. Care 22 (2018), 1–10, 10.1186/s13054-018-2110-1.
Suhaimi, F., Compte, A.L., Preiser, J.C., Shaw, G.M., Massion, P., Radermecker, R., Pretty, C.G., Lin, J., Desaive, T., Chase, J.G., What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies. J. Diabetes. Sci. Technol. 4 (2010), 284–298, 10.1177/193229681000400208.
Chase, J.G., Le Compte, A.J., Preiser, J.C., Shaw, G.M., Penning, S., Desaive, T., Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?. Ann. Intensive Care, 1, 2011, 11, 10.1186/2110-5820-1-11.
Doola, R., Dupuis, C., Preiser, J.C., Nutrition support, carbohydrate feeding and insulin sensitivity in the critically ill patient: a complex relationship. Curr. Opin. Clin. Nutr. Metab. Care 27 (2024), 350–354, 10.1097/MCO.0000000000001039.
Chase, J.G., Hann, C., Shaw, G., Wong, J., Lin, J., Lotz, T., Le Compte, A., Lonergan, T., Overview of Glycemic control in critical care: relating performance and clinial results. J. Diabetes. Sci. Technol. 1 (2007), 82–91, 10.1177/193229680700100113.
Lonergan, T., Le Compte, A., Willacy, M., Chase, J.G., Shaw, G.M., Wong, X.W., Lotz, T., Lin, J., Hann, C.E., A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison. Diabetes. Technol. Ther. 8 (2006), 191–206, 10.1089/dia.2006.8.191.
Chase, J.G., Benyo, B., Desaive, T., Glycemic control in the intensive care unit: a control systems perspective. Annu Rev. Control 48 (2019), 359–368, 10.1016/j.arcontrol.2019.03.007.
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, 10.1186/s13613-016-0125-9.
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, 10.1109/TBME.2012.2214384.
Ahamad, N., Razak, N., Suhaimi, F., Jamaluddin, U., Pretty, C.G., Chase, J.G., Ralib, A., Noor, B.M., Efficacy and safety of SPRINT and STAR protocol on Malaysian critically-ill patients. IEEE EMBS Conf. Biomed. Eng. Sci., 2016, 370–375, 10.1109/IECBES.2016.7843476.
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, 10.1016/j.cmpb.2007.04.006.
Uyttendaele, V., Knopp, J.L., Stewart, K.W., Desaive, T., Benyo, B., Szabo-Nemedi, N., Illyes, A., Shaw, G., 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, 10.1016/j.bspc.2018.05.032.
Uyttendaele, V., Knopp, J.L., Davidson, S., Desaive, T., Benyo, B., Shaw, G.M., Chase, J.G., 3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation. Biomed. Eng. Online 18 (2019), 1–18, 10.1186/s12938-019-0720-8.
Uyttendaele, V., Knopp, J.L., Desaive, T., Chase, J.G., Clinical trial validation of the STAR-3D glycemic control framework. IFAC-PapersOnLine 56 (2023), 4758–4763, 10.1016/j.ifacol.2023.10.1239.
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, 10.1177/193229681200600113.
Abu-Samah, A., Knopp, J.L., Abdul Razak, N.N., Razak, A.A., Jamaludin, U.K., Mohamad Suhaimi, F., Md Ralib, A., Mat Nor, M.B., Chase, J.G., Pretty, C.G., Model-based glycemic control in a Malaysian intensive care unit: performance and safety study. Med. Devices (Auckl) 12 (2019), 215–226, 10.2147/MDER.S187840.
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, 10.1186/1475-925X-9-84.
Uyttendaele, V., Knopp, J.L., Pirotte, M., Morimont, P., Lambermont, B.C., Shaw, G., Desaive, T., Chase, J.G., Translating A Risk-based Glycaemic Control Framework for Critically Ill Patients: STAR-Liège. 2020, IFAC-PapersOnLine.
Carayon, P., Gurses, A.P., A human factors engineering conceptual framework of nursing workload and patient safety in intensive care units. Intensive Crit. Care Nurs. 21 (2005), 284–301, 10.1016/j.iccn.2004.12.003.
Chase, J.G., Andreassen, S., Jensen, K., Shaw, G.M., Impact of human factors on clinical protocol performance: a proposed assessment framework and case examples. J. Diabetes. Sci. Technol. 2 (2008), 409–416, 10.1177/193229680800200310.
Holzinger, U., Warszawska, J., Kitzberger, R., Wewalka, M., Miehsler, W., Herkner, H., Madl, C., Real-time continuous glucose monitoring in critically ill patients: a prospective randomized trial. Diabetes Care 33 (2010), 467–472, 10.2337/dc09-1352.
Krinsley, J.S., Chase, J.G., Gunst, J., Martensson, J., Schultz, M.J., Taccone, F.S., Wernerman, J., Bohe, J., De Block, C., Desaive, T., Kalfon, P., Preiser, J.C., Continuous glucose monitoring in the ICU: clinical considerations and consensus. Crit. Care, 21, 2017, 197, 10.1186/s13054-017-1784-0.
Nielsen, C.G., Grigonyte-Daraskeviciene, M., Olsen, M.T., Moller, M.H., Norgaard, K., Perner, A., Martensson, J., Pedersen-Bjergaard, U., Kristensen, P.L., Bestle, M.H., Accuracy of continuous glucose monitoring systems in intensive care unit patients: a scoping review. Intensive Care Med., 2024, 10.1007/s00134-024-07663-6.
Zhou, T., Dickson, J.L., Shaw, G.M., Chase, J.G., Continuous glucose monitoring measures can Be used for glycemic control in the ICU: an In-silico study. J. Diabetes. Sci. Technol. 12 (2018), 7–19, 10.1177/1932296817738791.
Uyttendaele, V., Knopp, J.L., Shaw, G.M., Desaive, T., Chase, J.G., Risk and reward: extending stochastic glycaemic control intervals to reduce workload. Biomed. Eng. Online, 19, 2020, 26, 10.1186/s12938-020-00771-6.
Bruyneel, A., Larcin, L., Martins, D., Van Den Bulcke, J., Leclercq, P., Pirson, M., Cost comparisons and factors related to cost per stay in intensive care units in Belgium. BMC. Health Serv. Res., 23, 2023, 986, 10.1186/s12913-023-09926-2.
Grimm, C., Dickel, S., Sachkova, A., Popp, M., Golinksi, M., Fichtner, F., Kranke, P., Seeber, C., Laudi, S., Voigt-Radloff, S., Moerer, O., Targeted minimal staff-to-patient ratios are unachievable - A nationwide survey in German ICUs during the COVID-19 pandemic. Cureus., 13, 2021, e15755, 10.7759/cureus.15755.
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, 10.1016/j.cmpb.2010.12.008.
Stewart, K.W., Chase, J.G., Pretty, C.G., Shaw, G.M., Nutrition delivery, workload and performance in a model-based ICU glycaemic control system. Comput. Methods Programs Biomed. 166 (2018), 9–18, 10.1016/j.cmpb.2018.09.005.
Stewart, K.W., Chase, J.G., Pretty, C.G., Shaw, G.M., Nutrition delivery of a model-based ICU glycaemic control system. Ann. Intensive Care, 8, 2018, 4, 10.1186/s13613-017-0351-9.
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, 10.1016/j.cmpb.2010.11.006.
Lin, J., Lee, D., Chase, J.G., Shaw, G.M., Hann, C., Lotz, T., Wong, J., Stochastic modelling of insulin sensitivity variability in critical care. Biomed. Signal Process. Control 1 (2006), 229–242, 10.1016/j.bspc.2006.09.003.
Refaeilzadeh, P., Tang, L., Liu, H., Cross-validation. Encyclopedia Database Syst., 2009, 532–538.
Dickson, J.L., Stewart, K.W., Pretty, C.G., Flechet, M., Desaive, T., Penning, S., Lambermont, B.C., Benyo, 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, 10.1109/TBME.2017.2686432.
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, 10.1186/cc6868.
Stewart, K.W., Pretty, C.G., Shaw, G.M., Chase, J.G., Interpretation of retrospective BG measurements. J. Diabetes. Sci. Technol. 12 (2018), 967–975, 10.1177/1932296818786518.
Eslami, S., de Keizer, N.F., de Jonge, E., Schultz, M.J., Abu-Hanna, A., A systematic review on quality indicators for tight glycaemic control in critically ill patients: need for an unambiguous indicator reference subset. Crit. Care, 12, 2008, R139, 10.1186/cc7114.
Finfer, S., Wernerman, J., Preiser, J.C., Cass, T., Desaive, T., Hovorka, R., Joseph, J.I., Kosiborod, M., Krinsley, J., Mackenzie, I., Mesotten, D., Schultz, M.J., Scott, M.G., Slingerland, R., Van den Berghe, G., Van Herpe, T., Clinical review: consensus recommendations on measurement of blood glucose and reporting glycemic control in critically ill adults. Crit. Care, 17, 2013, 229, 10.1186/cc12537.
Krinsley, J.S., Preiser, J.C., Time in blood glucose range 70 to 140 mg/dl >80% is strongly associated with increased survival in non-diabetic critically ill adults. Crit. Care, 19, 2015, 179, 10.1186/s13054-015-0908-7.
Davidson, S.M., Pretty, C., Uyttendaele, V., Knopp, J., Desaive, T., Chase, J.G., Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data. Comput. Methods Programs Biomed., 182, 2019, 105043, 10.1016/j.cmpb.2019.105043.
Ferenci, T., Benyo, B., Kovacs, L., Fisk, L., Shaw, G.M., Chase, J.G., Daily evolution of insulin sensitivity variability with respect to diagnosis in the critically ill. PLoS. One, 8, 2013, e57119, 10.1371/journal.pone.0057119.
Heyland, D.K., Cahill, N., Day, A.G., Optimal amount of calories for critically ill patients: depends on how you slice the cake!. Crit. Care Med. 39 (2011), 2619–2626, 10.1097/CCM.0b013e318226641d.
Preiser, J.C., Arabi, Y.M., Berger, M.M., Casaer, M., McClave, S., Montejo-Gonzalez, J.C., Peake, S., Reintam Blaser, A., Van den Berghe, G., van Zanten, A., Wernerman, J., Wischmeyer, P., A guide to enteral nutrition in intensive care units: 10 expert tips for the daily practice. Crit. Care, 25, 2021, 424, 10.1186/s13054-021-03847-4.
Greabu, M., Badoiu, S.C., Stanescu, S. II, Miricescu, D., Totan, A.R., Badoiu, S.E., Costagliola, M., Jinga, V., Drugs interfering with insulin resistance and their influence on the associated hypermetabolic State in severe burns: a narrative review. Int. J. Mol. Sci., 22, 2021, 10.3390/ijms22189782.
Pretty, C., Chase, J.G., Le Compte, A., Lin, J., Shaw, G.M., Impact of metoprolol on insulin sensitivity in the ICU. IFAC Proc. Volumes 44 (2011), 1763–1767, 10.3182/20110828-6-IT-1002.01729.
Signal, M., Fisk, L., Shaw, G.M., Chase, J.G., Concurrent continuous glucose monitoring in critically ill patients: interim results and observations. J. Diabetes. Sci. Technol. 7 (2013), 1652–1653, 10.1177/193229681300700629.
Anger, K.E., Szumita, P.M., Barriers to glucose control in the intensive care unit. Pharmacotherapy: J. Hum. Pharmacol. Drug Therapy 26 (2006), 214–228, 10.1592/phco.26.2.214.
Carayon, P., Wetterneck, T.B., Rivera-Rodriguez, A.J., Hundt, A.S., Hoonakker, P., Holden, R., Gurses, A.P., Human factors systems approach to healthcare quality and patient safety. Appl. Ergon. 45 (2014), 14–25, 10.1016/j.apergo.2013.04.023.