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Purpose: Although the course of disease of type 1 and type 2 diabetes differs, the distinction is rarely made when patients are admitted to the intensive care unit (ICU). Here, we report patient- and admission-related characteristics in relation to glycemic measures of patients with type 1 and type 2 diabetes admitted to the ICU., Materials and Methods: A retrospective chart review was performed of 1574 patients with diabetes admitted between 2004 and 2011 to our ICU. Glycemic measures included mean glucose, the incidence of hypoglycemia and hyperglycemia, percentage of glucose values in/below/above target, and glucose variability. The ICU and hospital mortality were secondary outcomes., Results: We classified 2% (n=27) of patients as having type 1 diabetes and 98% (n=1547) as having type 2 diabetes. Patients with type 1 diabetes were significantly younger, had a lower body mass index, and were more frequently admitted to the ICU for medical diagnoses. No differences in glycemic measures were found, apart from a 20% higher glucose variability in the type 1 diabetes group., Conclusions: Patients with type 1 diabetes showed a higher glucose variability, but overall glycemic control was not different between patients with type 1 and type 2 diabetes. Very few diabetes patients admitted to the ICU have type 1 diabetes., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Publication of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) trial in 2009 and several observational studies caused a change in the recommendations for blood glucose control in intensive care patients. We evaluated local trends in blood glucose control in intensive care units in the Netherlands before and after the publication of the NICE-SUGAR trial and the revised Surviving Sepsis Campaign (SSC) guidelines in 2012., Methods: Survey focusing on the timing of changes in thresholds in local guidelines for blood glucose control and interrupted time-series analysis of patients admitted to seven intensive care units in the Netherlands from September 2008 through July 2014. Statistical process control was used to visualise and analyse trends in metrics for blood glucose control in association with the moment changes became effective., Results: Overall, the mean blood glucose level increased and the median percentage of blood glucose levels within the normoglycaemic range and in the hypoglycaemic range decreased, while the relative proportion of hyperglycaemic measurements increased. Changes in metrics were notable after publication of the NICE-SUGAR trial and the SSC guidelines but more frequent after changes in local guidelines; some changes seemed to appear independent of changes in local guidelines., Conclusion: Local guidelines for blood glucose practice have changed in intensive care units in the Netherlands since the publication of the NICE-SUGAR trial and the revised SSC guidelines. Trends in the metrics for blood glucose control suggest new, higher target ranges for blood glucose 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.

Objective: Despite the wide use of blood glucose management guidelines in intensive care (IC), hyperglycemia is still common. The aim of this study was the discovery of possible hyperglycemia determinants by applying the Patient Rule Induction Method (PRIM) to routinely collected data within the first 24 hours of admission, and to relate them to the literature., Methods: PRIM was applied in two set-ups to data of 2001 IC patients including 50,021 records of blood glucose levels and other variables. The independent predictors of blood glucose measurements were variables whose value is known before the time of the corresponding measurement. Subgroups were validated using a random split design, and time-sensitivity of performance was analyzed., Results: PRIM was able to identify relatively large subgroups having markedly high mean glucose values. PRIM also discovered possible determinants of which less is known about their relationship to hyperglycemia. Some possible determinants reported in the literature were not found by PRIM., Conclusions: We demonstrated for the first time the utility of using subgroup discovery to uncover possible determinants for non-responsiveness to treatment. This implies the possible use of this technology to scrutinize the effects of various guidelines in clinical medicine on patient outcomes without requiring the development of a global predictive model. We hypothesize that by focusing on the identified subgroups, clinical guidelines may be improved. Further research is required to test this hypothesis.