Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Glucose control in the ICU.

ABSTRACT: PURPOSE OF REVIEW:Critically ill patients usually develop hyperglycemia, which is associated with adverse outcome. Controversy exists whether the relationship is causal or not. This review summarizes recent evidence regarding glucose control in the ICU. RECENT FINDINGS:Despite promising effects of tight glucose control in pioneer randomized controlled trials, the benefit has not been confirmed in subsequent multicenter studies and one trial found potential harm. This discrepancy could be explained by methodological differences between the trials rather than by a different case mix. Strategies to improve the efficacy and safety of tight glucose control have been developed, including the use of computerized treatment algorithms. SUMMARY:The ideal blood glucose target remains unclear and may depend on the context. As compared with tolerating severe hyperglycemia, tight glucose control is well tolerated and effective in patients receiving early parenteral nutrition when provided with a protocol that includes frequent, accurate glucose measurements and avoids large glucose fluctuations. All patient subgroups potentially benefit, with the possible exception of patients with poorly controlled diabetes, who may need less aggressive glucose control. It remains unclear whether tight glucose control is beneficial or not in the absence of early parenteral nutrition.

SUBMITTER: Gunst J

PROVIDER: S-EPMC6774765 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Glucose Control in the ICU: A Continuing Story.

Project description:In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable.

| S-EPMC5094326 | biostudies-literature

Continuous glucose control in the ICU: report of a 2013 round table meeting.

Project description:Achieving adequate glucose control in critically ill patients is a complex but important part of optimal patient management. Until relatively recently, intermittent measurements of blood glucose have been the only means of monitoring blood glucose levels. With growing interest in the possible beneficial effects of continuous over intermittent monitoring and the development of several continuous glucose monitoring (CGM) systems, a round table conference was convened to discuss and, where possible, reach consensus on the various aspects related to glucose monitoring and management using these systems. In this report, we discuss the advantages and limitations of the different types of devices available, the potential advantages of continuous over intermittent testing, the relative importance of trend and point accuracy, the standards necessary for reporting results in clinical trials and for recognition by official bodies, and the changes that may be needed in current glucose management protocols as a result of a move towards increased use of CGM. We close with a list of the research priorities in this field, which will be necessary if CGM is to become a routine part of daily practice in the management of critically ill patients.

| S-EPMC4078395 | biostudies-literature

The use of continuous glucose monitoring combined with computer-based eMPC algorithm for tight glucose control in cardiosurgical ICU.

Project description:In postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm.Glucose control in eMPC-CGM group (n = 12) was compared with a control (C) group (n = 12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4-6.1?mmol/L.Of the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group.Our data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.

| S-EPMC3595106 | biostudies-other

Continuous glucose monitoring in the ICU: clinical considerations and consensus.

Project description:Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer benefit in the prevention of severe hyperglycemia and hypoglycemia by enabling insulin infusions to be adjusted more rapidly and potentially more accurately because trends in glucose concentrations can be more readily identified. Increasingly, it is apparent that a single glucose target/range may not be optimal for all patients at all times and, as with many other aspects of critical care patient management, a personalized approach to glucose control may be more appropriate. Here we consider some of the evidence supporting different glucose targets in various groups of patients, focusing on those with and without diabetes and neurological ICU patients. We also discuss some of the reasons why, despite evidence of benefit, CGM devices are still not widely employed in the ICU and propose areas of research needed to help move CGM from the research arena to routine clinical use.

| S-EPMC5535285 | biostudies-literature

Identifying elements of ICU care that families report as important but unsatisfactory: decision-making, control, and ICU atmosphere.

Project description:BackgroundOne in fi ve deaths in the United States occurs in the ICU, and many of these deaths are experienced as less than optimal by families of dying people. The current study investigated the relationship between family satisfaction with ICU care and overall ratings of the quality of dying as a means of identifying targets for improving end-of-life experiences for patients and families.MethodsThis multisite cross-sectional study surveyed families of patients who died in the ICU in one of 15 hospitals in western Washington State. Measures included the Family Satisfaction in the ICU (FS-ICU) and the Single-Item Quality of Dying (QOD-1) questionnaires. Associations between FS-ICU items and the QOD-1 were examined using multivariate linear regression controlling for patient and family demographics and hospital site.ResultsQuestionnaires were returned for 1,290 of 2,850 decedents (45%). Higher QOD-1 scores were significantly associated (all P < .05) with (1) perceived nursing skill and competence (β= 0.15), (2) support for family as decision-makers ( β= 0.10), (3) family control over the patient’s care( β= 0.18), and (4) ICU atmosphere (β= 0.12). FS-ICU items that received low ratings and correlated with higher QOD-1 scores (ie, important items with room for improvement) were (1) support of family as decision-maker, (2) family control over patient’s care, and (3) ICU atmosphere.ConclusionsIncreased support for families as decision-makers and for their desired level of control over patient care along with improvements in the ICU atmosphere were identified as aspects of the ICU experience that may be important targets for quality improvement.Trial registryClinicalTrials.gov; No.: NCT00685893; URL: www.clinicaltrials.gov.

| S-EPMC3494471 | biostudies-literature

Glucose as the Fifth Vital Sign: A Randomized Controlled Trial of Continuous Glucose Monitoring in a Non-ICU Hospital Setting.

Project description:ObjectiveThe current standard for hospital glucose management is point-of-care (POC) testing. We conducted a randomized controlled trial of real-time continuous glucose monitoring (RT-CGM) compared with POC in a non-intensive care unit (ICU) hospital setting.Research design and methodsA total of 110 adults with type 2 diabetes on a non-ICU floor received RT-CGM with Dexcom G6 versus usual care (UC). RT-CGM data were wirelessly transmitted from the bedside. Hospital telemetry monitored RT-CGM data and notified bedside nursing of glucose alerts and trends. Standardized protocols were used for interventions.ResultsThe RT-CGM group demonstrated significantly lower mean glucose (M∆ = -18.5 mg/dL) and percentage of time in hyperglycemia >250 mg/dL (-11.41%) and higher time in range 70-250 mg/dL (+11.26%) compared with UC (P values <0.05). Percentage of time in hypoglycemia was very low.ConclusionsRT-CGM can be used successfully in community-based hospital non-ICU settings to improve glucose management. Continuously streaming glucose readings may truly be the fifth vital sign.

| S-EPMC7576427 | biostudies-literature

Blood glucose control: Where are we?

Project description:Optimal blood glucose control warrants both early intensive therapy and individualization strategies in patients with diabetes mellitus.

| S-EPMC8504917 | biostudies-literature

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

Project description:Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

| S-EPMC5144164 | biostudies-literature

Enterococci at ICU

Project description:Enterococci from ICU stool samples

| PRJEB42663 | ENA

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU.

Project description:ObjectiveGlycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.Materials and methodsUsing electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing.ResultsOur forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%-16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%-94%.DiscussionML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values.ConclusionWe demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

| S-EPMC8324237 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data