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

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.status: publishe

Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?

Critically ill patients are highly variable in their response to care and treatment. This variability and the search for improved outcomes have led to a significant increase in the use of protocolized care to reduce variability in care. However, protocolized care does not address the variability of outcome due to inter- and intra-patient variability, both in physiological state, and the response to disease and treatment. This lack of patient-specificity defines the opportunity for patient-specific approaches to diagnosis, care, and patient management, which are complementary to, and fit within, protocolized approaches

International recommendations for glucose control in adult non diabetic critically ill patients

The purpose of this research is to provide recommendations for the management of glycemic control in critically ill patients.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishedPour la Société Française d'Anesthésie-Réanimation (SFAR); Société de Réanimation de langue Française (SRLF) and the Experts grou

Software-guided versus nurse-directed blood glucose control in critically ill patients: the LOGIC-2 multicenter randomized controlled clinical trial

Blood glucose control in the intensive care unit (ICU) has the potential to save lives. However, maintaining blood glucose concentrations within a chosen target range is difficult in clinical practice and holds risk of potentially harmful hypoglycemia. Clinically validated computer algorithms to guide insulin dosing by nurses have been advocated for better and safer blood glucose control. We conducted an international, multicenter, randomized controlled trial involving 1550 adult, medical and surgical critically ill patients, requiring blood glucose control. Patients were randomly assigned to algorithm-guided blood glucose control (LOGIC-C, n = 777) or blood glucose control by trained nurses (Nurse-C, n = 773) during ICU stay, according to the local target range (80-110 mg/dL or 90-145 mg/dL). The primary outcome measure was the quality of blood glucose control, assessed by the glycemic penalty index (GPI), a measure that penalizes hypoglycemic and hyperglycemic deviations from the chosen target range. Incidence of severe hypoglycemia ( <40 mg/dL) was the main safety outcome measure. New infections in ICU, duration of hospital stay, landmark 90-day mortality and quality of life were clinical safety outcome measures. The median GPI was lower in the LOGIC-C (10.8 IQR 6.2-16.1) than in the Nurse-C group (17.1 IQR 10.6-26.2) (P  < 0.001). Mean blood glucose was 111 mg/dL (SD 15) in LOCIC-C versus 119 mg/dL (SD 21) in Nurse-C, whereas the median time-in-target range was 67.0% (IQR 52.1-80.1) in LOGIC-C versus 47.1% (IQR 28.1-65.0) in the Nurse-C group (both P  < 0.001). The fraction of patients with severe hypoglycemia did not differ between LOGIC-C (0.9%) and Nurse-C (1.2%) (P = 0.6). The clinical safety outcomes did not differ between groups. The sampling interval was 2.3 h (SD 0.5) in the LOGIC-C group versus 3.0 h (SD 0.8) in the Nurse-C group (P  < 0.001). In a randomized controlled trial of a mixed critically ill patient population, the use of the LOGIC-Insulin blood glucose control algorithm, compared with blood glucose control by expert nurses, improved the quality of blood glucose control without increasing hypoglycemia. ClinicalTrials.gov, NCT02056353 . Registered on 4 February 201

Software-guided versus nurse-directed blood glucose control in critically ill patients: the LOGIC-2 multicenter randomized controlled clinical trial

Blood glucose control in the intensive care unit (ICU) has the potential to save lives. However, maintaining blood glucose concentrations within a chosen target range is difficult in clinical practice and holds risk of potentially harmful hypoglycemia. Clinically validated computer algorithms to guide insulin dosing by nurses have been advocated for better and safer blood glucose control.status: publishe