Roosmarijn T. M. van Hooijdonk

Diabetic status and the relation of the three domains of glycemic control to mortality in critically ill patients: an international multicenter cohort study.

IntroductionHyperglycemia, hypoglycemia, and increased glycemic variability have each beenindependently associated with increased risk of mortality in critically illpatients. The role of diabetic status on modulating the relation of these threedomains of glycemic control with mortality remains uncertain. The purpose of thisinvestigation was to determine how diabetic status affects the relation ofhyperglycemia, hypoglycemia, and increased glycemic variability with the risk ofmortality in critically ill patients.MethodsThis is a retrospective analysis of prospectively collected data involving 44,964patients admitted to 23 intensive care units (ICUs) from nine countries, betweenFebruary 2001 and May 2012. We analyzed mean blood glucose concentration (BG),coefficient of variation (CV), and minimal BG and created multivariable models toanalyze their independent association with mortality. Patients were stratifiedaccording to the diagnosis of diabetes.ResultsAmong patients without diabetes, mean BG bands between 80 and 140 mg/dl wereindependently associated with decreased risk of mortality, and mean BG bands> 140 mg/dl, with increased risk of mortality. Among patients withdiabetes, mean BG from 80 to 110 mg/dl was associated with increased risk ofmortality and mean BG from 110 to 180 mg/dl with decreased risk of mortality. Aneffect of center was noted on the relation between mean BG and mortality.Hypoglycemia, defined as minimum BG <70 mg/dl, was independently associatedwith increased risk of mortality among patients with and without diabetes andincreased glycemic variability, defined as CV > 20%, was independentlyassociated with increased risk of mortality only among patients without diabetes.Derangements of more than one domain of glycemic control had a cumulativeassociation with mortality, especially for patients without diabetes.ConclusionsAlthough hyperglycemia, hypoglycemia, and increased glycemic variability is eachindependently associated with mortality in critically ill patients, diabeticstatus modulates these relations in clinically important ways. Our findingssuggest that patients with diabetes may benefit from higher glucose target rangesthan will those without diabetes. Additionally, hypoglycemia is independentlyassociated with increased risk of mortality regardless of the patients diabeticstatus, and increased glycemic variability is independently associated withincreased risk of mortality among patients without diabetes.See related commentary by Krinsley,http://ccforum.com/content/17/2/131See related commentary by Finfer and Billot,http://ccforum.com/content/17/2/134

Interobserver agreement of Centers for Disease Control and Prevention criteria for classifying infections in critically ill patients

Objectives:Correct classification of the source of infection is important in observational and interventional studies of sepsis. Centers for Disease Control and Prevention criteria are most commonly used for this purpose, but the robustness of these definitions in critically ill patients is not known. We hypothesized that in a mixed ICU population, the performance of these criteria would be generally reduced and would vary among diagnostic subgroups. Design:Prospective cohort. Setting:Data were collected as part of a cohort of 1,214 critically ill patients admitted to two hospitals in The Netherlands between January 2011 and June 2011. Patients:Eight observers assessed a random sample of 168 of 554 patients who had experienced at least one infectious episode in the ICU. Each patient was assessed by two randomly selected observers who independently scored the source of infection (by affected organ system or site), the plausibility of infection (rated as none, possible, probable, or definite), and the most likely causative pathogen. Assessments were based on a post hoc review of all available clinical, radiological, and microbiological evidence. The observed diagnostic agreement for source of infection was classified as partial (i.e., matching on organ system or site) or complete (i.e., matching on specific diagnostic terms), for plausibility as partial (2-point scale) or complete (4-point scale), and for causative pathogens as an approximate or exact pathogen match. Interobserver agreement was expressed as a concordant percentage and as a kappa statistic. Interventions:None. Measurements and Main Results:A total of 206 infectious episodes were observed. Agreement regarding the source of infection was 89% (183/206) and 69% (142/206) for a partial and complete diagnostic match, respectively. This resulted in a kappa of 0.85 (95% CI, 0.79–0.90). Agreement varied from 63% to 91% within major diagnostic categories and from 35% to 97% within specific diagnostic subgroups, with the lowest concordance observed in cases of ventilator-associated pneumonia. In the 142 episodes for which a complete match on source of infection was obtained, the interobserver agreement for plausibility of infection was 83% and 65% on a 2- and 4-point scale, respectively. For causative pathogen, agreement was 78% and 70% for an approximate and exact pathogen match, respectively. Conclusions:Interobserver agreement for classifying sources of infection using Centers for Disease Control and Prevention criteria was excellent overall. However, full concordance on all aspects of the diagnosis between independent observers was rare for some types of infection, in particular for ventilator-associated pneumonia.

Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study

BACKGROUND Host responses during sepsis are highly heterogeneous, which hampers the identification of patients at high risk of mortality and their selection for targeted therapies. In this study, we aimed to identify biologically relevant molecular endotypes in patients with sepsis. METHODS This was a prospective observational cohort study that included consecutive patients admitted for sepsis to two intensive care units (ICUs) in the Netherlands between Jan 1, 2011, and July 20, 2012 (discovery and first validation cohorts) and patients admitted with sepsis due to community-acquired pneumonia to 29 ICUs in the UK (second validation cohort). We generated genome-wide blood gene expression profiles from admission samples and analysed them by unsupervised consensus clustering and machine learning. The primary objective of this study was to establish endotypes for patients with sepsis, and assess the association of these endotypes with clinical traits and survival outcomes. We also established candidate biomarkers for the endotypes to allow identification of patient endotypes in clinical practice. FINDINGS The discovery cohort had 306 patients, the first validation cohort had 216, and the second validation cohort had 265 patients. Four molecular endotypes for sepsis, designated Mars1-4, were identified in the discovery cohort, and were associated with 28-day mortality (log-rank p=0·022). In the discovery cohort, the worst outcome was found for patients classified as having a Mars1 endotype, and at 28 days, 35 (39%) of 90 people with a Mars1 endotype had died (hazard ratio [HR] vs all other endotypes 1·86 [95% CI 1·21-2·86]; p=0·0045), compared with 23 (22%) of 105 people with a Mars2 endotype (HR 0·64 [0·40-1·04]; p=0·061), 16 (23%) of 71 people with a Mars3 endotype (HR 0·71 [0·41-1·22]; p=0·19), and 13 (33%) of 40 patients with a Mars4 endotype (HR 1·13 [0·63-2·04]; p=0·69). Analysis of the net reclassification improvement using a combined clinical and endotype model significantly improved risk prediction to 0·33 (0·09-0·58; p=0·008). A 140-gene expression signature reliably stratified patients with sepsis to the four endotypes in both the first and second validation cohorts. Only Mars1 was consistently significantly associated with 28-day mortality across the cohorts. To facilitate possible clinical use, a biomarker was derived for each endotype; BPGM and TAP2 reliably identified patients with a Mars1 endotype. INTERPRETATION This study provides a method for the molecular classification of patients with sepsis to four different endotypes upon ICU admission. Detection of sepsis endotypes might assist in providing personalised patient management and in selection for trials. FUNDING Center for Translational Molecular Medicine, Netherlands.

Glucose prediction by analysis of exhaled metabolites - a systematic review.

BackgroundIn critically ill patients, glucose control with insulin mandates time– and blood–consuming glucose monitoring. Blood glucose level fluctuations are accompanied by metabolomic changes that alter the composition of volatile organic compounds (VOC), which are detectable in exhaled breath. This review systematically summarizes the available data on the ability of changes in VOC composition to predict blood glucose levels and changes in blood glucose levels.MethodsA systematic search was performed in PubMed. Studies were included when an association between blood glucose levels and VOCs in exhaled air was investigated, using a technique that allows for separation, quantification and identification of individual VOCs. Only studies on humans were included.ResultsNine studies were included out of 1041 identified in the search. Authors of seven studies observed a significant correlation between blood glucose levels and selected VOCs in exhaled air. Authors of two studies did not observe a strong correlation. Blood glucose levels were associated with the following VOCs: ketone bodies (e.g., acetone), VOCs produced by gut flora (e.g., ethanol, methanol, and propane), exogenous compounds (e.g., ethyl benzene, o–xylene, and m/p–xylene) and markers of oxidative stress (e.g., methyl nitrate, 2–pentyl nitrate, and CO).ConclusionThere is a relation between blood glucose levels and VOC composition in exhaled air. These results warrant clinical validation of exhaled breath analysis to monitor blood glucose levels.

Sweet Spot: Glucose Control in the Intensive Care Unit.

Hyperglycemia, hypoglycemia, and glycemic variability are all independently associated with morbidity and mortality of critically ill patients. A strategy aiming at normoglycemia (so-called tight glycemic control) could improve outcomes of critically ill patients, but results from randomized controlled trials of tight glycemic control are conflicting. Strict glycemic control is associated with an increased risk of hypoglycemia, which could offset the benefit of this intervention. Notably, the risk of hypoglycemia is not necessarily removed with less tight glucose control regimens. The best targets of blood glucose control in critically ill patients, therefore, remain a matter of debate. It should be realized that blood glucose control is a complex intervention, consisting of many critical aspects that have the potential to affect its efficacy and safety. Efficacy, and in particular safety, of blood glucose control could still improve. First, glucose algorithms could overcome the lack of knowledge and skills of nursing staff when they are less experienced in safe and efficient blood glucose control. Several computerized glucose control algorithms have been developed over recent years, but they all need clinical validation. Also, the workload induced by such algorithms should be evaluated. Second, continuous blood glucose monitoring has the potential to improve safety and efficacy. Until recently, blood glucose levels were monitored manually using point-of-care devices with significant inaccuracies. Various continuous monitoring systems have been developed, but studies testing their accuracies and usefulness in an intensive care unit setting are highly needed.

Glycemic variability is complex - is glucose complexity variable?

Observational studies show an independent association between increased glycemic variability and higher mortality in critically ill patients. Minimization of glycemic variability is therefore suggested as a new target of glycemic control, which may require very frequent or almost continuous monitoring of glucose levels. Brunner and colleagues show the use of real-time subcutaneous continuous glucose monitoring does not decrease glycemic variability. Continuous glucose monitoring, however, may reveal changes in glucose complexity, which may be of interest since both increased and decreased glucose complexity is associated with higher mortality in the critically ill.

DETECT the Extremes that Usually Remain Undetected in Conventional Observational Studies

In medicine, clinicians and researchers tend to score the quality of available evidence according to the famous pyramid of evidence–based medicine in which controlled trials and their meta-analyses are seen as superior to observational studies (i.e., yielding evidence of a better quality). Indeed, we frequently say that what is suggested in observational studies must be confirmed in 1 or more controlled trials. This approach is not always practical and is sometimes even unrealistic, as not everything can or will be captured in the setting of a controlled trial. Under certain circumstances, observational studies could perform better, in particular when they are large enough to capture reasonable numbers of certain events. Examples of this are observational studies using data mining of blood glucose concentrations in critically ill patients. Glucose control in the critically ill has been extensively studied by clinicians and researchers over the last 2 decades. Numerous observational studies have shown a clear association between hyperglycemia and mortality in intensive care unit (ICU)4 patients (1–3), suggesting that a strategy aiming at normoglycemia with intravenous insulin could reduce mortality. Indeed, a series of controlled trials in different cohorts of critically ill patients showed that this strategy, frequently called strict glycemic control (SGC), improved the outcome of critically ill patients (4–6). This provides an excellent example of how evidence–based medicine works: what was suggested in observational studies was confirmed in controlled trials. Unfortunately, subsequent controlled trials could not confirm the benefit of intensive insulin therapy, for several reasons explained elsewhere (7). One reason could be the higher incidence of severe hypoglycemia. Indeed, observational studies showed that in critically ill patients, severe hypoglycemia is associated with death as well, and the …