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Substantial attention and resources have been directed to improving outcomes of patients with critical illnesses, in particular sepsis, but all recent clinical trials testing various interventions or strategies have failed to detect a robust benefit on mortality. Acute heart failure is also a critical illness, and although the underlying etiologies differ, acute heart failure and sepsis are critical care illnesses that have a high mortality in which clinical trials have been difficult to conduct and have not yielded effective treatments. Both conditions represent a syndrome that is often difficult to define with a wide variation in patient characteristics, presentation, and standard management across institutions. Referring to past experiences and lessons learned in acute heart failure may be informative and help frame research in the area of sepsis. Academic heart failure investigators and industry have worked closely with regulators for many years to transition acute heart failure trials away from relying on dyspnea assessments and all-cause mortality as the primary measures of efficacy, and recent trials have been designed to assess novel clinical composite endpoints assessing organ dysfunction and mortality while still assessing all-cause mortality as a separate measure of safety. Applying the lessons learned in acute heart failure trials to severe sepsis and septic shock trials might be useful to advance the field. Novel endpoints beyond all-cause mortality should be considered for future sepsis trials. [ABSTRACT FROM AUTHOR]

The article presents a study which examined (AMI) patients in the first 48 hours admitted to U.S. hospitals from 2000 to 2008, to identify glucose variability (GV) metrics that are most predictive of in-hospital mortality and examine whether GV provides additional prognostic information above and beyond mean blood glucose (BG). Mean BG remains an independent predictor of in-hospital death, even in models adjusting for GV and multiple patient characteristics.

The article discusses a study on the relationship between levels of serum potassium and in-hospital mortality in patients with acute myocardial infarction (AMI) in the era of widespread use of β-blocker and reperfusion therapy. The study used information on 38,689 patients with AMI included in the Health Facts database of Cerner Corp. in the U.S. The occurrence of ventricular fibrillation, ventricular flutter or cardiac arrest in 1707 patients during hospitalization is discussed. AMI patients with postadmission serum potassium levels between 3.5 and less than 4.5 milliequivalents per liter (mEq/L) were found to have the lowest mortality.

The article comments on the study conducted by Gandhi and colleagues concerning the levels of blood glucose during cardiac surgery. The study presents that intensive insulin therapy to maintain normal levels of blood glucose could improve survival of the patient. The author comments that Gandhi and colleagues had changed the postoperative routine management in the intensive care unit to include postoperative tight blood glucose control for all patients during the randomized, controlled study.

Background: Implementing tight glycemic control (TGC) in intensive care unit (ICU) patients requires accurate blood glucose (BG) monitoring. We evaluated the performance of two commercially available bedside glucometers, Accu-Chek and HemoCue, in patients admitted to the ICU and in whom TGC was applied., Methods: Thirty-seven adult ICU patients were prospectively included. During 48 hours, BG was determined simultaneously on the same arterial blood sample using the two point-of-care testing (POCT) glucometers as compared with the standard technique. Data of 452 paired measurements were analyzed using linear regression, Clark error grid analysis (EGA), the method of Bland-Altman, and the GLYCENSIT procedure., Results: Both tested glucometers showed satisfactory results when evaluated with linear regression and EGA. Correlation coefficients were above 0.9, and 100% of all the glucose readings were within the safe zones A and B using EGA. However, when applying more appropriate tests, both sensors failed to provide sufficient accuracy in the setting of TGC in ICU patients. The Hemocue revealed a bias of >10 mg/dl with a trend to systematically overestimate the actual BG value. The bias for the Accu-Chek was 6 mg/dl with wide limits of agreement and a variable over- and underestimation of the actual BG value depending on the level of BG (hypo-, normo-, or hyperglycemia)., Conclusions: When TGC is implemented in ICU practice, caution is warranted when adjusting insulin rates based only on BG readings obtained by the tested glucometers. ICU practitioners should weigh the advantages and disadvantages of such devices: a greater bias but with a more predictable error and measurement behavior versus a somewhat lower bias but with an unpredictable direction of the difference.

Background: In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability., Methods: The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay system in the ICU is analyzed with GLYCENSIT., Results: THE GLYCENSIT ANALYSIS COMPRISES THREE PHASES: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU., Conclusions: The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.

Background: Strict blood glucose control by applying nurse-driven protocols is common nowadays in intensive care units (ICUs). Implementation of a predictive control system can potentially reduce the workload for medical staff but requires a model for accurately predicting the glycemia signal within a certain time horizon., Methods: GlucoDay (A. Menarini Diagnostics, Italy) data coming from 19 critically ill patients (from a surgical ICU) are used to estimate the initial ICU "minimal" model (based on data of the first 24 hours) and to reestimate the model as new measurements are obtained. The reestimation is performed every hour or every 4 hours. For both approaches the optimal size of the data set for each reestimation is determined., Results: The prediction error that is obtained when applying the 1-hour reestimation strategy is significantly smaller than when the model is reestimated only every 4 hours (p < 0.001). The optimal size of the data set to be considered in each reestimation process of the ICU minimal model is found to be 4 hours. The obtained average "mean percentage error" is 7.6% (SD 3.1%) and 14.6% (SD 7.0%) when the model is reestimated every hour and 4 hours, respectively., Conclusions: Implementation of the ICU minimal model in the appropriate reestimation process results in clinically acceptable prediction errors. Therefore, the model is able to predict glycemia trends of patients admitted to the surgical ICU and can potentially be used in a predictive control system.