Is there more to glycaemic control than meets the eye?

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

Implementing glucose control in intensive care: a multicenter trial using statistical process control

Background

Glucose control (GC) with insulin decreases morbidity and mortality of critically ill patients. In this study we investigated GC performance over time during implementation of GC strategies within three intensive care units (ICUs) and in routine clinical practice.

Methods

All adult critically ill patients who stayed for >24 h between 1999 and 2007 were included. Effects of implementing local GC guidelines and guideline revisions on effectiveness/efficiency-related indicators, safety-related indicators, and protocol-related indicators were measured.

Results

Data of 17,111 patient admissions were evaluated, with 714,141 available blood glucose levels (BGL) measurements. Mean BGL, time to reach target, hyperglycemia index, sampling frequency, percentage of hyperglycemia events, and in-range measurements statistically changed after introducing GC in all ICUs. The introduction of simple rules on GC had the largest effect. Subsequent changes in the protocol had a smaller effect than the introduction of the protocol itself. As soon as the protocol was introduced, in all ICUs the percentage of hypoglycemia events increased. Various revisions were implemented to reduce hypoglycemia events, but levels never returned to those from pre-implementation. More intensive implementation strategies including the use of a decision support system resulted in better control of the process.

Conclusion

There are various strategies to achieve GC in routine clinical practice but with variable success. All of them were associated with an increase in hypoglycemia events, but GC was never stopped. Instead, these events have been accepted and managed. Statistical process control is a useful tool for monitoring phenomena over time and captures within-institution changes.

Electronic supplementary material

The online version of this article (doi:10.1007/s00134-010-1924-3) contains supplementary material, which is available to authorized users.

Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study

INTRODUCTION

Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation.

RESULTS

This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error.

DISCUSSION

Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements.

CONCLUSION

This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT.

Data mining technologies for blood glucose and diabetes management

Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management context is particularly well suited to a data mining approach. The availability of electronic health records and monitoring facilities, including telemedicine programs, is leading to accumulating huge data sets that are accessible to physicians, practitioners, and health care decision makers. Moreover, because diabetes is a lifelong disease, even data available for an individual patient may be massive and difficult to interpret. Finally, the capability of interpreting blood glucose readings is important not only in diabetes monitoring but also when monitoring patients in intensive care units. This article describes and illustrates work that has been carried out in our institutions in two areas in which data mining has a significant potential utility to researchers and clinical practitioners: analysis of (i) blood glucose home monitoring data of diabetes mellitus patients and (ii) blood glucose monitoring data from hospitalized intensive care unit patients.

Intensive versus conventional glucose control in critically ill patients

BACKGROUND

The optimal target range for blood glucose in critically ill patients remains unclear.

METHODS

Within 24 hours after admission to an intensive care unit (ICU), adults who were expected to require treatment in the ICU on 3 or more consecutive days were randomly assigned to undergo either intensive glucose control, with a target blood glucose range of 81 to 108 mg per deciliter (4.5 to 6.0 mmol per liter), or conventional glucose control, with a target of 180 mg or less per deciliter (10.0 mmol or less per liter). We defined the primary end point as death from any cause within 90 days after randomization.

RESULTS

Of the 6104 patients who underwent randomization, 3054 were assigned to undergo intensive control and 3050 to undergo conventional control; data with regard to the primary outcome at day 90 were available for 3010 and 3012 patients, respectively. The two groups had similar characteristics at baseline. A total of 829 patients (27.5%) in the intensive-control group and 751 (24.9%) in the conventional-control group died (odds ratio for intensive control, 1.14; 95% confidence interval, 1.02 to 1.28; P=0.02). The treatment effect did not differ significantly between operative (surgical) patients and nonoperative (medical) patients (odds ratio for death in the intensive-control group, 1.31 and 1.07, respectively; P=0.10). Severe hypoglycemia (blood glucose level, < or = 40 mg per deciliter [2.2 mmol per liter]) was reported in 206 of 3016 patients (6.8%) in the intensive-control group and 15 of 3014 (0.5%) in the conventional-control group (P<0.001). There was no significant difference between the two treatment groups in the median number of days in the ICU (P=0.84) or hospital (P=0.86) or the median number of days of mechanical ventilation (P=0.56) or renal-replacement therapy (P=0.39).

CONCLUSIONS

In this large, international, randomized trial, we found that intensive glucose control increased mortality among adults in the ICU: a blood glucose target of 180 mg or less per deciliter resulted in lower mortality than did a target of 81 to 108 mg per deciliter. (ClinicalTrials.gov number, NCT00220987.)

Impact of human factors on clinical protocol performance: a proposed assessment framework and case examples

BACKGROUND

Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and can often produce highly variable results. Thus, tight control remains elusive as there is not enough understanding of the relationship between control performance and protocol design, particularly with regard to how a given protocol is implemented.

METHODS

This article examines the role of human factors and how individuals relate to technological protocols in clinical settings. The study consists of an overall brief review that is used to create a first graphical representation of the impact of human factors in clinical medical protocol implementations. This initial framework is examined in the context of two similar, but different, case studies-the specialized relative insulin and nutrition tables glycemic control protocol and the TREAT system for antibiotic selection.

RESULTS

A graphical framework relating the human factors impact on medical protocol implementation is created. This framework describes the primary impacts on performance as resulting from clinical burden and protocol transparency. Their primary effect is on compliance with the protocol, which directly affects performance and outcome, particularly in long-term studies versus short pilot studies.

SUMMARY

Compliance is a key element in obtaining the best clinical outcome that a given protocol can provide. The issues that most affect compliance are quite often unrelated to the patient or treatment, but are a function of the protocol design and its ability to integrate (by its design) into a given clinical setting. A framework for examining these issues in design and in post-hoc assessment is therefore proposed and examined in two brief case studies.