Glycemic Control In Critical Care Research Articles

Glycaemic control in critical care: Can flash glucose monitoring help?

Good glycaemic control confers an outcome benefit in both diabetic and non-diabetic critically unwell patients. Critically unwell patients receiving intravenous insulin in the intensive care unit (ICU) require hourly glucose monitoring. This brief communication highlights the impact of the introduction of the FreeStyle Libre glucose monitor, a form of continuous glucose monitoring, on the frequency of glucose recordings in patients receiving intravenous insulin in the ICU at York Teaching Hospital NHS Foundation Trust.

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

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.

Effectiveness of an evidence-based protocol for the control of stress-induced hyperglycaemia in critical care

AimTo assess the effectiveness of the implementation of a protocol for glycaemic control in critical care, in terms of maintenance of a pre-established target of blood glucose level, reduction of hyperglycaemia and prevention of severe hypoglycaemia. MethodProspective “pre-post” quasi-experimental study carried out in a general critical care unit. Adult patients treated with intravenous insulin were included. We recorded all glycaemic tests performed from November 2014 to August 2015 (pre-intervention) and from November 2015 to August 2016 (post-intervention). The intervention consisted of the implementation of an evidence-based glycaemic control protocol to achieve glycaemic levels in a range of 140–180mg/dl. Main variables analysed were: proportion of glycaemic tests in the target range, proportions of severe hypoglycaemia (under 40mg/dl) and hyperglycaemia over 200mg/dl. ResultsWe analysed 7864 glycaemic tests from 125 patients, 66 pre-intervention and 59 post-intervention. Average age was 66.24±13.99 years, 64% of patients were male. The proportion of tests within the target range was higher in the intervention group (38.82 vs. 44.34 p<.001). Only one case of severe hypoglycaemia was identified, which happened in the pre-intervention period. The rate of severe hyperglycaemia was lower in the post-intervention group (19.19 vs. 16.28 p=.001). ConclusionsOur experience shows that implementation of evidence-based interventions can improve glycaemic control during critical illness. We found higher glycaemia levels in the target range. The protocol proved useful in the prevention of severe hypoglycaemia. Nurse-led interventions based on clinical data improved health results in our patients.

Efectividad de un protocolo basado en la evidencia para el control de la hiperglucemia por estrés en cuidados intensivos

AimTo assess the effectiveness of the implementation of a protocol for glycaemic control in critical care, in terms of maintenance of a pre-established target of blood glucose level, reduction of hyperglycaemia and prevention of severe hypoglycaemia. MethodProspective “pre-post” quasi-experimental study carried out in a general critical care unit. Adult patients treated with intravenous insulin were included. We recorded all glycaemic tests performed from November 2014 to August 2015 (pre-intervention) and from November 2015 to August 2016 (post-intervention). The intervention consisted of the implementation of an evidence-based glycaemic control protocol to achieve glycaemic levels in a range of 140-180mg/dl. Main variables analysed were: proportion of glycaemic tests in the target range, proportions of severe hypoglycaemia (under 40mg/dl) and hyperglycaemia over 200mg/dl. ResultsWe analysed 7864 glycaemic tests from 125 patients, 66 pre-intervention and 59 post-intervention. Average age was 66.24±13.99 years, 64% of patients were male. The proportion of tests within the target range was higher in the intervention group (38.82 vs. 44.34 p<.001). Only one case of severe hypoglycaemia was identified, which happened in the pre-intervention period. The rate of severe hyperglycaemia was lower in the post-intervention group (19.19 vs. 16.28 p=.001). ConclusionsOur experience shows that implementation of evidence-based interventions can improve glycaemic control during critical illness. We found higher glycaemia levels in the target range. The protocol proved useful in the prevention of severe hypoglycaemia. Nurse-led interventions based on clinical data improved health results in our patients.

Interface Design and Human Factors Considerations for Model-Based Tight Glycemic Control in Critical Care

Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input. The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload. The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user's immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users. The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.

Data Entry Errors and Design for Model-Based Tight Glycemic Control in Critical Care

Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. Model-based methods and computerized protocols offer the opportunity to improve TGC quality but require human data entry, particularly of blood glucose (BG) values, which can be significantly prone to error. This study presents the design and optimization of data entry methods to minimize error for a computerized and model-based TGC method prior to pilot clinical trials. To minimize data entry error, two tests were carried out to optimize a method with errors less than the 5%-plus reported in other studies. Four initial methods were tested on 40 subjects in random order, and the best two were tested more rigorously on 34 subjects. The tests measured entry speed and accuracy. Errors were reported as corrected and uncorrected errors, with the sum comprising a total error rate. The first set of tests used randomly selected values, while the second set used the same values for all subjects to allow comparisons across users and direct assessment of the magnitude of errors. These research tests were approved by the University of Canterbury Ethics Committee. The final data entry method tested reduced errors to less than 1-2%, a 60-80% reduction from reported values. The magnitude of errors was clinically significant and was typically by 10.0 mmol/liter or an order of magnitude but only for extreme values of BG < 2.0 mmol/liter or BG > 15.0-20.0 mmol/liter, both of which could be easily corrected with automated checking of extreme values for safety. The data entry method selected significantly reduced data entry errors in the limited design tests presented, and is in use on a clinical pilot TGC study. The overall approach and testing methods are easily performed and generalizable to other applications and protocols.

The metrics of glycaemic control in critical care

Trials of tight glucose control have compared measures of central tendency, such as average blood glucose, and yielded conflicting results. Other metrics, such as standard deviation, reflect different properties of glucose control and are also associated with changes in outcome. It is possible, therefore, that the conflicting results from interventional studies arise from effects on glycaemic control that have not been reported. Using glucose measurements from patients admitted to four adult intensive care units in one UK hospital, we sought to identify metrics of glycaemic control, examine the relationship between them and identify the metrics that are both independently and most strongly associated with outcome. We examined nine previously described metrics and identified a further four. Cluster analysis classified these metrics into two families, namely, those reflecting measures of central tendency and those reflecting measures of dispersion. A measure of minimum glucose was also identified but related to neither family. Plots of the quintiles of these metrics against hospital mortality revealed population-specific relationships. Areas under receiver-operating characteristic curves could not identify an optimum metric of central tendency or dispersion. Using odds ratios, we were able to show that the effect of these metrics is independent of one another. Our results suggest that glycaemic control is associated with outcome on the basis of three independent metrics, reflecting measures of central tendency, measures of dispersion and a measure of minimum glucose.

Insulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care

AbstractEffective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a patient's stay, are a function of variability in insulin sensitivity/resistance resulting from the level and evolution of stress response, and are independently associated with mortality. This study examines the daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, 54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points (25th and 75th percentiles) are used to assess differences between groups and their evolution over time. Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < 0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < 0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability with lower SI early in a patient's stay greatly increases the difficulty in achieving and safely maintaining glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will require greater measurement frequency, reduced reliance on insulin, and more explicit specification of carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome.

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric ( S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

Continuous Glucose Monitors and the Burden of Tight Glycemic Control in Critical Care: Can They Cure the Time Cost?

Tight glycemic control (TGC) in critical care has shown distinct benefits but has also proven to be difficult to obtain. The risk of severe hypoglycemia (<40 mg/dl) raises significant concerns for safety. Added clinical burden has also been an issue. Continuous glucose monitors (CGMs) offer frequent automated measurement and thus the possibility of using them for early detection and intervention of hypoglycemic events. Additionally, regular measurement by CGM may also be able to reduce clinical burden. An in silico study investigates the potential of CGM devices to reduce clinical effort in a published TGC protocol. This study uses retrospective clinical data from the Specialized Relative Insulin Nutrition Titration (SPRINT) TGC study covering 20 patients from a benchmark cohort. Clinically validated metabolic system models are used to generate a blood glucose (BG) profile for each patient, resulting in 33 continuous, separate BG episodes (6881 patient hours). The in silico analysis is performed with three different stochastic noise models: two Gaussian and one first-order autoregressive. The noisy, virtual CGM BG values are filtered and used to drive the SPRINT TGC protocol. A simple threshold alarm is used to trigger glucose interventions to avert potential hypoglycemia. The Monte Carlo method was used to get robust results from the stochastic noise models. Using SPRINT with simulated CGM noise, the BG time in an 80-110 mg/dl band was reduced no more than 4.4% to 45.2% compared to glucometer sensors. Antihypoglycemic interventions had negligible effect on time in band but eliminated all recorded hypoglycemic episodes in these simulations. Assuming 4-6 calibration measurements per day, the nonautomated clinical measurements are reduced from an average of 16 per day to as low as 4. At 2.5 min per glucometer measurement, a daily saving of approximately 25-30 min per patient could potentially be achieved. This paper has analyzed in silico the use of CGM sensors to provide BG input data to the SPRINT TGC protocol. A very simple algorithm was used for early hypoglycemic detection and prevention and tested with four different-sized intravenous glucose boluses. Although a small decrease in time in band (still clinically acceptable) was experienced with the addition of CGM noise, the number of hypoglycemic events was reduced. The reduction to time in band depends on the specific CGM sensor error characteristics and is thus a trade-off for reduced nursing workload. These results justify a pilot clinical trial to verify this study.

The Glucosafe system for tight glycemic control in critical care: A pilot evaluation study

Purpose “Glucosafe’ is a new model-based decision support system for glycemic control in critical care. Safety and achievement of glycemic goals using the system are tested prospectively. Methods Four penalty functions were developed to balance regimens of nutrition and insulin therapy against model-predicted glycemic outcome. The system advises the regimen where the penalty sum is minimal. An interactive interface allows advice alterations. Ten hyperglycemic patients (median Acute Physiology and Chronic Health Evaluation II, 12.5; interquartile range, 7.5-16.3) from a neuro and trauma intensive care unit were included for pilot testing using Glucosafe for 12 to 14 hours. Glycemic outcomes were compared to the 24-hour intervals before and after intervention. Results Hypoglycemia (blood glucose [BG] <3.5 mmol/L) was not observed. Mean log-normal BG ± standard deviation was reduced from 8.6 ± 2.4 mmol/L preintervention to 7.0 ± 1.1 mmol/L during the intervention. Nine patients reached the 4.4- to 6.1-mmol/L band after a mean 5 hours. At 5 hours intervention, mean log-normal BG was 6.7 mmol/L, 40% of measurements were in the 4.4- to 6.1-mmol/L band, and 84% were in the 4.4- to 7.75-mmol/L band. Conclusions Safety was demonstrated with the developed penalty functions. The low BG variance achieved may permit minor adjustments of the penalty function values to reduce average BG if desired.

PNO3-4 The concept of minimal mathematical modelling to understand and improve glycemic control in critical care

PNO3-4 The concept of minimal mathematical modelling to understand and improve glycemic control in critical care

Model-Based Insulin and Nutrition Administration for Tight Glycaemic Control in Critical Care

Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration. Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results. None directly address both the practical clinical difficulties and significant patient variation seen in general critical care, while also providing tight control. The approach presented manages both nutritional inputs and exogenous insulin infusions using tables simplified from a model-based, computerised protocol. Unique delivery aspects include bolus insulin delivery for safety and variable enteral nutrition rates. Unique development aspects include the use of simulated virtual patient trials created from retrospective data. The model, protocol development, and first 50 clinical case results are presented. High qualitative correlation to within +/-10% between simulated virtual trials and published clinical results validates the overall approach. Pilot tests covering 7358 patient hours produced an average glucose of 5.9 +/- 1.1 mmol/L. Time in the 4-6.1 mmol/L band was 59%, with 84% in 4.0-7.0 mmol/L, and 92% in 4.0-7.75 mmol/L. The average feed rate was 63% of patient specific goal feed and the average insulin dose was 2.6U/hour. There was one hypoglycaemic measurement of 2.1 mmol/L. No departures from protocol or clinical interventions were required at any time. Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels. Clinical results show very tight control in safe glycaemic bands. The approach could be readily adopted in any typical ICU.

Overview of Glycemic Control in Critical Care: Relating Performance and Clinical Results

Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoctitration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95(th) percentile (+/-2sigma) standard error (SE(95%)) to enable better comparison across cohorts. Model-based control trials lower blood glucose into a 72-110 mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE(95%) shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to the current one size fits all sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or one method fits all control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient.

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute 1-h glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalizable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies.

INSULIN + NUTRITION CONTROL FOR TIGHT CRITICAL CARE GLYCAEMIC REGULATION

A new insulin and nutrition control method for tight glycaemic control in critical care is presented from concept to clinical trials to clinical practice change. The primary results show that the method can provide very tight glycaemic control in critical care for a very critically ill cohort. More specifically, the final clinical practice change protocol provided 2100 hours of control with average blood glucose of .8 +/- 0.9 mmol/L for an initial 10 patient pilot study. It also used less insulin, while providing the same or greater nutritional input, as compared to retrospective hospital control for a relatively very critically ill cohort with high insulin resistance.