Glucose control in pediatric intensive care unit patients using an insulin-glucose algorithm

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system.

An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered.

The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion

PURPOSE OF REVIEW

This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier.

RECENT FINDINGS

Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric

BACKGROUND

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.

METHODS

We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.

RESULTS

For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.

CONCLUSION

The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.

A unified Hyperglycemia and Diabetic ketoacidosis (DKA) insulin infusion protocol based on an Excel algorithm and implemented via Electronic Medical Record (EMR) in Intensive Care Units

BACKGROUND

To assess the efficacy of a unified hyperglycemia and diabetic ketoacidosis (DKA) insulin infusion protocol (IIP), based on an Excel algorithm and implemented as an electronic order set, in achieving glycemic targets and minimizing hypoglycemia.

METHODS

An IIP was instituted in medical and surgical intensive care units for post-cardiac surgery (PCS) and other stress hyperglycemia (SH), diabetes hyperglycemia (DH), and DKA. The IIP initiated therapeutic insulin rates at elevated blood glucose (BG), and decreased insulin when target range was achieved. A convenience sample (n=62) was studied; 20 PCS, 15 with DH, 9 with SH, 8 with diabetes on vasopressors, 7 with diabetes on glucocorticoids and 3 with DKA were assessed.

RESULTS

The protocol maintained BG at 144±24.7mg/dL for PCS and 167±36mg/dL for patients with diabetes mellitus. It maintained acceptable target range (ATR) (100mg/dL-180mg/dL) 89% of the time for PCS and 67% of the time for patients with diabetes mellitus. There were no measurements of BG<70mg/dL. The protocol lowered the BG at a similar rate and time period in those with diabetes, DKA and those with or without vasopressors or glucocorticoids. To determine long-term efficacy, a retrospective review of Point of Care (POC) RALS (Remote Automated Data System) BG data 2 years post implementation demonstrated fewer episodes of hypoglycemia<70mg/dL and hyperglycemia>240mg/dL and more BG values within ATR.

CONCLUSIONS

This IIP maintained ATR without hypoglycemia for patients in the ICU setting without requiring complex nursing calculations.

Automated hybrid closed-loop control with a proportional-integral-derivative based system in adolescents and adults with type 1 diabetes: individualizing settings for optimal performance

BACKGROUND

Automated insulin delivery systems, utilizing a control algorithm to dose insulin based upon subcutaneous continuous glucose sensor values and insulin pump therapy, will soon be available for commercial use. The objective of this study was to determine the preliminary safety and efficacy of initialization parameters with the Medtronic hybrid closed-loop controller by comparing percentage of time in range, 70-180 mg/dL (3.9-10 mmol/L), mean glucose values, as well as percentage of time above and below target range between sensor-augmented pump therapy and hybrid closed-loop, in adults and adolescents with type 1 diabetes.

METHODS

We studied an initial cohort of 9 adults followed by a second cohort of 15 adolescents, using the Medtronic hybrid closed-loop system with the proportional-integral-derivative with insulin feed-back (PID-IFB) algorithm. Hybrid closed-loop was tested in supervised hotel-based studies over 4-5 days.

RESULTS

The overall mean percentage of time in range (70-180 mg/dL, 3.9-10 mmol/L) during hybrid closed-loop was 71.8% in the adult cohort and 69.8% in the adolescent cohort. The overall percentage of time spent under 70 mg/dL (3.9 mmol/L) was 2.0% in the adult cohort and 2.5% in the adolescent cohort. Mean glucose values were 152 mg/dL (8.4 mmol/L) in the adult cohort and 153 mg/dL (8.5 mmol/L) in the adolescent cohort.

CONCLUSIONS

Closed-loop control using the Medtronic hybrid closed-loop system enables adaptive, real-time basal rate modulation. Initializing hybrid closed-loop in clinical practice will involve individualizing initiation parameters to optimize overall glucose control.

Design and rationale of Heart and Lung Failure - Pediatric INsulin Titration Trial (HALF-PINT): A randomized clinical trial of tight glycemic control in hyperglycemic critically ill children

Objectives

Test whether hyperglycemic critically ill children with cardiovascular and/or respiratory failure experience more ICU-free days when assigned to tight glycemic control with a normoglycemic versus hyperglycemic blood glucose target range.

Design

Multi-center randomized clinical trial.

Setting

Pediatric ICUs at 35 academic hospitals.

Patients

Children aged 2 weeks to 17 years receiving inotropic support and/or acute mechanical ventilation, excluding cardiac surgical patients.

Interventions

Patients receive intravenous insulin titrated to either 80–110 mg/dL (4.4–6.1 mmol/L) or 150–180 mg/dL (8.3–10.0 mmol/L). The intervention begins upon confirmed hyperglycemia and ends when the patient meets study-defined ICU discharge criteria or after 28 days. Continuous glucose monitoring, a minimum glucose infusion, and an explicit insulin infusion algorithm are deployed to achieve the BG targets while minimizing hypoglycemia risk.

Measurements and main results

The primary outcome is ICU-free days (equivalent to 28-day hospital mortality-adjusted ICU length of stay). Secondary outcomes include 90-day hospital mortality, organ dysfunction scores, ventilator-free days, nosocomial infection rate, neurodevelopmental outcomes, and nursing workload. To detect an increase of 1.25 ICU-free days (corresponding to a 20% relative reduction in 28-day hospital mortality and a one-day reduction in ICU length of stay), 1414 patients are needed for 80% power using a two-sided 0.05 level test.

Conclusions

This trial tests whether hyperglycemic critically ill children randomized to 80–110 mg/dL benefit more than those randomized to 150–180 mg/dL. This study implements validated bedside support tools including continuous glucose monitoring and a computerized algorithm to enhance patient safety and ensure reproducible bedside decision-making in achieving glycemic control.

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. These include the clinical staff, the patient's significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. The paper ends summarizing the lessons learned through the experiences of the authors and discussing the challenges arising from a large-scale integration of sensors and actuators.

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

BACKGROUND

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers.

METHOD

We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis.

RESULTS

For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%.

CONCLUSIONS

This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting.

Impact of Tight Glycemic Control on Neurodevelopmental Outcomes at 1 Year of Age for Children with Congenital Heart Disease: A Randomized Controlled Trial

OBJECTIVE

To assess the association of postoperative tight glycemic control and hypoglycemia in children undergoing cardiac surgery with neurodevelopmental outcomes at 1 year of age.

STUDY DESIGN

A 2-center, prospective, randomized trial of postoperative tight glycemic control vs standard care was conducted in 980 children undergoing cardiac surgery. Neurodevelopmental outcomes were assessed at nine to 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III), the Adaptive Behavior Assessment System, Second Edition, the Ages and Stages Questionnaire, Third Edition, and the Brief Infant Toddler Social-Emotional Assessment.

RESULTS

Neurodevelopmental follow-up was performed on 237 patients with a mean age of 13 months. No significant treatment group differences were found in the Bayley-III and Adaptive Behavior Assessment System, Second Edition composite scores or percentage at risk based on the Ages and Stages Questionnaire, Third Edition and the Brief Infant Toddler Social-Emotional Assessment. Patients who experienced moderate to severe hypoglycemia (n = 8) had lower Bayley-III composite scores compared with patients with no to mild hypoglycemia, even after controlling for factors known to be associated with poorer neurodevelopmental outcomes.

CONCLUSION

For infants undergoing cardiac surgery, tight glycemic control did not impact neurodevelopmental outcomes compared with standard care. These data suggest a possible association between moderate to severe hypoglycemia and poorer neurodevelopmental outcomes at 1 year of age.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT00443599.

Vascular Glucose Sensor Symposium: Continuous Glucose Monitoring Systems (CGMS) for Hospitalized and Ambulatory Patients at Risk for Hyperglycemia, Hypoglycemia, and Glycemic Variability

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, length of stay, and cost in a variety of critical care and non-critical care patient populations in the hospital. The results from prospective randomized clinical trials designed to determine the risks and benefits of intensive insulin therapy and tight glycemic control have been confusing; and at times conflicting. The limitations of point-of-care blood glucose (BG) monitoring in the hospital highlight the great clinical need for an automated real-time continuous glucose monitoring system (CGMS) that can accurately measure the concentration of glucose every few minutes. Automation and standardization of the glucose measurement process have the potential to significantly improve BG control, clinical outcome, safety and cost.

Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control

Closed-loop insulin delivery continues to be one of most promising strategies for achieving near-normal control of blood glucose levels in individuals with diabetes. Of the many components that need to work well for the artificial pancreas to be advanced into routine use, the algorithm used to calculate insulin delivery has received a substantial amount of attention. Most of that attention has focused on the relative merits of proportional-integral-derivative versus model-predictive control. A meta-analysis of the clinical data obtained in studies performed to date with these approaches is conducted here, with the objective of determining if there is a trend for one approach to be performing better than the other approach. Challenges associated with implementing each approach are reviewed with the objective of determining how these approaches might be improved. Results of the meta-analysis, which focused predominantly on the breakfast meal response, suggest that to date, the two approaches have performed similarly. However, uncontrolled variables among the various studies, and the possibility that future improvements could still be effected in either approach, limit the validity of this conclusion. It is suggested that a more detailed examination of the challenges associated with implementing each approach be conducted.

Glycemic Control in Pediatric Patients on Extracorporeal Membrane Oxygenation

OBJECTIVES To determine whether glycemic control has an effect on outcomes for pediatric patients on extracorporeal membrane oxygenation (ECMO) therapy, while controlling for multiple factors.

METHODS A single-center retrospective chart review was performed on 82 patients who required ECMO from January 1, 2008, to December 31, 2010. All glucose concentrations collected while patients were on ECMO were analyzed; multiple other factors that may have affected mortality were also recorded. Primary outcome was mortality, and secondary outcomes were length of time on ECMO and length of time until death or discharge from the hospital.

RESULTS Of 82 patients, 53 patients survived ECMO (64.6%). Glucose control had no effect on survival of patients on ECMO (p=0.56), even when controlling for multiple factors (p=0.48). Similarly, statistical evaluation showed no differences for hospital mortality in relationship to controlled serum glucose (p=0.50). Patients with controlled glucose spent an average of 31.5% more time on ECMO than non-controlled patients (p=0.048).

CONCLUSIONS In this study, glycemic control, defined as serum glucose concentration between 60 mg/dL and 250 mg/dL for &gt;95% of the time on ECMO, had no statistically significant effect on mortality for patients on ECMO. Future studies could focus on tighter glucose control or specific dextrose/glucose protocols to evaluate whether improved glucose control would have an effect on morbidity and mortality.

The artificial pancreas: is it important to understand how the β cell controls blood glucose?

It has been more than 7 years since the first fully automated closed-loop insulin delivery system that linked subcutaneous insulin delivery and glucose sensing was published. Since the initial report, the physiologic insulin delivery (PID) algorithm used to emulate the β cell has been modified from the original proportional-integral-derivative terms needed to fit the β cell's biphasic response to a hyperglycemic clamp to include terms emulating cephalic phase insulin release and the effect of insulin per se to inhibit insulin secretion. In this article, we compare the closed-loop glucose profiles obtained as each new term has been added, reassess the ability of the revised PID model to describe the β cells' insulin response to a hyperglycemic clamp, and look for the first time at its ability to describe the response to a hypoglycemic clamp. We also consider changes that might be added to the model based on perfused pancreas data. We conclude that the changes introduced in the PID model have systematically improved the closed-loop meal response. We note that the changes made do not adversely affect the ability of the model to fit hyperglycemic clamp data but are necessary to fit the response to a hypoglycemic clamp. Finally, we note a number of β cell characteristics observed in the perfused pancreas have not been included in the model. We suggest that continuing the effort to understand and incorporate aspects of how the β cell achieves glucose control can provide valuable insights into how improvements in future artificial pancreas algorithms might be achieved.

The future is now: software-guided intensive insulin therapy in the critically ill

Since the development of intensive insulin therapy for the critically ill adult, tight glycemic control (TGC) has become increasingly complicated to apply and achieve. Software-guided (SG) algorithms for insulin dosing represent a new method to achieve euglycemia in critical illness. We provide an overview of the state of SG TGC with an eye to the future. The current milieu is disorganized, with little research that incorporates newer variables of dysglycemia, such as glycemic variability. To develop and implement better algorithms, scientists, programmers, and clinicians need to standardize measurements and variables.

Adaptive blood glucose control for intensive care applications

Control of blood glucose concentration for patients in intensive care units (ICUs) has been demonstrated to be beneficial in reducing mortality and the incidence of serious complications, for both diabetic and non-diabetic patients. However, the high degree of variability and uncertainty characterizing the physiological conditions of critically ill subjects makes automated glucose control quite difficult; consequently, traditional, nurse-implemented protocols are widely employed. These protocols are based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside insulin administration. In this paper, a novel automatic adaptive control strategy based on frequent glucose measurements and a self-tuning control technique is validated based on a simulation study for 200 virtual patients. The adaptive control strategy is shown to be highly effective in controlling blood glucose concentration despite the large degree of variability in the blood glucose response exhibited by the 200 simulated patients.

Design and rationale of safe pediatric euglycemia after cardiac surgery: a randomized controlled trial of tight glycemic control after pediatric cardiac surgery

OBJECTIVES

To describe the design of a clinical trial testing the hypothesis that children randomized to tight glycemic control with intensive insulin therapy after cardiac surgery will have improved clinical outcomes compared to children randomized to conventional blood glucose management.

DESIGN

Two-center, randomized controlled trial.

SETTING

Cardiac ICUs at two large academic pediatric centers.

PATIENTS

Children from birth to those aged 36 months recovering in the cardiac ICU after surgery with cardiopulmonary bypass.

INTERVENTIONS

Subjects in the tight glycemic control (intervention) group receive an intravenous insulin infusion titrated to achieve normoglycemia (target blood glucose range of 80-110 mg/dL; 4.4-6.1 mmol/L). The intervention begins at admission to the cardiac ICU from the operating room and terminates when the patient is ready for discharge from the ICU. Continuous glucose monitoring is performed during insulin infusion to minimize the risks of hypoglycemia. The standard care group has no target blood glucose range.

MEASUREMENTS AND MAIN RESULTS

The primary outcome is the development of any nosocomial infection (bloodstream, urinary tract, and surgical site infection or nosocomial pneumonia). Secondary outcomes include mortality, measures of cardiorespiratory function and recovery, laboratory indices of nutritional balance, immunologic, endocrinologic, and neurologic function, cardiac ICU and hospital length of stay, and neurodevelopmental outcome at 1 and 3 yrs of age. A total of 980 subjects will be enrolled (490 in each treatment arm) for sufficient power to show a 50% reduction in the prevalence of the primary outcome.

CONCLUSIONS

Pediatric cardiac surgery patients may recognize great benefit from tight glycemic control in the postoperative period, particularly with regard to reduction of nosocomial infections. The Safe Pediatric Euglycemia after Cardiac Surgery trial is designed to provide an unbiased answer to the question of whether this therapy is indeed beneficial and to define the associated risks of therapy.

Tight glycemic control versus standard care after pediatric cardiac surgery

BACKGROUND

In some studies, tight glycemic control with insulin improved outcomes in adults undergoing cardiac surgery, but these benefits are unproven in critically ill children at risk for hyperinsulinemic hypoglycemia. We tested the hypothesis that tight glycemic control reduces morbidity after pediatric cardiac surgery.

METHODS

In this two-center, prospective, randomized trial, we enrolled 980 children, 0 to 36 months of age, undergoing surgery with cardiopulmonary bypass. Patients were randomly assigned to either tight glycemic control (with the use of an insulin-dosing algorithm targeting a blood glucose level of 80 to 110 mg per deciliter [4.4 to 6.1 mmol per liter]) or standard care in the cardiac intensive care unit (ICU). Continuous glucose monitoring was used to guide the frequency of blood glucose measurement and to detect impending hypoglycemia. The primary outcome was the rate of health care-associated infections in the cardiac ICU. Secondary outcomes included mortality, length of stay, organ failure, and hypoglycemia.

RESULTS

A total of 444 of the 490 children assigned to tight glycemic control (91%) received insulin versus 9 of 490 children assigned to standard care (2%). Although normoglycemia was achieved earlier with tight glycemic control than with standard care (6 hours vs. 16 hours, P<0.001) and was maintained for a greater proportion of the critical illness period (50% vs. 33%, P<0.001), tight glycemic control was not associated with a significantly decreased rate of health care-associated infections (8.6 vs. 9.9 per 1000 patient-days, P=0.67). Secondary outcomes did not differ significantly between groups, and tight glycemic control did not benefit high-risk subgroups. Only 3% of the patients assigned to tight glycemic control had severe hypoglycemia (blood glucose <40 mg per deciliter [2.2 mmol per liter]).

CONCLUSIONS

Tight glycemic control can be achieved with a low hypoglycemia rate after cardiac surgery in children, but it does not significantly change the infection rate, mortality, length of stay, or measures of organ failure, as compared with standard care. (Funded by the National Heart, Lung, and Blood Institute and others; SPECS ClinicalTrials.gov number, NCT00443599.).

Value of continuous glucose monitoring for minimizing severe hypoglycemia during tight glycemic control

OBJECTIVE

Tight glycemic control can potentially reduce morbidity and mortality in the intensive care unit but increases the risk of hypoglycemia. The most effective means to avoid hypoglycemia is to obtain frequent blood glucose samples, but this increases the burden to nursing staff. The objective of this study was to assess the ability of a real-time continuous glucose monitor to reduce hypoglycemia (blood glucose <60 mg/dL [3.3 mmol/L]) during standard care or tight glycemic control effected with a proportional integral derivative insulin titration algorithm.

DESIGN

Real-time continuous glucose monitor profiles obtained from an ongoing prospective randomized trial of tight glycemic control were retrospectively analyzed to determine whether the continuous glucose measure had prevented instances of hypoglycemia.

SETTING

Cardiac intensive care unit.

PATIENTS

Children 3 yrs of age or younger undergoing cardiac surgery were studied.

INTERVENTIONS

Intravenous insulin infusion and rescue glucose guided by the real-time continuous glucose monitor and the proportional integral derivative algorithm in the tight glycemic control arm (n = 155; target glucose 80-110 mg/dL [4.4-6.1 mmol/L]) and the real-time continuous glucose monitor in the standard care arm (n = 156).

MEASUREMENTS AND MAIN RESULTS

No reduction in hypoglycemia was observed with real-time continuous glucose monitor alarms set at 60 mg/dL (3.3 mmol/L) (zero of 19 occurrences of blood glucose <60 mg/dL [3.3 mmol/L] detected); 18 of 40 subsequent incidences of hypoglycemia were detected after the alarm threshold was increased to 70 mg/dL (3.9 mmol/L). In the tight glycemic control arm, eight incidences were reduced in duration and an additional eight events were prevented with intravenous glucose. In the standard care arm, three of nine occurrences of hypoglycemia were detected with the duration reduced in all cases. On average, one to two false hypoglycemia alarms were observed in each patient.

CONCLUSIONS

The real-time continuous glucose monitor in combination with proportional integral derivative control can reduce hypoglycemia during tight glycemic control. The real-time continuous glucose monitor can also reduce hypoglycemia during standard care. However, false alarms increase the overall nursing workload.

Hospital Strategies for Insulin Therapy with Rapid-Acting Insulin Analogs

This article presents strategies on how to meet the challenges presented by the use of insulin in the hospital setting and describes trends seen in current hospital practice. Insulin provides the greatest flexibility in the hospital setting to achieve optimal blood glycemic control in patients with known type 2 diabetes, thereby reducing complications and death. Important challenges include implementing protocols for use of subcutaneous insulin injection (including optimal use of insulin pens), conversion from continuous subcutaneous insulin infusion or intravenous infusion to subcutaneous administration by multiple injections, and dosing of insulin in patients receiving corticosteroids. One important trend is a move away from the use of sliding-scale insulin to the use of correction-dose insulin as an adjunct to basal/bolus insulin. In this approach, insulin treatment is closely tailored to changing levels of glycemia, and a protocol is put in place for administration of a correction dose of rapid-acting insulin in response to a glycemic excursion. Insulin analogs can more closely mimic physiological insulin profiles than regular insulin, and rapid-acting analogs are invaluable agents as correction insulin administered by pump or in transition to multiple daily injections and as part of basal/bolus therapy. Good glycemic control can improve outcomes of hospital patients in several ways, including facilitating more rapid recovery from infections, shortening intensive care stays, and minimizing costs. Strategies employed to meet the challenges of insulin use in the hospital setting include the increasing use of continuous glucose monitoring systems and the development of insulin dosing algorithms.

In silico evaluation of glucose control protocols for critically ill patients

This letter presents an in silico evaluation method of glucose control protocols for critically ill patients with hyperglycemia. Although various glucose control protocols were introduced and investigated in clinical trials, development and validation of a novel glucose control protocol for critically ill patients require too much time and resources in clinical evaluation. We employed a virtual patient model of the critically ill patient with hyperglycemia and evaluated the clinically investigated glucose control protocols in a computational environment. The three-day simulation results presented the time profiles of glucose and insulin concentrations, the amount of enteral feed and intravenous bolus of glucose, and the intravenous insulin infusion rate. The hyperglycemia and hypoglycemia index, blood glucose concentrations, insulin doses, intravenous glucose infusion rates, and glucose feed rates were compared between different protocols. It is shown that a similar hypoglycemia incidence exists in simulation and clinical results. We concluded that this in silico simulation method using a virtual patient model could be useful for predicting hypoglycemic incidence of novel glucose control protocols for critically ill patients, prior to clinical trials.

Evaluating glycemic control algorithms by computer simulations

BACKGROUND

Numerous guidelines and algorithms exist to achieve glycemic control. Their strengths and weaknesses are difficult to assess without head-to-head comparison in time-consuming clinical trials. We hypothesized that computer simulations may be useful.

METHODS

Two open-label randomized clinical trials were replicated using computer simulations. One study compared performance of the enhanced model predictive control (eMPC) algorithm at two intensive care units in the United Kingdom and Belgium. The other study compared three glucose control algorithms-eMPC, Matias (the absolute glucose protocol), and Bath (the relative glucose change protocol)-in a single intensive care unit. Computer simulations utilized a virtual population of 56 critically ill subjects derived from routine data collected at four European surgical and medical intensive care units.

RESULTS

In agreement with the first clinical study, computer simulations reproduced the main finding and discriminated between the two intensive care units in terms of the sampling interval (1.3 h vs. 1.8 h, United Kingdom vs. Belgium; P < 0.01). Other glucose control metrics were comparable between simulations and clinical results. The principal outcome of the second study was also reproduced. The eMPC demonstrated better performance compared with the Matias and Bath algorithms as assessed by the time when plasma glucose was in the target range between 4.4 and 6.1 mmol/L (65% vs. 43% vs. 42% [P < 0.001], eMPC vs. Matias vs. Bath) without increasing the risk of severe hypoglycemia.

CONCLUSIONS

Computer simulations may provide resource-efficient means for preclinical evaluation of algorithms for glycemic control in the critically ill.

A new general glucose homeostatic model using a proportional-integral-derivative controller

The glucose-insulin system is a challenging process to model due to the feedback mechanisms present, hence the implementation of a model-based approach to the system is an on-going and challenging research area. A new approach is proposed here which provides an effective way of characterising glycaemic regulation. The resulting model is built on the premise that there are three phases of insulin secretion, similar to those seen in a proportional-integral-derivative (PID) type controller used in engineering control problems. The model relates these three phases to a biological understanding of the system, as well as the logical premise that the homeostatic mechanisms will maintain very tight control of the system. It includes states for insulin, glucose, insulin action and a state to simulate an integral function of glucose. Structural identifiability analysis was performed on the model to determine whether a unique set of parameter values could be identified from the available observations, which should permit meaningful conclusions to be drawn from parameter estimation. Although two parameters--glucose production rate and the proportional control coefficient--were found to be unidentifiable, the former is not a concern as this is known to be impossible to measure without a tracer experiment, and the latter can be easily estimated from other means. Subsequent parameter estimation using Intravenous Glucose Tolerance Test (IVGTT) and hyperglycaemic clamp data was performed and subsequent model simulations have shown good agreement with respect to these real data.

Comparison of the effectiveness and safety of two insulin infusion protocols in the management of hyperglycemia in critically ill children

OBJECTIVES

To compare the effectiveness and safety of a paper-based and a computerized algorithm used for tight glycemic control.

SETTING

Academic pediatric intensive care unit.

DESIGN

Retrospective cohort study.

PATIENTS

Two groups of nondiabetic critically ill children with persistent hyperglycemia (blood glucose ≥140 mg/dL [ ≥7.8 mmol/L] for at least 2 hrs) were included.

INTERVENTION

One group of patients' blood glucose was controlled at 90-119 mg/dL (5.0-6.6 mmol/L) using the Yale Insulin Infusion Protocol (YIIP), a paper-based protocol. Another group of patients' blood glucose was controlled at 80-110 mg/dL (4.4-6.1 mmol/L) with eProtocol insulin (ePi), a computerized decision support tool.

MEASUREMENTS AND MAIN RESULTS

The effectiveness of the protocols was compared using percentages of blood glucose values within target range and glucose variability index. A safety comparison was made using hypoglycemia rates at ≤40 mg/dL (≤2.2 mmol/L), ≤50 mg/dL (≤2.8 mmol/L), and ≤60 mg/dL (≤3.3 mmol/L). Forty-two patients and 12 patients were included in the YIIP and ePi groups, respectively. The percent of values in range was lower in the YIIP group (33%) compared with the ePi group (41%) (p < .001). Mean glucose variability index was comparable in the two groups (18.7 ± 8.9 mg/dL/hr [1.0 ± 0.5 mmol/L/hr] for the YIIP group and 14.4 ± 7.6 mg/dL/hr [0.8 ± 0.4 mmol/L/hr] for the ePi group; p = .111). Hypoglycemia rates were statistically similar in both groups. In the YIIP group, 10% of patients and in the ePi group, 25% of patients had blood glucose ≤40 mg/dL (≤2.2 mmol/L) (p = .168).

CONCLUSION

YIIP is less effective but is as safe as ePi in achieving tight glycemic control. We are awaiting the results of two multicenter trials designed to determine the survival benefit of tight glycemic control in children. Further studies are needed to determine the clinical significance of the different glucose metrics in critically ill patients.

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

INTRODUCTION

The purpose of this research is to provide recommendations for the management of glycemic control in critically ill patients.

METHODS

Twenty-one experts issued recommendations related to one of the five pre-defined categories (glucose target, hypoglycemia, carbohydrate intake, monitoring of glycemia, algorithms and protocols), that were scored on a scale to obtain a strong or weak agreement. The GRADE (Grade of Recommendation, Assessment, Development and Evaluation) system was used, with a strong recommendation indicating a clear advantage for an intervention and a weak recommendation indicating that the balance between desirable and undesirable effects of an intervention is not clearly defined.

RESULTS

A glucose target of less than 10 mmol/L is strongly suggested, using intravenous insulin following a standard protocol, when spontaneous food intake is not possible. Definition of the severe hypoglycemia threshold of 2.2 mmol/L is recommended, regardless of the clinical signs. A general, unique amount of glucose (enteral/parenteral) to administer for any patient cannot be suggested. Glucose measurements should be performed on arterial rather than venous or capillary samples, using central lab or blood gas analysers rather than point-of-care glucose readers.

CONCLUSIONS

Thirty recommendations were obtained with a strong (21) and a weak (9) agreement. Among them, only 15 were graded with a high level of quality of evidence, underlying the necessity to continue clinical studies in order to improve the risk-to-benefit ratio of glucose control.

The use of insulin pumps in youth with type 1 diabetes

The use of insulin pump therapy (continuous subcutaneous insulin infusion) has increased dramatically in youth with type 1 diabetes (T1D) in the past decade. In this review we provide background and practical clinical advice on insulin basal rates and bolus doses and on the advantages of pump therapy with exercise. Acute complications of T1D (hypoglycemia and diabetic ketoacidosis) in the context of pump therapy are reviewed. The advantages of pump therapy in the school setting and in hospitalized patients are discussed. Finally, diabetes management in the 21st century, in which pump therapy is combined with continuous glucose monitoring, and its potential for a closed-loop pancreas are presented.

Clinical benefits of tight glycaemic control: focus on the paediatric patient

Hyperglycaemia and glucose variability occur frequently during critical illness or after major surgery in children and are associated with worse outcome. Association does not necessarily imply causality however, and the question whether tight glycaemic control (TGC) with insulin infusion improves morbidity and mortality can only be answered by randomised controlled trials (RCTs). Currently, only one single-centre RCT exists, proving the concept of TGC in critically ill children. Attenuation of inflammation and reduction of secondary infections, decreased prolonged stay in intensive care and reduced dependency on haemodynamic support were accomplished, despite a substantial increased incidence of biochemical hypoglycaemia. Before universal implementation in paediatric intensive care both long-term effects on outcome and development and issues regarding optimal levels of blood glucose control need to be cleared in multicentre prospective RCTs. Technological improvement might be helpful in optimising blood glucose control.

Essential elements of the native glucoregulatory system, which, if appreciated, may help improve the function of glucose controllers in the intensive care unit setting

In 2001, Van den Berghe and colleagues were able to show that tight glucose control decreases morbidity and mortality rates in the intensive care unit (ICU) setting. Several large, prospective, randomized controlled trials have failed to confirm these results. All of these studies attempted tight glucose control using expert-designed algorithms to adjust the rate of intravenous insulin. Unfortunately, these studies each had high rates of hypoglycemia, a high percentage of glucose values outside of the target range, and increased glucose variability. These three measurements have been shown to increase mortality rates in ICU patients. In order to achieve a high rate of success with regards to tight glucose control, a closed-loop system will need to be created. The two main elements of such a system are a continuous glucose sensor and a recursive glucose control algorithm. This review highlights the important elements of the native glucoregulatory system, which, if utilized, may help create a successful glucose control algorithm for a closed-loop system.

Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control

Current care guidelines recommend glucose control (GC) in critically ill patients. To achieve GC, many ICUs have implemented a (nurse-based) protocol on paper. However, such protocols are often complex, time-consuming, and can cause iatrogenic hypoglycemia. Computerized glucose regulation protocols may improve patient safety, efficiency, and nurse compliance. Such computerized clinical decision support systems (Cuss) use more complex logic to provide an insulin infusion rate based on previous blood glucose levels and other parameters. A computerized CDSS for glucose control has the potential to reduce overall workload, reduce the chance of human cognitive failure, and improve glucose control. Several computer-assisted glucose regulation programs have been published recently. In order of increasing complexity, the three main types of algorithms used are computerized flowcharts, Proportional-Integral-Derivative (PID), and Model Predictive Control (MPC). PID is essentially a closed-loop feedback system, whereas MPC models the behavior of glucose and insulin in ICU patients. Although the best approach has not yet been determined, it should be noted that PID controllers are generally thought to be more robust than MPC systems. The computerized Cuss that are most likely to emerge are those that are fully a part of the routine workflow, use patient-specific characteristics and apply variable sampling intervals.

An evaluation of "I, Pancreas" algorithm performance in silico

BACKGROUND

The objective of this study was to investigate the performance of a newly proposed insulin titrating algorithm to achieve tight glycemic control in the critically ill.

METHODS

A simulation environment with 10 critically ill virtual subjects was employed to evaluate the "I, Pancreas" algorithm proposed by Braithwaite et al. and described in an article in this issue of Journal of Diabetes Science and Technology. The algorithm was coded in MATLAB and was "plugged in" to a simulation environment to provide glucose control in a 48-hour-long simulated study.

RESULTS

Mean blood glucose was 6.5 +/- 0.4 mmol/liter (118 +/- 7.8 mg/dl), percentage of time spent in the target glucose range was 38% (32-44%), and the hyperglycemic index was 0.6 (0.4 -1.0) mmol/liter [11.1 (7.7-18.1) mg/dl]. A single episode of mild hypoglycemia at 3.8 mmol/liter (69 mg/dl) was observed during 480 hours of glucose control.

CONCLUSION

In this initial in silico evaluation, the "I, Pancreas" algorithm provided a safe control of glucose in the simulated study and achieved tight glycemic control 38% of the time.

Intensive Care Unit Insulin Delivery Algorithms: Why So Many? How to Choose?

OBJECTIVE: Studies showing improved outcomes with tight glycemic control in the intensive care unit (ICU) have resulted in a substantial number of new insulin delivery algorithms being proposed. The present study highlights mechanisms used in the better-known approaches, examines what might be critical differences among them, and uses systems theory to characterize the conditions under which each can be expected to perform best. METHODS: Algorithm dose (DeltaI/DeltaG) and step (response to a persistent elevation in glucose) response curves were calculated for written instruction algorithms, developed at the Providence Heart and Vascular Institute (Portland [P] protocol), the University of Washington (UW), and Yale University (Y), together with similar curves for the Glucommander (GM) and proportional integral derivative (PID) computer algorithms. From the simulated curves, different mechanisms used to adjust insulin delivery were identified. RESULTS: All algorithms increased insulin delivery in response to persistent hyperglycemia, but the mechanism used altered the algorithm's sensitivity to glucose, or gain, in the GM, UW, and Y protocols, while leaving it unchanged for the P protocol and PID algorithm. CONCLUSIONS: The increase in insulin delivery in response to persistent hyperglycemia observed with all the algorithms can be expected to bring subjects who respond to insulin to targeted glucose ranges. However, because the PID and P protocols did not alter the insulin delivery response curves, these algorithms can be expected to take longer to achieve target glucose levels in individuals who are insulin resistant and/or are exposed to increased carbohydrate loads (e.g., glucose infusions). By contrast, the GM, UW, and Y algorithms can be expected to adapt to the insulin resistance such that the time to achieve target levels is unchanged if the time for insulin to act does not change. If the insulin resistance is accompanied by a longer time for insulin to act, the UW, Y, and GM algorithms may increase the risk of hypoglycemia. Under these conditions, the longer time required for the PID and P protocols to achieve a target glucose level may be a reasonable trade-off for no increase in the risk of hypoglycemia.

Inpatient insulin therapy

PURPOSE OF REVIEW

In a 2001 report from a surgical intensive care unit in Leuven, Belgium, intravenous insulin infusion targeting blood glucose 80-110 mg/dl reduced patient mortality and morbidities. Subsequent research has failed to define glycemic targets necessary or sufficient for attainment of desired health outcomes in other inpatient settings, but a large body of evidence suggests hospital outcomes are related to hyperglycemia.

RECENT FINDINGS

Recent literature describes observational evidence for hypoglycemia as an independent predictor of mortality in a general medical intensive care unit; superiority of performance of computerized intravenous insulin algorithms in comparison to earlier manual algorithms; acceptability of early transition to scheduled basal prandial correction subcutaneous insulin analog therapy for maintenance of glycemic targets after induction of euglycemia by intravenous insulin infusion, among cardiothoracic surgery patients; inferiority of sliding scale insulin compared to basal prandial correction therapy; and feasibility of diabetes patient self-management in the hospital setting.

SUMMARY

With development of improved insulin administration strategies problems of hypoglycemia and variability of glycemic control are reduced. Investigators and care providers need to achieve glycemic targets to optimize patient outcomes.

Glucose monitoring in acute care: technologies on the horizon

Current glucose monitoring technology appears inadequate for the management of diabetic surgical and in critically ill patients requiring intensive insulin therapy. Subcutaneous sensors measure interstitial fluid glucose, and this technology has not yet been shown to provide the timely and accurate measurements necessary for intravenous insulin administration in surgical and critical care patients on intensive insulin therapy. Technologies under development that may be more suitable for surgical and intensive care unit patients are the automated intermittent type glucose monitors and central catheter glucose monitors. Improved accuracy, patient safety, incorporation of control algorithms, and alleviation of added nursing labor are important factors for consideration with future acute care glucose monitors. Hospital costs for these monitors are difficult to estimate but may be relatively low if their use can be related to better patient outcome, reduced labor costs, and increased job satisfaction for the nursing staff.

A Feedforward-Feedback Glucose Control Strategy for Type 1 Diabetes Mellitus

As the "artificial pancreas" becomes closer to reality, automated insulin delivery based on real-time glucose measurements becomes feasible for people with diabetes. This paper is concerned with the development of novel feedforward-feedback control strategies for real-time glucose control and type 1 diabetes. Improved post-meal responses can be achieved by a pre-prandial snack or bolus, or by reducing the glucose setpoint prior to the meal. Several feedforward-feedback control strategies provide attractive alternatives to the standard meal insulin bolus and are evaluated in simulations using a physiological model.

Nurse-led implementation of an insulin-infusion protocol in a general intensive care unit: improved glycaemic control with increased costs and risk of hypoglycaemia signals need for algorithm revision

Background

Strict glycaemic control (SGC) has become a contentious issue in modern intensive care. Physicians and nurses are concerned about the increased workload due to SGC as well as causing harm through hypoglycaemia. The objective of our study was to evaluate our existing degree of glycaemic control, and to implement SGC safely in our ICU through a nurse-led implementation of an algorithm for intensive insulin-therapy.

Methods

The study took place in the adult general intensive care unit (11 beds) of a 44-bed department of intensive care at a tertiary care university hospital. All patients admitted during the 32 months of the study were enrolled. We retrospectively analysed all arterial blood glucose (BG) results from samples that were obtained over a period of 20 months prior to the implementation of SGC. We then introduced an algorithm for intensive insulin therapy; aiming for arterial blood-glucose at 4.4 – 6.1 mmol/L. Doctors and nurses were trained in the principles and potential benefits and risks of SGC. Consecutive statistical analyses of blood samples over a period of 12 months were used to assess performance, provide feedback and uncover incidences of hypoglycaemia.

Results

Median BG level was 6.6 mmol/L (interquartile range 5.6 to 7.7 mmol/L) during the period prior to implementation of SGC (494 patients), and fell to 5.9 (IQR 5.1 to 7.0) mmol/L following introduction of the new algorithm (448 patients). The percentage of BG samples > 8 mmol/L was reduced from 19.2 % to 13.1 %. Before implementation of SGC, 33 % of samples were between 4.4 to 6.1 mmol/L and 12 patients (2.4 %) had one or more episodes of severe hypoglycaemia (< 2.2 mmol/L). Following implementation of SGC, 45.8 % of samples were between 4.4 to 6.1 mmol/L and 40 patients (8.9 %) had one or more episodes of severe hypoglycaemia. Of theses, ten patients died while still hospitalised (all causes).

Conclusion

The retrospective part of the study indicated ample room for improvement. Through the implementation of SGC the fraction of samples within the new target range increased from 33% to 45.8%. There was also a significant increase in severe hypoglycaemic episodes. There continues to be potential for improved glycaemic control within our ICU. This might be achieved through an improved algorithm and continued efforts to increase nurses' confidence and skills in achieving SGC.

Analysis of algorithms for intensive care unit blood glucose control

Intensive care unit (ICU) blood glucose control algorithms were reviewed and analyzed in the context of linear systems theory and classical feedback control algorithms. Closed-loop performance was illustrated by applying the algorithms in simulation studies using an in silico model of an ICU patient. Steady-state and dynamic input-output analysis was used to provide insight about controller design and potential closed-loop performance. The proportional-integral-derivative, columnar insulin dosing (CID, Glucommander-like), and glucose regulation for intensive care patients (GRIP) algorithms were shown to have similar features and performance. The CID strategy is a time-varying proportional-only controller (no integral action), whereas the GRIP algorithm is a nonlinear controller with integral action. A minor modification to the GRIP algorithm was suggested to improve the closed-loop performance. Recommendations were made to guide control theorists on important ICU control topics worthy of further study.