Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

BACKGROUND

Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS).

OBJECTIVE

The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care.

METHODS

This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established.

RESULTS

Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term "medication" there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs).

CONCLUSION

The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user-device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

Comment on Lionte et al. Association of Multiple Glycemic Parameters at Hospital Admission with Mortality and Short-Term Outcomes in Acutely Poisoned Patients. Diagnostics 2021, 11, 361

We have read with great interest the article by Lionte et al., "Association of multiple glycemic parameters at hospital admission with mortality and short-term outcomes in acutely poisoned patients", recently published in your journal [...].

Effectiveness and safety of the Space GlucoseControl system for glycaemia control in caring for postoperative cardiac surgical patients

BACKGROUND

Hyperglycaemia is a very common complication in post-cardiac surgical patients, and as such, it must be properly managed. For this purpose, the enhanced Model Predictive Control algorithm for glycaemia control has been implemented into a nurse-led device called Space GlucoseControl (SGC) that aims to achieve a safe and effective blood glucose control in a better way than the traditional "paper-based" protocols.

PURPOSE

The aim of the study was to know the effectiveness and safety of the SGC in glycaemia control in cardiosurgical adult patients in the immediate postoperative period in the intensive care unit.

METHODS

A prospective before-and-after intervention study was conducted. One hundred sixty cardiosurgical adult patients with hyperglycaemia were selected: 80 in the control group from May to November 2018 and 80 in the intervention group (use of the SGC device) from January to December 2019. The primary outcome was the percentage of time within the target range (140-180 mg/dL in the control group and 100-160 mg/dL in the intervention group).

RESULTS

The percentage of time within the target range was significantly higher in the SGC group than in the control group (70.5% [58.25-80] vs 54.83% [36.09-75], p < 0.001). The range was also achieved earlier with the SGC (5 [3-6.875] hours vs 7 [4-11] hours; p < 0.05). The first blood glucose value after reaching the target range was higher in the control group, with statistical significance (p < 0.05). There were no hypoglycaemia episodes in the control group. However, during SGC treatment, six episodes of hypoglycaemia occurred, and all of them were nonsevere (mean value = 61 mg/dL).

CONCLUSION

The SGC is useful to achieve a faster tight glycaemic control, with a higher percentage of time within the target range, although episodes of nonsevere hypoglycaemia could be observed.

Feasibility of Blood Glucose Management Using Intra-Arterial Glucose Monitoring in Combination with an Automated Insulin Titration Algorithm in Critically Ill Patients

Background: This two-center pilot study combined for the first time an intra-arterial glucose sensor with a decision support system for insulin dosing (SGCplus system) in critically ill patients with hyperglycemia. Methods: Twenty-two patients who were equipped with an arterial line and required iv insulin therapy were managed by the SGCplus system during their medical treatment at the intensive care unit. Results: Time to target was 111 ± 195 min (80-150 mg/dL) and 135 ± 267 min (100-160 mg/dL) in the lower and higher glucose target group. Mean blood glucose (BG) was 142 ± 32 mg/dL with seven BG values <70 mg/dL. Mean daily insulin dose was 62 ± 38 U and mean daily carbohydrate intake 148 ± 50 g/day (enteral nutrition) and 102 ± 58 g/day (parenteral nutrition). Acceptance of SGCplus suggestions was high (93%). Conclusions: The SGCplus system can be safely applied in critically ill patients with hyperglycemia and enables good glycemic control.

Blood glucose control in the intensive care unit: Where is the data?

Blood glucose control, including hyperglycemia correction, maintaining glucose at optimal level and avoiding hypoglycemia, is a challenge clinicians face every day in intensive care units (ICUs). If managed inadequately, its related mortality can increase. Prior to 2001, no relevant data from randomized, controlled studies assessing glucose control in the ICU were available. In the past 18 years, however, many clinical trials have defined criteria for managing abnormal blood glucose levels, as well as provided suggestions for glycemic monitoring. Point-of-care blood glucose monitors have become the preferred bedside technology to aid in glycemic management. In addition, in some institutions, continuous glucose monitoring is now available. Cost-effectiveness of adequate glycemic control in the ICU must be taken into consideration when addressing this complex issue. Newer types of glycemic monitoring may reduce nursing staff fatigue and shorten times for the treatment of hyperglycemia or hypoglycemia. There are a variety of glycemic care protocols available. However, not all ICU clinicians are aware of them. The following minireview describes some of these concepts.

Comparison of Space Glucose Control and Routine Glucose Management Protocol for Glycemic Control in Critically Ill Patients: A Prospective, Randomized Clinical Study

Background:

The Space Glucose Control (SGC) system is a computer-assisted device combining infusion pumps with the enhanced Model Predictive Control algorithm to achieve the target blood glucose (BG) level safely. The objective of this study was to evaluate the efficacy and safety of glycemic control by SGC with customized BG target range of 5.8–8.9 mmol/L in the critically ill patients.

Methods:

It is a randomized controlled trial of seventy critically ill patients with mechanical ventilation and hyperglycemia (BG ≥ 9.0 mmol/L). Thirty-six patients in the SGC group and 34 in the routine glucose management group were observed for three consecutive days. Target BG for both groups was 5.8–8.9 mmol/L. The primary outcome was the percentage time in the target range.

Results:

The percentage time within BG target range in the SGC group (69 ± 15%) was significantly higher than in the routine management group (52 ± 24%; P < 0.01). No measurement was ≤2.2 mmol/L, and there was only one episode of hypoglycemia (2.3–3.3 mmol/L) in each group. The average BG was significantly lower in the SGC group (7.8 ± 0.7 mmol/L) than in the routine management group (9.1 ± 1.6 mmol/L, P < 0.001). Target BG level was reached earlier in the SGC group than routine management group (2.5 ± 2.9 vs. 12.1 ± 15.3 h, P = 0.001). However, the SGC group performed worse for daily insulin requirement (59.8 ± 39.3 vs. 28.4 ± 36.7 U, P = 0.001) and sampling interval (2.0 ± 0.5 vs. 3.7 ± 0.5 h, P < 0.001) than the routine management group did. Multiple linear regression showed that the intervention group remained a significant individual predictor (P < 0.001) of the percentage time in target range.

Conclusions:

The SGC system, with a BG target of 5.8–8.9 mmol/L, resulted in effective and reliable glycemic control with few hypoglycemic episodes in critically ill patients with mechanical ventilation and hyperglycemia. However, the workload was increased.

Trial Registration:

http://www.clinicaltrials.gov, NCT 02491346; https://www.clinicaltrials.gov/ct2/show/NCT02491346?term=NCT02491346&cond=Hyperglycemia&cntry1=ES%3ACN&rank=1.

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.

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

Background

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

Methods

We conducted an international, multicenter, randomized controlled trial involving 1550 adult, medical and surgical critically ill patients, requiring blood glucose control. Patients were randomly assigned to algorithm-guided blood glucose control (LOGIC-C, n = 777) or blood glucose control by trained nurses (Nurse-C, n = 773) during ICU stay, according to the local target range (80–110 mg/dL or 90–145 mg/dL). The primary outcome measure was the quality of blood glucose control, assessed by the glycemic penalty index (GPI), a measure that penalizes hypoglycemic and hyperglycemic deviations from the chosen target range. Incidence of severe hypoglycemia (<40 mg/dL) was the main safety outcome measure. New infections in ICU, duration of hospital stay, landmark 90-day mortality and quality of life were clinical safety outcome measures.

Results

The median GPI was lower in the LOGIC-C (10.8 IQR 6.2–16.1) than in the Nurse-C group (17.1 IQR 10.6–26.2) (P < 0.001). Mean blood glucose was 111 mg/dL (SD 15) in LOCIC-C versus 119 mg/dL (SD 21) in Nurse-C, whereas the median time-in-target range was 67.0% (IQR 52.1–80.1) in LOGIC-C versus 47.1% (IQR 28.1–65.0) in the Nurse-C group (both P < 0.001). The fraction of patients with severe hypoglycemia did not differ between LOGIC-C (0.9%) and Nurse-C (1.2%) (P = 0.6). The clinical safety outcomes did not differ between groups. The sampling interval was 2.3 h (SD 0.5) in the LOGIC-C group versus 3.0 h (SD 0.8) in the Nurse-C group (P < 0.001).

Conclusions

In a randomized controlled trial of a mixed critically ill patient population, the use of the LOGIC-Insulin blood glucose control algorithm, compared with blood glucose control by expert nurses, improved the quality of blood glucose control without increasing hypoglycemia.

Trial registration

ClinicalTrials.gov, NCT02056353. Registered on 4 February 2014.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1799-6) contains supplementary material, which is available to authorized users.

Energy, Protein, Carbohydrate, and Lipid Intakes and Their Effects on Morbidity and Mortality in Critically Ill Adult Patients: A Systematic Review

The guidelines for nutritional support in critically ill adult patients differ in various aspects. The optimal amount of energy and nutritional substrates supplied is important for reducing morbidity and mortality, but unfortunately this is not well known, because the topic is complex and every patient is individual. The aim of this review was to gather recent pertinent information concerning the nutritional support of critically ill patients in the intensive care unit (ICU) with respect to the energy, protein, carbohydrate, and lipid intakes and the effect of their specific utilization on morbidity and mortality. Enteral nutrition (EN) is generally recommended over parenteral nutrition (PN) and is beneficial when administered within 24-48 h after ICU admission. In contrast, early PN does not provide substantial advantages in terms of morbidity and mortality, and the time when it is safe and beneficial remains unclear. The most advantageous recommendation seems to be administration of a hypocaloric (<20 kcal · kg^-1 · d^-1), high-protein diet (amino acids at doses of ≥2 g · kg^-1 · d^-1), at least during the first week of critical illness. Another important factor for reducing morbidity is the maintenance of blood glucose concentrations at 120-150 mg/dL, which is accomplished with the use of insulin and lower doses of glucose of 1-2 g · kg^-1 · d^-1, because this prevents the risk of hypoglycemia and is associated with a better prognosis according to recent studies. A fat emulsion is used as a source of required calories because of insulin resistance in the majority of patients. In addition, lipid oxidation in these patients is ∼25% higher than in healthy subjects.

Towards Usable E-Health. A Systematic Review of Usability Questionnaires

BACKGROUND

The use of e-health can lead to several positive outcomes. However, the potential for e-health to improve healthcare is partially dependent on its ease of use. In order to determine the usability for any technology, rigorously developed and appropriate measures must be chosen.

OBJECTIVES

To identify psychometrically tested questionnaires that measure usability of e-health tools, and to appraise their generalizability, attributes coverage, and quality.

METHODS

We conducted a systematic review of studies that measured usability of e-health tools using four databases (Scopus, PubMed, CINAHL, and HAPI). Non-primary research, studies that did not report measures, studies with children or people with cognitive limitations, and studies about assistive devices or medical equipment were systematically excluded. Two authors independently extracted information including: questionnaire name, number of questions, scoring method, item generation, and psychometrics using a data extraction tool with pre-established categories and a quality appraisal scoring table.

RESULTS

Using a broad search strategy, 5,558 potentially relevant papers were identified. After removing duplicates and applying exclusion criteria, 35 articles remained that used 15 unique questionnaires. From the 15 questionnaires, only 5 were general enough to be used across studies. Usability attributes covered by the questionnaires were: learnability (15), efficiency (12), and satisfaction (11). Memorability (1) was the least covered attribute. Quality appraisal showed that face/content (14) and construct (7) validity were the most frequent types of validity assessed. All questionnaires reported reliability measurement. Some questionnaires scored low in the quality appraisal for the following reasons: limited validity testing (7), small sample size (3), no reporting of user centeredness (9) or feasibility estimates of time, effort, and expense (7).

CONCLUSIONS

Existing questionnaires provide a foundation for research on e-health usability. However, future research is needed to broaden the coverage of the usability attributes and psychometric properties of the available questionnaires.

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.

Evaluation of a Nurse-Managed Insulin Infusion Protocol

BACKGROUND

The purpose of this study was to evaluate the performance of an insulin infusion protocol targeting a blood glucose (BG) level of 140-180 mg/dL and to characterize protocol adherence.

MATERIALS AND METHODS

This was a retrospective observational cohort study including patients for whom the protocol was ordered from January 2012 to May 2013. Performance metrics were assessed in all patients and in patients with an initial BG level of ≥200 mg/dL. Protocol adherence was assessed in a random subset of 50 patients without hypoglycemia and in all hypoglycemic patients.

RESULTS

In patients with an initial BG level of ≥200 mg/dL, the mean time to goal was 7.1 h. The rate of decline of BG level in the first 6 h was 16.4 mg/dL/h. Mean BG level was 167 mg/dL, with 43.9% of BG values within goal and 80.3% between 80 and 199 mg/dL. The rate of hypoglycemic events was 0.14 per 100 h. The mean protocol violation rate was higher in patients with hypoglycemia compared with those without (39.8 vs. 23.5 per 100 h, P = 0.002), and 60.7% of hypoglycemic events were attributable to protocol violations. The protocol violation rate (42.8 vs. 17.6 per 100 h; P < 0.001) and the odds of hypoglycemia (odds ratio = 5.2; 95% confidence interval, 1.6, 16.5) were higher in the cardiac surgery patients compared with other patients.

CONCLUSIONS

This protocol provides adequate BG control within the clinically acceptable range of 80-199 mg/dL but not within the narrower range of 140-180 mg/dL, with a low incidence of hypoglycemia. Risk factors for hypoglycemia and barriers to protocol adherence in the cardiac surgery population should be elucidated.

Space GlucoseControl system for blood glucose control in intensive care patients--a European multicentre observational study

BACKGROUND

Glycaemia control (GC) remains an important therapeutic goal in critically ill patients. The enhanced Model Predictive Control (eMPC) algorithm, which models the behaviour of blood glucose (BG) and insulin sensitivity in individual ICU patients with variable blood samples, is an effective, clinically proven computer based protocol successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols. eMPC has been integrated into the B.Braun Space GlucoseControl system (SGC), which allows direct data communication between pumps and microprocessor. The present study was undertaken to assess the clinical performance and safety of the SGC for glycaemia control in critically ill patients under routine conditions in different ICU settings and with various nutritional protocols.

METHODS

The study endpoints were the percentage of time the BG was within the target range 4.4 - 8.3 mmol.l(-1), the frequency of hypoglycaemic episodes, adherence to the advice of the SGC and BG measurement intervals. BG was monitored, and insulin was given as a continuous infusion according to the advice of the SGC. Nutritional management (enteral, parenteral or both) was carried out at the discretion of each centre.

RESULTS

17 centres from 9 European countries included a total of 508 patients, the median study time was 2.9 (1.9-6.1) days. The median (IQR) time-in-target was 83.0 (68.7-93.1) % of time with the mean proposed measurement interval 2.0 ± 0.5 hours. 99.6% of the SGC advices on insulin infusion rate were accepted by the user. Only 4 episodes (0.01% of all BG measurements) of severe hypoglycaemia <2.2 mmol.l(-1) in 4 patients occurred (0.8%; 95% CI 0.02-1.6%).

CONCLUSION

Under routine conditions and under different nutritional protocols the Space GlucoseControl system with integrated eMPC algorithm has exhibited its suitability for glycaemia control in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01523665.