LOGIC-Insulin Algorithm–Guided Versus Nurse-Directed Blood Glucose Control During Critical Illness

Herpe, Tom Van; Mesotten, Dieter; Wouters, Pieter; Herbots, Jacques; Voets, Evy; Buyens, Jo; Moor, Bart De; Berghe, Greet Van den

doi:10.2337/dc12-0584

Cited by 88 publications

(93 citation statements)

References 39 publications

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“…This simulation study, which used true clinical data from critically ill patients in whom TGC was done by the LOGIC-Insulin control system (21 ), showed that less stringent allowable error criteria may be needed for continuous than for intermittent glucose sensors. It also confirms the conclusion by Boyd and Bruns that quality specifications of intermittent glucose sensors may be different for CGM devices (20 ).…”

Section: Discussionmentioning

confidence: 99%

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Modeling of Effect of Glucose Sensor Errors on Insulin Dosage and Glucose Bolus Computed by LOGIC-Insulin

et al. 2014

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BACKGROUND Effective and safe glycemic control in critically ill patients requires accurate glucose sensors and adequate insulin dosage calculators. The LOGIC-Insulin calculator for glycemic control has recently been validated in the LOGIC-1 randomized controlled trial. In this study, we aimed to determine the allowable error for intermittent and continuous glucose sensors, on the basis of the LOGIC-Insulin calculator. METHODS A gaussian simulation model with a varying bias (0%–20%) and CV (−20% to +20%) simulated blood glucose values from the LOGIC-1 study (n = 149 patients) in 10 Monte Carlo steps. A clinical error grid system was developed to compare the simulated LOGIC-Insulin–directed intervention with the nominal intervention (0% bias, 0% CV). The severity of error measuring the clinical effect of the simulated LOGIC-Insulin intervention was graded as type B, C, and D errors. Type D errors were classified as acutely life-threatening (0% probability preferred). RESULTS The probability of all types of errors was lower for continuous sensors compared with intermittent sensors. The maximum total error (TE), defined as the first TE introducing a type B/C/D error, was similar for both sensor types. To avoid type D errors, TEs <15.7% for intermittent sensors and <17.8% for continuous sensors were required. Mean absolute relative difference thresholds for type C errors were 7.1% for intermittent and 11.0% for continuous sensors. CONCLUSIONS Continuous sensors had a lower probability for clinical errors than intermittent sensors at the same accuracy level. These simulations demonstrated the suitability of the LOGIC-Insulin control system for use with continuous, as well as intermittent, sensors.

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Section: Discussionmentioning

confidence: 99%

“…Recently, we clinically validated the LOGICInsulin blood glucose algorithm in an RCT (21 ). This software system, incorporating an advanced algorithm (22 ), advises the bedside nurse on insulin dosage (or glucose bolus in the event of hypoglycemia) and the timing of the next blood sampling.…”

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Modeling of Effect of Glucose Sensor Errors on Insulin Dosage and Glucose Bolus Computed by LOGIC-Insulin

et al. 2014

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“…In this regard, model-based glycaemic control methods have recently shown good safety and performance [28][29][30][31][32][33]. Their main advantage is the use of a computer model to identify patient-specific insulin sensitivity (SI), which quantitatively describes patient response to insulin.…”

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confidence: 99%

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Dickson

Stewart

Pretty

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-Background: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). Objective: This paper presents a modelbased virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. Methods: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross-simulation) were compared. Results: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and crosssimulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 hours due to different cohort starting BG and underlying SI. Conclusions and significance: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

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“…8 In the LOGIC-Insulin algorithm study, patients in the CIIP group experienced significantly less mild (BG < 70 mg/dL) moderate (BG < 60 mg/dL) and severe hypoglycemia (BG < 40 mg/dL) in comparison to patients treated with a paper-based insulin infusion protocol, while both groups achieved similar mean BG levels. 9 Comparable BG control with a reduction in the incidence of hypoglycemia was reported in other studies comparing computerized to paper-based insulin infusion protocols. [10][11][12] In January 2013 we converted all 4 adult ICUs in our hospital (2 medical and 2 surgical) to a CIIP.…”

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confidence: 67%

Reducing the Risk of Hypoglycemia Associated With Intravenous Insulin

Sandler

Misiasz

Jones

et al. 2014

J Diabetes Sci Technol

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SymposiumThe level of glycemic control needed to improve outcomes without increasing the risk of hypoglycemia in hospitalized patients with critical illness is controversial. Achieving reasonable control of blood glucose without causing an excess risk of hypoglycemia is especially challenging in patients with ESRD, a condition commonly encountered in the critically ill.In 2001, Van den Berghe and colleague's landmark study in a surgical intensive care unit (ICU) suggested that strict blood glucose (BG) control (mean 103 mg/dL) was associated with a mortality and morbidity benefit in comparison to standard therapy (mean 153 mg/dL).1 A subsequent study of patients in the medical ICU however failed to show similar benefits and hypoglycemia was a frequent complication.2 Meta-analysis of multiple studies shows an inconsistent mortality benefit with intensive glycemic control and highlights the increased risk of hypoglycemia.3 More recently, the NICE-SUGAR study compared all-cause and cause-specific mortality and morbidity (new organ failure, bacteremia, redcell transfusion, and volume resuscitation) in over 6100 surgical and medical ICU patients treated either with an intensive insulin regimen (target BG 81-108 mg/dL) or a standard insulin regimen (target BG less than 180 mg/dL). 4 This trial concluded that intensive BG control was associated with a significant increase in the incidence of both moderate hypoglycemia (BG 41-70 mg/dL), and severe hypoglycemia (BG ≤ 40 mg/dL). Both degrees of hypoglycemia were associated with increased all-cause mortality, and particularly cardiovascular mortality. 5The cumulative effect of repeated hypoglycemia-related stress responses increases the risk of cardiac arrhythmias, neurological impairment, seizures, and death. [5][6] In light of AbstractComputerized insulin infusion protocols have facilitated more effective blood glucose (BG) control in intensive care units (ICUs). This is particularly important in light of the risks associated with hypoglycemia. End stage renal disease (ESRD) increases the risk of insulin-induced hypoglycemia. We evaluated BG control in 210 patients in 2 medical ICUs and in 2 surgical ICUs who were treated with a computerized insulin infusion program (CIIP). Our CIIP was programmed for a BG target of 140-180 mg/dL for medical ICU patients or 120-160 mg/dL for surgical ICU patients. In addition, we focused on BG control in the 11% of our patients with ESRD. Mean BG was 147 ± 20 mg/dL for surgical ICU patients and 171 ± 26 mg/ dL for medical ICU patients. Of both surgical and medical ICU patients, 17% had 1 or more BG 60-79 mg/dL, while 3% of surgical ICU and 8% of medical ICU patients had 1 or more BG < 60 mg/dL. Mean BG in ESRD patients was 147 ± 16 mg/dL similar to 152 ± 23 mg/dL in patients without ESRD. Of ESRD patients, 41% had 1 or more BG < 79 mg/dL as compared with 17.8% of non-ESRD patients (P < .01). A higher BG target for medical ICU patients as compared with surgical ICU patients yielded comparably low rates of moderate or severe hypoglycemia....

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LOGIC-Insulin Algorithm–Guided Versus Nurse-Directed Blood Glucose Control During Critical Illness

Cited by 88 publications

References 39 publications

Modeling of Effect of Glucose Sensor Errors on Insulin Dosage and Glucose Bolus Computed by LOGIC-Insulin

Modeling of Effect of Glucose Sensor Errors on Insulin Dosage and Glucose Bolus Computed by LOGIC-Insulin

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Reducing the Risk of Hypoglycemia Associated With Intravenous Insulin

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