Multicentric, Randomized, Controlled Trial to Evaluate Blood Glucose Control by the Model Predictive Control Algorithm Versus Routine Glucose Management Protocols in Intensive Care Unit Patients

Plank, Johannes; Bláha, J; Cordingley, Jeremy; Wilinska, Malgorzata E.; Chassin, Ludovic J.; Morgan, C.; Squire, S; Haluzı́k, Martin; Kremen, J; Svačina, Š; Toller, Wolfgang; Plasnik, A; Ellmerer, Martin; Hovorka, Roman; Pieber, Thomas R.

doi:10.2337/dc06-0838

Cited by 15 publications

(8 citation statements)

References 6 publications

(3 reference statements)

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“…The LOGIC-Insulin software required more frequent blood glucose measurements than in the nurse-directed protocol; however, the obtained sampling interval of 2.2 h falls in the 2–3 h range that is applicable in routine glucose management protocols in at least three ICUs across Europe (38). In the future, clinically validated computerized algorithms for TGC will be integrated with continuous glucose monitoring sensors in a semiclosed loop system to allow nurses to handle the increased information output from the sensor and to decrease the workload of the frequent blood draws (39).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2013

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OBJECTIVETight blood glucose control (TGC) in critically ill patients is difficult and labor intensive, resulting in poor efficacy of glycemic control and increased hypoglycemia rate. The LOGIC-Insulin computerized algorithm has been developed to assist nurses in titrating insulin to maintain blood glucose levels at 80–110 mg/dL (normoglycemia) and to avoid severe hypoglycemia (<40 mg/dL). The objective was to validate clinically LOGIC-Insulin relative to TGC by experienced nurses.RESEARCH DESIGN AND METHODSThe investigator-initiated LOGIC-1 study was a prospective, parallel-group, randomized, controlled clinical trial in a single tertiary referral center. A heterogeneous mix of 300 critically ill patients were randomized, by concealed computer allocation, to either nurse-directed glycemic control (Nurse-C) or algorithm-guided glycemic control (LOGIC-C). Glycemic penalty index (GPI), a measure that penalizes both hypoglycemic and hyperglycemic deviations from normoglycemia, was the efficacy outcome measure, and incidence of severe hypoglycemia (<40 mg/dL) was the safety outcome measure.RESULTSBaseline characteristics of 151 Nurse-C patients and 149 LOGIC-C patients and study times did not differ. The GPI decreased from 12.4 (interquartile range 8.2–18.5) in Nurse-C to 9.8 (6.0–14.5) in LOGIC-C (P < 0.0001). The proportion of study time in target range was 68.6 ± 16.7% for LOGIC-C patients versus 60.1 ± 18.8% for Nurse-C patients (P = 0.00016). The proportion of severe hypoglycemic events was decreased in the LOGIC-C group (Nurse-C 0.13%, LOGIC-C 0%; P = 0.015) but not when considered as a proportion of patients (Nurse-C 3.3%, LOGIC-C 0%; P = 0.060). Sampling interval was 2.2 ± 0.4 h in the LOGIC-C group versus 2.5 ± 0.5 h in the Nurse-C group (P < 0.0001).CONCLUSIONSCompared with expert nurses, LOGIC-Insulin improved efficacy of TGC without increasing rate of hypoglycemia.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2013

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“…A major limitation of the previous version of the MPC was the need for hourly blood glucose measurements (29). It was argued that frequent blood sampling may have biased the overall results toward the eMPC relative to the standard protocol.…”

Section: Discussionmentioning

confidence: 99%

Blood Glucose Control by a Model Predictive Control Algorithm with Variable Sampling Rate Versus a Routine Glucose Management Protocol in Cardiac Surgery Patients: A Randomized Controlled Trial

Hovorka

Kremen

Bláha

et al. 2007

The Journal of Clinical Endocrinology &Amp; Metabolism

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104

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“…Insulin was measured using a mouse ultra-sensitive ELISA kit (Mercodia). Homeostasis model assessment (HOMA) of insulin resistance was calculated using glucose and insulin measurements obtained using the following formula: [fasting glucose (mmol/L)] 3 [fasting insulin (mU/mL)]/ 22.5 (Plank et al 2006). GTTs were performed on mice that were fasted overnight (16-18 h).…”

Section: Measurements Of Insulin Resistancementioning

confidence: 99%

Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis

et al. 2011

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Although central to many studies of phenotypic variation and disease susceptibility, characterizing the genetic architecture of complex traits has been unexpectedly difficult. For example, most of the susceptibility genes that contribute to highly heritable conditions such as obesity and type 2 diabetes (T2D) remain to be identified despite intensive study. We took advantage of mouse models of diet-induced metabolic disease in chromosome substitution strains (CSSs) both to characterize the genetic architecture of diet-induced obesity and glucose homeostasis and to test the feasibility of gene discovery. Beginning with a survey of CSSs, followed with genetic and phenotypic analysis of congenic, subcongenic, and subsubcongenic strains, we identified a remarkable number of closely linked, phenotypically heterogeneous quantitative trait loci (QTLs) on mouse chromosome 6 that have unexpectedly large phenotypic effects. Although fine-mapping reduced the genomic intervals and gene content of these QTLs over 3000-fold, the average phenotypic effect on body weight was reduced less than threefold, highlighting the “fractal” nature of genetic architecture in mice. Despite this genetic complexity, we found evidence for 14 QTLs in only 32 recombination events in less than 3000 mice, and with an average of four genes located within the three body weight QTLs in the subsubcongenic strains. For Obrq2a1, genetic and functional studies collectively identified the solute receptor Slc35b4 as a regulator of obesity, insulin resistance, and gluconeogenesis. This work demonstrated the unique power of CSSs as a platform for studying complex genetic traits and identifying QTLs.

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Multicentric, Randomized, Controlled Trial to Evaluate Blood Glucose Control by the Model Predictive Control Algorithm Versus Routine Glucose Management Protocols in Intensive Care Unit Patients

Cited by 15 publications

References 6 publications

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

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

Blood Glucose Control by a Model Predictive Control Algorithm with Variable Sampling Rate Versus a Routine Glucose Management Protocol in Cardiac Surgery Patients: A Randomized Controlled Trial

Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis

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