Glycemic Effects of a Low-Carbohydrate Enteral Formula Compared With an Enteral Formula of Standard Composition in Critically Ill Patients: An Open-Label Randomized Controlled Clinical Trial.

BACKGROUND: Enteral low-carbohydrate formulas (LCFs) could serve as a noninsulin alternative for the treatment of stress hyperglycemia in critically ill patients. We compared the glycemic effects of an LCF with a standard formula. METHODS: We conducted an open-label randomized trial in patients admitted to our intensive care unit between September 2015 and June 2016. Adult patients with an indication for enteral nutrition were randomized to an LCF (Glucerna 1.5 kcal) or a standard enteral formula (Fresubin Energy Fibre, with additional protein supplement). Primary outcome was glucose variability defined as mean absolute glucose (MAG) change (mmol/L/h). Secondary outcomes were mean glucose, time in target, hypoglycemic and hyperglycemic events, and insulin requirements. We assessed glycemic outcomes per blinded continuous glucose monitoring (CGM) system and compared outcomes with glucose measurements per blood gas analysis and point-of-care device. RESULTS: We randomized 107 patients (LCF: n = 53; standard: n = 54). Six patients had no CGM data, leaving 101 patients (n = 52; n = 49) for the intention-to-treat analysis. MAG change and time in target range were not different between groups. LCF gave a lower mean glucose measured per point-of-care device (7.8 ± 1.0 vs 8.4 ± 1.1 mmol/L, P = .007). LCF patients required significantly less insulin on the second study day (46.8 vs 68.0 IU, P = .036). CONCLUSION: LCF showed a trend toward a modestly reduced mean glucose and significantly lower insulin requirements as compared with standard feeding but had no effect on glucose variability or time in target range.

Higher glucose variability in type 1 than in type 2 diabetes patients admitted to the intensive care unit: A retrospective cohort study.

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.

Upfront Genotyping of DPYD*2A to Individualize Fluoropyrimidine Therapy: A Safety and Cost Analysis.

PURPOSE: Fluoropyrimidines are frequently prescribed anticancer drugs. A polymorphism in the fluoropyrimidine metabolizing enzyme dihydropyrimidine dehydrogenase (DPD; ie, DPYD*2A) is strongly associated with fluoropyrimidine-induced severe and life-threatening toxicity. This study determined the feasibility, safety, and cost of DPYD*2A genotype-guided dosing. PATIENTS AND METHODS: Patients intended to be treated with fluoropyrimidine-based chemotherapy were prospectively genotyped for DPYD*2A before start of therapy. Variant allele carriers received an initial dose reduction of ≥ 50% followed by dose titration based on tolerance. Toxicity was the primary end point and was compared with historical controls (ie, DPYD*2A variant allele carriers receiving standard dose described in literature) and with DPYD*2A wild-type patients treated with the standard dose in this study. Secondary end points included a model-based cost analysis, as well as pharmacokinetic and DPD enzyme activity analyses. RESULTS: A total of 2,038 patients were prospectively screened for DPYD*2A, of whom 22 (1.1%) were heterozygous polymorphic. DPYD*2A variant allele carriers were treated with a median dose-intensity of 48% (range, 17% to 91%). The risk of grade ≥ 3 toxicity was thereby significantly reduced from 73% (95% CI, 58% to 85%) in historical controls (n = 48) to 28% (95% CI, 10% to 53%) by genotype-guided dosing (P < .001); drug-induced death was reduced from 10% to 0%. Adequate treatment of genotype-guided dosing was further demonstrated by a similar incidence of grade ≥ 3 toxicity compared with wild-type patients receiving the standard dose (23%; P = .64) and by similar systemic fluorouracil (active drug) exposure. Furthermore, average total treatment cost per patient was lower for screening (€2,772 [$3,767]) than for nonscreening (€2,817 [$3,828]), outweighing screening costs. CONCLUSION: DPYD*2A is strongly associated with fluoropyrimidine-induced severe and life-threatening toxicity. DPYD*2A genotype-guided dosing results in adequate systemic drug exposure and significantly improves safety of fluoropyrimidine therapy for the individual patient. On a population level, upfront genotyping seemed cost saving.

Lowering blood glucose during hip surgery does not influence coagulation activation.

BACKGROUND: Hyperglycaemia during and after hip surgery is associated with coagulation activation and an increased risk of venous thromboembolism. Whether lowering of glucose levels during hip surgery diminishes coagulation activation is unknown. We investigated the efficacy of the human GLP-1 analogue liraglutide to lower glucose during and after hip surgery and studied its influence on coagulation activation. METHODS: A total of 37 obese subjects who underwent hip surgery were randomized to subcutaneous liraglutide or placebo for 4 consecutive days, starting one day prior to surgery. Glucose levels and coagulation indices at three fixed time-points (pre-operative, 2 h post-operative and 3 days post-operative) were measured. RESULTS: Liraglutide reduced glucose at day three post-surgery (median glucose (IQR) liraglutide 5.5 (5.2-5.7) vs. placebo 5.8 (5.5-6.2); difference 0.3 mmol/L, P = 0.04). Changes in 6 out of 8 coagulation indices studied did not differ between the two groups. Only D-dimer levels were significantly lower in the liraglutide group at day three post-surgery and FVIII levels were significantly higher in the liraglutide group 2 h post-surgery. CONCLUSION: Although the human GLP-1 analogue liraglutide moderately reduced post-operative blood glucose levels in non-diabetic and prediabetic obese patients undergoing elective hip surgery, no changes were observed with respect to coagulation activation.

Accuracy of Intra-arterial and Subcutaneous Continuous Glucose Monitoring in Postoperative Cardiac Surgery Patients in the ICU.

BACKGROUND: The GluCath® intra-arterial continuous glucose monitoring (IA-CGM) system uses a novel quenched chemical fluorescence sensing mechanism to optically measure blood glucose when deployed in the radial artery. The aim of this study was to compare the accuracy of the IA-CGM and the FreeStyle Navigator® subcutaneous continuous glucose monitoring (SC-CGM) system with standard care. METHODS: After admission to the intensive care unit (ICU), the IA-CGM was inserted via a 20 gauge radial arterial study catheter and the SC-CGM was placed at the abdominal wall of postoperative cardiac surgery patients with an expected ICU length of stay > 24 hours. Each device was calibrated according to manufacturer instructions. Glucose values of both CGM systems were blinded for the clinical staff. Reference blood glucose samples were collected from the study catheter every 1-2 hours for at least 24 hours and analyzed on a Radiometer ABL blood gas analyzer. RESULTS: The IA-CGM and SC-CGM sensors were successfully inserted in 8 subjects. Accuracy assessment was performed with 183 paired points: 85.8% of the IA-CGM measurements and 84.2% of the SC-CGM measurements met ISO 15197:2003 glucometer criteria (within 20%) across a 79-248 mg/dl (4.4-13.8 mmol/L) glucose range. Overall ± SD mean absolute relative difference was 12.3 ± 11.3% for IA-CGM and 11.1 ± 8.3% for SC-CGM (difference -1.2%, 95% CI -3.3 to 0.8; P = .24). CONCLUSIONS: The IA-CGM system directly measured arterial blood glucose and did not interfere with clinical care. However, accuracy was similar to that of the less invasive SC-CGM device.

Insulin treatment guided by subcutaneous continuous glucose monitoring compared to frequent point-of-care measurement in critically ill patients: a randomized controlled trial.

INTRODUCTION: Glucose measurement in intensive care medicine is performed intermittently with the risk of undetected hypoglycemia. The workload for the ICU nursing staff is substantial. Subcutaneous continuous glucose monitoring (CGM) systems are available and may be able to solve some of these issues in critically ill patients. METHODS: In a randomized controlled design in a mixed ICU in a teaching hospital we compared the use of subcutaneous CGM with frequent point of care (POC) to guide insulin treatment. Adult critically ill patients with an expected stay of more than 24 hours and in need of insulin therapy were included. All patients received subcutaneous CGM. CGM data were blinded in the control group, whereas in the intervention group these data were used to feed a computerized glucose regulation algorithm. The same algorithm was used in the control group fed by intermittent POC glucose measurements. Safety was assessed with the incidence of severe hypoglycemia (<2.2 mmol/L), efficacy with the percentage time in target range (5.0 to 9.0 mmol/L). In addition, we assessed nursing workload and costs. RESULTS: In this study, 87 patients were randomized to the intervention and 90 to the control group. CGM device failure resulted in 78 and 78 patients for analysis. The incidence of severe glycemia and percentage of time within target range was similar in both groups. A significant reduction in daily nursing workload for glucose control was found in the intervention group (17 versus 36 minutes; P <0.001). Mean daily costs per patient were significantly reduced with EUR 12 (95% CI -32 to -18, P = 0.02) in the intervention group. CONCLUSIONS: Subcutaneous CGM to guide insulin treatment in critically ill patients is as safe and effective as intermittent point-of-care measurements and reduces nursing workload and daily costs. A new algorithm designed for frequent measurements may lead to improved performance and should precede clinical implementation. TRIAL REGISTRATION: Clinicaltrials.gov, NCT01526044. Registered 1 February 2012.

Poor agreement of computerized calculators for mean amplitude of glycemic excursions.

BACKGROUND: Glucose variability has been identified as a predictor of hypoglycemia and has been associated with mortality in critically ill patients without diabetes. A popular metric to quantify glucose variability is the mean amplitude of glycemic excursions (MAGE). The "ruler and pencil" approach to calculate MAGE is operator-dependent and time-consuming for analysis of continuous glucose monitoring data. Therefore, several computer software programs have been developed for the automated calculation of MAGE. The aim of our study was to evaluate the agreement of currently available MAGE calculators when applied to the same set of continuous glucose monitoring (CGM) traces. MATERIALS AND METHODS: Four software programs for calculation of MAGE were identified and used to calculate MAGE of 21 CGM traces from seven patients with type 1 diabetes. Subsequently, the median MAGE per calculator was calculated. The correlation between the MAGE calculators was evaluated by Spearman's correlation analysis. Between-group comparison was performed using analysis of variance. RESULTS: The median MAGE (interquartile range) per calculator was 8.7 (7.1-10.7), 6.7 (5.5-8.6), 6.7 (5.2-8.6), and 5.8 (4.3-7.1), which was statistically different overall (P<0.001). The correlation coefficients between the calculators ranged from 0.787 to 0.999. CONCLUSIONS: Available computer programs developed to calculate MAGE show varying agreement. Although software programs for the calculation of MAGE would seem attractive to assess glucose variability, their use has limitations by different outcomes, in the absence of a gold standard.

The effect of diabetes mellitus on the association between measures of glycaemic control and ICU mortality: a retrospective cohort study.

INTRODUCTION: In critical illness, four measures of glycaemic control are associated with ICU mortality: mean glucose concentration, glucose variability, the incidence of hypoglycaemia (≤2.2 mmol/l) or low glucose (2.3 to 4.7 mmol/l). Underlying diabetes mellitus (DM) might affect these associations. Our objective was to study whether the association between these measures of glycaemic control and ICU mortality differs between patients without and with DM and to explore the cutoff value for detrimental low glucose in both cohorts. METHODS: This retrospective database cohort study included patients admitted between January 2004 and June 2011 to a 24-bed medical/surgical ICU in a teaching hospital. We analysed glucose and outcome data from 10,320 patients: 8,682 without DM and 1,638 with DM. The cohorts were subdivided into quintiles of mean glucose and quartiles of glucose variability. Multivariable regression models were used to examine the independent association between the four measures of glycaemic control and ICU mortality, and for defining the cutoff value for detrimental low glucose. RESULTS: Regarding mean glucose, a U-shaped relation was observed in the non-DM cohort with an increased ICU mortality in the lowest and highest glucose quintiles (odds ratio=1.4 and 1.8, P<0.001). No clear pattern was found in the DM cohort. Glucose variability was related to ICU mortality only in the non-DM cohort, with highest ICU mortality in the upper variability quartile (odds ratio=1.7, P<0.001). Hypoglycaemia was associated with ICU mortality in both cohorts (odds ratio non-DM=2.5, P<0.001; odds ratio DM=4.2, P=0.001), while low-glucose concentrations up to 4.9 mmol/l were associated with an increased risk of ICU mortality in the non-DM cohort and up to 3.5 mmol/l in the DM cohort. CONCLUSION: Mean glucose and high glucose variability are related to ICU mortality in the non-DM cohort but not in the DM cohort. Hypoglycaemia (≤2.2 mmol/l) was associated with ICU mortality in both. The cutoff value for detrimental low glucose is higher in the non-DM cohort (4.9 mmol/l) than in the DM cohort (3.5 mmol/l). While hypoglycaemia (≤2.2 mmol/l) should be avoided in both groups, DM patients seem to tolerate a wider glucose range than non-DM patients.