Accuracy of AccuChek glucose measurement in intensive care patients*:

Accuracy of Blood Glucose Measurement and Blood Glucose Targets

BACKGROUND

To summarize new evidence regarding the methodological aspects of blood glucose control in the intensive care unit (ICU).

METHODS

We reviewed the literature on blood glucose control in the ICU up to August 2019 through Ovid Medline and Pubmed.

RESULTS

Since the publication of the Leuven studies, the benefits of glycemic control have been recognized. However, the methodology of blood glucose control, notably the blood glucose measurement accuracy and the insulin titration protocol, plays an important but underestimated role. This may partially explain the negative results of the large, pragmatic multicenter trials and made everyone realize that tight glycemic control with less-frequent glucose measurements on less accurate blood glucose meters is neither feasible nor advisable in daily practice. Blood gas analyzers remain the gold standard. New generation point-of-care blood glucose meters may be an alternative when using whole blood of critically ill patients in combination with a clinically validated insulin dosing algorithm.

CONCLUSION

When implementing blood glucose management in an ICU one needs to take into account the interaction between aimed glycemic target and blood glucose measurement methodology.

Intraoperative hyperglycemia is independently associated with infectious complications after non-cardiac surgery

Background

Perioperative hyperglycemia and its associated increase in morbidity and mortality have been well studied in the critical care and cardiac surgery literature. However, there is little data regarding the impact of intraoperative hyperglycemia on post-operative infectious complications in non-cardiac surgery.

Methods

All National Surgery Quality Improvement Program patients undergoing general, vascular, and urological surgery at our tertiary care center were reviewed. After integrating intraoperative glucose measurements from our intraoperative electronic health record, we categorized patients as experiencing mild (8.3–11.0 mmol/L), moderate (11.1–16.6 mmol/L), and severe (≥ 16.7 mmol/L) intraoperative hyperglycemia. Using multiple logistic regression to adjust for patient comorbidities and surgical factors, we evaluated the association of hyperglycemia with the primary outcome of postoperative surgical site infection, pneumonia, urinary tract infection, or sepsis within 30 days.

Results

Of 13,954 patients reviewed, 3150 patients met inclusion criteria and had an intraoperative glucose measurement. 49% (n = 1531) of patients experienced hyperglycemia and 15% (n = 482) patients experienced an infectious complication. Patients with mild (adjusted odds ratio 1.30, 95% confidence interval [1.01 to 1.68], p-value = 0.04) and moderate hyperglycemia (adjusted odds ratio 1.57, 95% confidence interval [1.08–2.28], p-value = 0.02) had a statistically significant risk-adjusted increase in infectious complications. The model c-statistic was 0.72 [95% confidence interval 0.69–0.74].

Conclusions

This is one of the first studies to demonstrate an independent relationship between intraoperative hyperglycemia and postoperative infectious complications. Future studies are needed to evaluate a causal relationship and impact of treatment.

Electronic supplementary material

The online version of this article (10.1186/s12871-018-0546-0) contains supplementary material, which is available to authorized users.

Glucometrics and Insulinometrics

PURPOSE OF REVIEW

Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines.

RECENT FINDINGS

There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

Validity of bedside blood glucose measurement in critically ill patients with intensive insulin therapy

Background and Aims:

There have been variable results on the practice of tight glycemic control, and studies have demonstrated that point-of-care (POC) glucometers have variable accuracy. Glucometers must be accurate, and many variables can affect blood glucose levels. The purpose of this study was to determine the difference between blood glucose concentrations obtained from POC glucometers and laboratory results in critically ill patients with intensive insulin therapy.

Materials and Methods:

This was a descriptive study which enrolled 300 critically ill patients. Four samples of arterial blood were collected and analyzed at the bedside with the POC glucometer and also in the central laboratory to obtain the blood glucose level. To define the effect of various factors on this relation, we noted the levels of hemoglobin (Hb), PaO₂, body temperature, bilirubin, history of drug usage, and sepsis.

Results:

There were not any significant differences between blood sugar levels using laboratory and glucometer methods of measurements. There was a good and significant correlation between glucose levels between two methods (r = 0.81, P < 0.001). Among evaluated factors (body temperature, bilirubin level, blood pressure, Hb level, PaO₂, sepsis, and drugs) which added one by one in model, just drugs decreased the correlation more than others (r = 0.78).

Conclusions:

The results of POC glucometer differ from laboratory glucose concentrations, especially in critically ill patients with unstable hemodynamic status while receiving several drugs. This may raise the concern about using POC devices for tight glycemic control in critically ill patients. These results should be interpreted with caution because of the large variation of accuracy among different glucometer devices.

Monitoring nutrition and glucose in acute brain injury

The metabolic response to injury is well described; however, very little is understood about optimal markers to measure this response. This summary will address the current evidence about monitoring nutritional status including blood glucose after acute brain injury (ABI). An electronic literature search was conducted for English language articles describing the testing, utility, and optimal methods to measure nutritional status and blood glucose levels in the neurocritical care population. A total of 45 articles were included in this review. Providing adequate and timely nutritional support can help improve outcome after ABI. However, the optimal content and total nutrition requirements remain unclear. In addition, how best to monitor the nutritional status in ABI is still being elucidated, and at present, there is no validated optimal method to monitor the global response to nutritional support on a day-to-day basis in ABI patients. Nitrogen balance may be monitored to assess the adequacy of caloric intake as it relates to protein energy metabolism, but indirect calorimetry, anthropometric measurement, or serum biomarker requires further validation. The adverse effects of hyperglycemia in ABI are well described, and data indicate that blood glucose should be carefully controlled in critically ill patients. However, the optimal frequency or duration for blood glucose monitoring after ABI remains poorly defined. There are significant knowledge gaps about monitoring nutritional status and response to nutritional interventions in ABI; these need to be addressed and hence few recommendations can be made. The optimal frequency and duration of blood glucose monitoring need further study.

Inaccuracy of Venous Point-of-Care Glucose Measurements in Critically Ill Patients: A Cross-Sectional Study

Introduction

Current guidelines and consensus recommend arterial and venous samples as equally acceptable for blood glucose assessment in point-of-care devices, but there is limited evidence to support this recommendation. We evaluated the accuracy of two devices for bedside point-of-care blood glucose measurements using arterial, fingerstick and catheter venous blood samples in ICU patients, and assessed which factors could impair their accuracy.

Methods

145 patients from a 41-bed adult mixed-ICU, in a tertiary care hospital were prospectively enrolled. Fingerstick, central venous (catheter) and arterial blood (indwelling catheter) samples were simultaneously collected, once per patient. Arterial measurements obtained with Precision PCx, and arterial, fingerstick and venous measurements obtained with Accu-chek Advantage II were compared to arterial central lab measurements. Agreement between point-of-care and laboratory measurements were evaluated with Bland-Altman, and multiple linear regression models were used to investigate interference of associated factors.

Results

Mean difference between Accu-chek arterial samples versus central lab was 10.7 mg/dL (95% LA -21.3 to 42.7 mg/dL), and between Precision PCx versus central lab was 18.6 mg/dL (95% LA -12.6 to 49.5 mg/dL). Accu-chek fingerstick versus central lab arterial samples presented a similar bias (10.0 mg/dL) but a wider 95% LA (-31.8 to 51.8 mg/dL). Agreement between venous samples with arterial central lab was the poorest (mean bias 15.1 mg/dL; 95% LA -51.7 to 81.9). Hyperglycemia, low hematocrit, and acidosis were associated with larger differences between arterial and venous blood measurements with the two glucometers and central lab. Vasopressor administration was associated with increased error for fingerstick measurements.

Conclusions

Sampling from central venous catheters should not be used for glycemic control in ICU patients. In addition, reliability of the two evaluated glucometers was insufficient. Error with Accu-chek Advantage II increases mostly with central venous samples. Hyperglycemia, lower hematocrit, acidosis, and vasopressor administration increase measurement error.

Accuracy of bedside glucometry in critically ill children with peripheral hypoperfusion

The accuracy of glucose test strip in critically care has been questioned. We investigated the accuracy of glucose test strip in critically ill children. Patients, aged from 1 month to 18 years admitted in pediatric intensive care unit. Demographic data, hemodynamic parameters, and perfusion index (PI) were recorded. Glucose test strips were performed from finger stick blood [capillary blood glucose (CBG)] and from whole blood [whole blood glucose (WBG)] along with laboratory plasma blood glucose (PBG) from either arterial or venous blood samples. The accuracy of glucose test strips was defined according to ISO 15197 and Clarke error grid (CEG). One hundred and eighty one blood samplings including 117 arterial blood (CBG, WBGa, PBGa) and 64 venous blood (CBG, WBGv, PBGv) were obtained. The accuracy of WBGa was 98.3 and 95.2% when compared to the accuracy of CBG (88.7 and 83.3%. The accuracy of WBGv was 92.2% and 87.0 when compared to the accuracy of CBG which was 79.7 and 72.9% (ISO 15197: 2003 and 2013, respectively). Bland-Altman plot demonstrated bias and precision of 7.4±17.7 mg/dL in acceptable PI group compared to 30.2±23.4 mg/dL in low PI group (PI≤0.3). The CBG test strip must be interpreted carefully in critically ill children. A low PI was associated with poor CBG strip accuracy. WBG test strip from arterial blood was more appropriate for glucose monitoring in children with peripheral hypoperfusion.

Statistical transformation and the interpretation of inpatient glucose control data from the intensive care unit

Glucose control can be problematic in critically ill patients. We evaluated the impact of statistical transformation on interpretation of intensive care unit inpatient glucose control data. Point-of-care blood glucose (POC-BG) data derived from patients in the intensive care unit for 2011 was obtained. Box-Cox transformation of POC-BG measurements was performed, and distribution of data was determined before and after transformation. Different data subsets were used to establish statistical upper and lower control limits. Exponentially weighted moving average (EWMA) control charts constructed from April, October, and November data determined whether out-of-control events could be identified differently in transformed versus nontransformed data. A total of 8679 POC-BG values were analyzed. POC-BG distributions in nontransformed data were skewed but approached normality after transformation. EWMA control charts revealed differences in projected detection of out-of-control events. In April, an out-of-control process resulting in the lower control limit being exceeded was identified at sample 116 in nontransformed data but not in transformed data. October transformed data detected an out-of-control process exceeding the upper control limit at sample 27 that was not detected in nontransformed data. Nontransformed November results remained in control, but transformation identified an out-of-control event less than 10 samples into the observation period. Using statistical methods to assess population-based glucose control in the intensive care unit could alter conclusions about the effectiveness of care processes for managing hyperglycemia. Further study is required to determine whether transformed versus nontransformed data change clinical decisions about the interpretation of care or intervention results.

Improved safety with intravenous insulin therapy for critically ill patients with renal failure

OBJECTIVE

The aim of this study was to evaluate the safety and efficacy of a new intravenous (IV) regular human insulin infusion (RHI) algorithm for glycemic control in critically ill patients with renal failure.

METHODS

Adult trauma patients with renal failure who received a new RHI algorithm were compared with those who received the discontinued RHI algorithm (historical control). Target blood glucose (BG) concentration was 70 to 149 mg/dL (3.9-8.3 mmol/L). Patients were evaluated for 7 d while receiving the RHI infusion and continuous enteral or parenteral nutrition.

RESULTS

Mean BG was higher for the new RHI algorithm group (n = 25) compared with control (n = 21): 145 ± 10 mg/dL or 8.1 ± 0.6 mmol/L versus 133 ± 14 mg/dL or 7.4 ± 0.8 mmol/L (P = 0.001). The new RHI algorithm resulted in less time within the target BG range (11.9 ± 2.5 h/d versus 16.1 ± 3.3 h/d; P = 0.001); however, BGs were within 70 to 179 mg/dL (or 3.9-10 mmol/L) for 16.3 ± 2.6 h/d. The proportion of patients who experienced an episode of moderate hypoglycemia (BG 40-60 mg/dL or 2.2-3.3 mmol/L) or severe hypoglycemia (BG < 40 mg/dL or 2.2 mmol/L) was decreased (32% versus 76%; P = 0.001) and eliminated (0% versus 29%, P = 0.006), respectively.

CONCLUSIONS

The new RHI algorithm improved patient safety by decreasing the prevalence of moderate hypoglycemia and eliminating severe hypoglycemia. The duration of glycemic control within the target BG range was decreased, but acceptable within a higher target BG ceiling.

Software-guided insulin dosing: tight glycemic control and decreased glycemic derangements in critically ill patients

OBJECTIVE

To determine whether glycemic derangements are more effectively controlled using software-guided insulin dosing compared with paper-based protocols.

PATIENTS AND METHODS

We prospectively evaluated consecutive critically ill patients treated in a tertiary hospital surgical intensive care unit (ICU) between January 1 and June 30, 2008, and between January 1 and September 30, 2009. Paper-based protocol insulin dosing was evaluated as a baseline during the first period, followed by software-guided insulin dosing in the second period. We compared glycemic metrics related to hyperglycemia, hypoglycemia, and glycemic variability during the 2 periods.

RESULTS

We treated 110 patients by the paper-based protocol and 87 by the software-guided protocol during the before and after periods, respectively. The mean ICU admission blood glucose (BG) level was higher in patients receiving software-guided intensive insulin than for those receiving paper-based intensive insulin (181 vs 156 mg/dL; P=.003, mean of the per-patient mean). Patients treated with software-guided intensive insulin had lower mean BG levels (117 vs 135 mg/dL; P=.0008), sustained greater time in the desired BG target range (95-135 mg/dL; 68% vs 52%; P=.0001), had less frequent hypoglycemia (percentage of time BG level was <70 mg/dL: 0.51% vs 1.44%; P=.04), and showed decreased glycemic variability (BG level per-patient standard deviation from the mean: ±29 vs ±42 mg/dL; P=.01).

CONCLUSION

Surgical ICU patients whose intensive insulin infusions were managed using the software-guided program achieved tighter glycemic control and fewer glycemic derangements than those managed with the paper-based insulin dosing regimen.

Accuracy of blood-glucose measurements using glucose meters and arterial blood gas analyzers in critically ill adult patients: systematic review

INTRODUCTION

Glucose control to prevent both hyperglycemia and hypoglycemia is important in an intensive care unit. Arterial blood gas analyzers and glucose meters are commonly used to measure blood-glucose concentration in an intensive care unit; however, their accuracies are still unclear.

METHODS

We performed a systematic literature search (January 1, 2001, to August 31, 2012) to find clinical studies comparing blood-glucose values measured with glucose meters and/or arterial blood gas analyzers with those simultaneously measured with a central laboratory machine in critically ill adult patients.

RESULTS

We reviewed 879 articles and found 21 studies in which the accuracy of blood-glucose monitoring by arterial blood gas analyzers and/or glucometers by using central laboratory methods as references was assessed in critically ill adult patients. Of those 21 studies, 11 studies in which International Organization for Standardization criteria, error-grid method, or percentage of values within 20% of the error of a reference were used were selected for evaluation. The accuracy of blood-glucose measurements by arterial blood gas analyzers and glucose meters by using arterial blood was significantly higher than that of measurements with glucose meters by using capillary blood (odds ratios for error: 0.04, P<0.001; and 0.36, P<0.001). The accuracy of blood-glucose measurements with arterial blood gas analyzers tended to be higher than that of measurements with glucose meters by using arterial blood (P=0.20). In the hypoglycemic range (defined as <81 mg/dl), the incidence of errors using these devices was higher than that in the nonhypoglycemic range (odds ratios for error: arterial blood gas analyzers, 1.86, P=0.15; glucose meters with capillary blood, 1.84, P=0.03; glucose meters with arterial blood, 2.33, P=0.02). Unstable hemodynamics (edema and use of a vasopressor) and use of insulin were associated with increased error of blood glucose monitoring with glucose meters.

CONCLUSIONS

Our literature review showed that the accuracy of blood-glucose measurements with arterial blood gas analyzers was significantly higher than that of measurements with glucose meters by using capillary blood and tended to be higher than that of measurements with glucose meters by using arterial blood. These results should be interpreted with caution because of the large variation of accuracy among devices. Because blood-glucose monitoring was less accurate within or near the hypoglycemic range, especially in patients with unstable hemodynamics or receiving insulin infusion, we should be aware that current blood glucose-monitoring technology has not reached a high enough degree of accuracy and reliability to lead to appropriate glucose control in critically ill patients.

Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients

OBJECTIVE

To evaluate the literature and identify important aspects of insulin therapy that facilitate safe and effective infusion therapy for a defined glycemic end point.

METHODS

Where available, the literature was evaluated using Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) methodology to assess the impact of insulin infusions on outcome for general intensive care unit patients and those in specific subsets of neurologic injury, traumatic injury, and cardiovascular surgery. Elements that contribute to safe and effective insulin infusion therapy were determined through literature review and expert opinion. The majority of the literature supporting the use of insulin infusion therapy for critically ill patients lacks adequate strength to support more than weak recommendations, termed suggestions, such that the difference between desirable and undesirable effect of a given intervention is not always clear.

RECOMMENDATIONS

The article is focused on a suggested glycemic control end point such that a blood glucose ≥ 150 mg/dL triggers interventions to maintain blood glucose below that level and absolutely <180 mg/dL. There is a slight reduction in mortality with this treatment end point for general intensive care unit patients and reductions in morbidity for perioperative patients, postoperative cardiac surgery patients, post-traumatic injury patients, and neurologic injury patients. We suggest that the insulin regimen and monitoring system be designed to avoid and detect hypoglycemia (blood glucose ≤ 70 mg/dL) and to minimize glycemic variability.Important processes of care for insulin therapy include use of a reliable insulin infusion protocol, frequent blood glucose monitoring, and avoidance of finger-stick glucose testing through the use of arterial or venous glucose samples. The essential components of an insulin infusion system include use of a validated insulin titration program, availability of appropriate staffing resources, accurate monitoring technology, and standardized approaches to infusion preparation, provision of consistent carbohydrate calories and nutritional support, and dextrose replacement for hypoglycemia prevention and treatment. Quality improvement of glycemic management programs should include analysis of hypoglycemia rates, run charts of glucose values <150 and 180 mg/dL. The literature is inadequate to support recommendations regarding glycemic control in pediatric patients.

CONCLUSIONS

While the benefits of tight glycemic control have not been definitive, there are patients who will receive insulin infusion therapy, and the suggestions in this article provide the structure for safe and effective use of this therapy.

Transitional NPH insulin therapy for critically ill patients receiving continuous enteral nutrition and intravenous regular human insulin

BACKGROUND

The intent of this study was to evaluate the efficacy and safety of transitioning from a continuous intravenous (IV) regular human insulin (RHI) or intermittent IV RHI therapy to subcutaneous neutral protamine Hagedorn (NPH) insulin with intermittent corrective IV RHI for critically ill patients receiving continuous enteral nutrition (EN).

METHODS

Data were obtained from critically ill trauma patients receiving continuous EN during transitional NPH insulin therapy. Target blood glucose concentration (BG) range was 70-149 mg/dL. BG was determined every 1-4 hours.

RESULTS

Thirty-two patients were transitioned from a continuous IV RHI infusion (CIT) to NPH with intermittent corrective IV RHI therapy. Thirty-four patients had NPH added to their preexisting supplemental intermittent IV RHI therapy (SIT). BG concentrations were maintained in the target range for 18 ± 3 and 15 ± 4 h/d for the CIT and SIT groups, respectively (P < .05). Thirty-eight percent of patients experienced a BG <60 mg/dL, and 9% had a BG <40 mg/dL. Hypoglycemia was more prevalent for those who were older (P < .01) or exhibited greater daily BG variability (P < .01) or worse HgbA1C (p < 0.05).

CONCLUSION

Transitional NPH therapy with intermittent corrective IV RHI was effective for achieving BG concentrations within 70-149 mg/dL for the majority of the day. NPH therapy should be implemented with caution for those who are older, have erratic daily BG control, or have poor preadmission glycemic control.

Assessing inpatient glycemic control: what are the next steps?

Despite the emergence of glucometrics (i.e., systematic analysis of data on blood glucose levels of inpatients) as a subject of high interest, there remains a lack of standardization on how glucose parameters are measured and reported. This dilemma must be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines and that can also be supported by quality care organizations. In this article, we review some of the questions that need to be resolved through consensus and review of the evidence, and discuss some of the limitations in analyzing and reporting inpatient glucose data that must be addressed (or at least accepted as limitations) before hospitals can commit resources to gathering, compiling, and presenting inpatient glucose statistics as a health care quality measure. Standards must include consensus on which measures to report, the unit of analysis, definitions of targets for hyperglycemia treatment, a definition of hypoglycemia, determination of how data should be gathered (from chart review or from laboratory information systems), and which type of sample (blood or point of care) should be used for analysis of glycemic control. Hospitals and/or their representatives should be included in the discussion. For inpatient glucose control to remain a focus of interest, further dialogue and consensus on the topic are needed.

Accuracy of point-of-care glucose measurements

Control of blood glucose (BG) in an acceptable range is a major therapy target for diabetes patients in both the hospital and outpatient environments. This review focuses on the state of point-of-care (POC) glucose monitoring and the accuracy of the measurement devices. The accuracy of the POC glucose monitor depends on device methodology and other factors, including sample source and collection and patient characteristics. Patient parameters capable of influencing measurements include variations in pH, blood oxygen, hematocrit, changes in microcirculation, and vasopressor therapy. These elements alone or when combined can significantly impact BG measurement accuracy with POC glucose monitoring devices (POCGMDs). In general, currently available POCGMDs exhibit the greatest accuracy within the range of physiological glucose levels but become less reliable at the lower and higher ranges of BG levels. This issue raises serious safety concerns and the importance of understanding the limitations of POCGMDs. This review will discuss potential interferences and shortcomings of the current POCGMDs and stress when these may impact the reliability of POCGMDs for clinical decision-making.

Mapping point-of-care performance using locally-smoothed median and maximum absolute difference curves

BACKGROUND

The goal is to introduce visual performance mapping efficient for establishing acceptance criteria and facilitating decisions regarding the utility of hospital point-of-care devices. This approach uniquely reveals the quality of performance locally, as opposed to globally.

METHODS

After presenting theoretical foundations, this study illustrates the approach by applying it to six hospital glucose meter systems (GMSs) using clinical multi-center (n=2767) and multi-system (n=613, n=100) observations.

RESULTS

LS MAD curves identified breakouts, that is, points where the locally-smoothed median absolute difference (LS MAD) curve exceeds the recommended error tolerance limit of 5 mg/dL (0.28 mmol/L). LS maximum absolute difference (MaxAD) breakthroughs, which occur where the LS MaxAD curve exceeds the 99th percentile of MaxADs from x=30-200 mg/dL (1.67-11.10 mmol/L), showed extreme error locations. A multi-sensor interference- and hematocrirt-correcting GMS displayed a flat LS MAD curve until it reached a breakout of 179 mg/dL (9.94 mmol/L) and generated breakthroughs that could affect bedside decision-making, but less erratically than other systems with inadequate performance for hospital critical care. We discovered Class I (meter high, reference low) and Class II (converse) discrepant values in some systems. Class I errors could lead to inappropriate insulin dosing and hypoglycemic episodes in tight glucose control.

CONCLUSIONS

LS MAD-MaxAD curves help assess the performance of point-of-care testing. Visual mapping of systematic and random errors locally over the entire analyte measurement range in a single integrated display is an advantage when considering the adverse impact of zones of poor quantitative performance on specific clinical applications, threshold-driven bedside decisions and the care of critically ill patients.

The effects of precision, haematocrit, pH and oxygen tension on point-of-care glucose measurement in critically ill patients: a prospective study

BACKGROUND

Critical care glycaemic control protocols commonly have treatment adjustment (target) ranges spanning ≤2 mmol/L. These require precise point-of-care glucose measurement, unaffected by other variables, to avoid measurement errors increasing glycaemic variability and hypoglycaemic episodes (both strongly associated with mortality in critically ill patients).

METHODS

A prospective 206 intensive care patient study was carried out. Arterial glucose concentrations were measured in duplicate using three point-of-care instruments (MediSense Precision PCχ, HemoCue DM and Radiometer 700), a central laboratory instrument (Siemens ADVIA), and in whole blood and plasma using the Yellow Springs Instruments 2300 instrument.

RESULTS

Coefficients of variation for the MediSense, HemoCue, Radiometer and Siemens instruments were 5.1%, 2.5%, 2.1% and 2.3%, respectively. Compared with the Siemens instrument, the bias (95% limits of agreement) for the MediSense, HemoCue and Radiometer instruments were 0.0 (-1.4 to 1.4), 0.0 (-1.2 to 1.1) and -0.2 (-0.9 to 0.6) mmol/L, respectively. The whole blood-plasma glucose concentration difference was significantly affected by the haematocrit. MediSense and HemoCue instrument performances were substantially affected by haematocrit. MediSense instrument performance was also affected by pH and PaO(2). Radiometer instrument performance was not affected by haematocrit, pH or PaO(2).

CONCLUSIONS

The MediSense instrument was too imprecise for use in critically ill patients. The haematocrit range seen is too great to allow fixed-factor conversion between whole blood and plasma values, substantially affecting the accuracy of both glucose meters. However, the Radiometer instrument was unaffected by the haematocrit, pH or pO(2), resulting in a performance equivalent to the laboratory method. Instrument performance differences may therefore partially explain the differing results of tight glycaemic control therapy trials.

Blood glucose measurements in critically ill patients

Studies on tight glycemic control by intensive insulin therapy abruptly changed the climate of limited interest in the problem of hyperglycemia in critically ill patients and reopened the discussion on accuracy and reliability of glucose sensor devices. This article describes important components of blood glucose measurements and their interferences with the focus on the intensive care unit setting. Typical methodologies, organized from analytical accuracy to clinical accuracy, to assess imprecision and bias of a glucose sensor are also discussed. Finally, a list of recommendations and requirements to be considered when evaluating (time-discrete) glucose sensor devices is given.

Hypoglycemia at admission is associated with inhospital mortality in Ugandan patients with severe sepsis

OBJECTIVE

Dysglycemia during sepsis is associated with poor outcomes in resource-rich settings. In resource-limited settings, hypoglycemia is often diagnosed clinically without the benefit of laboratory support. We studied the utility of point-of-care glucose monitoring to predict mortality in severely septic patients in Uganda.

DESIGN

Prospective observational study.

SETTING

One national and two regional referral hospitals in Uganda.

PATIENTS

We enrolled 532 patients with sepsis at three hospitals in Uganda. The analysis included 418 patients from the three sites with inhospital mortality data, a documented admission blood glucose concentration, and evidence of organ dysfunction at admission (systolic blood pressure≤100 mm Hg, lactate>4 mmol/L, platelet number<100,000/μL, or altered mental status).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We evaluated the association between admission point-of-care blood glucose concentration and inhospital mortality. We also assessed the accuracy of altered mental status as a predictor of hypoglycemia. Euglycemia occurred in 33.5% (140 of 418) of patients, whereas 16.3% (68 of 418) of patients were hypoglycemic and 50.2% (210 of 418) were hyperglycemic. Univariate Cox regression analyses comparing in-hospital mortality among hypoglycemic (35.3% [24 of 68], hazard ratio 2.0, 95% confidence interval 1.2-3.6, p=.013) and hyperglycemic (29.5% [62 of 210], hazard ratio 1.5, 95% confidence interval 0.96-2.4, p=.08) patients to euglycemic (19.3% [27 of 140]) patients showed statistically significantly higher rates of inhospital mortality for patients with hypoglycemia. Hypoglycemia (adjusted hazard ratio 1.9, 95% confidence interval 1.1-3.3, p=.03) remained significantly and independently associated with inhospital mortality in the multivariate model. The sensitivity and specificity of altered mental status for hypoglycemia were 25% and 86%, respectively.

CONCLUSION

Hypoglycemia is an independent risk factor for inhospital mortality in patients with severe sepsis and cannot be adequately assessed by clinical examination. Correction of hypoglycemia may improve outcomes of critically ill patients in resource-limited settings.

Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus

BACKGROUND

Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these tests varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for the use of laboratory testing for patients with diabetes. A new system was developed to grade the overall quality of the evidence and the strength of the recommendations. Draft guidelines were posted on the Internet and presented at the 2007 Arnold O. Beckman Conference. The document was modified in response to oral and written comments, and a revised draft was posted in 2010 and again modified in response to written comments. The National Academy of Clinical Biochemistry and the Evidence-Based Laboratory Medicine Committee of the American Association for Clinical Chemistry jointly reviewed the guidelines, which were accepted after revisions by the Professional Practice Committee and subsequently approved by the Executive Committee of the American Diabetes Association.

CONTENT

In addition to long-standing criteria based on measurement of plasma glucose, diabetes can be diagnosed by demonstrating increased blood hemoglobin A(1c) (HbA(1c)) concentrations. Monitoring of glycemic control is performed by self-monitoring of plasma or blood glucose with meters and by laboratory analysis of HbA(1c). The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of autoantibodies, urine albumin, insulin, proinsulin, C-peptide, and other analytes are addressed.

SUMMARY

The guidelines provide specific recommendations that are based on published data or derived from expert consensus. Several analytes have minimal clinical value at present, and their measurement is not recommended.

Increased hypoglycemia associated with renal failure during continuous intravenous insulin infusion and specialized nutritional support

OBJECTIVE

To evaluate glycemic control for critically ill, hyperglycemic trauma patients with renal failure who received concurrent intensive insulin therapy and continuous enteral nutrition (EN) or parenteral nutrition (PN).

METHODS

Adult trauma patients with renal failure who were given EN or PN concurrently with continuous graduated intravenous regular human insulin (RHI) infusion for at least 3 d were evaluated. Our conventional RHI algorithm was modified for those with renal failure by allowing greater changes in blood glucose (BG) concentrations before the infusion rate was escalated. BG concentration was determined every 1 to 2 h while receiving the insulin infusion. BG control was evaluated on the day before RHI infusion and for a maximum of 7 d while receiving RHI. Target BG during the RHI infusion was 70 to 149 mg/dL (3.9 to 8.3 mmol/L). Glycemic control and incidence of hypoglycemia for those with renal failure were compared with a historical cohort of critically ill, hyperglycemic trauma patients without renal failure given our conventional RHI algorithm.

RESULTS

Twenty-one patients with renal failure who received the modified RHI algorithm were evaluated and compared with 40 patients without renal failure given our conventional RHI algorithm. Average BG concentration was significantly greater for those with renal failure (133±14 mg/dL or 7.3±0.7 mmol/L) compared with those without renal failure (122±15 mg/dL or 6.8±0.8 mmol/L), respectively (P<0.01). Patients with renal failure showed worsened glycemic variability, with 16.1±3.3 h/d within the target BG range, 6.9±3.2 h/d above the target BG range, and 1.4±1.1 h/d below the target BG range compared with 19.6±4.7 h/d (P<0.001), 3.4±3.0 h/d (P<0.001), and 0.7±0.8 h/d (P<0.01) for those without renal failure, respectively. Moderate hypoglycemia (<60 mg/dL or<3.3 mmol/L) occurred in 76% of patients with renal failure compared with 35% without renal failure (P<0.005). Severe hypoglycemia (BG<40 mg/dL or<2.2 mmol/L) occurred in 29% of patients with renal failure compared with none of those without renal failure (P<0.001).

CONCLUSION

Despite receiving a modified RHI infusion, critically ill trauma patients with renal failure are at greater risk for developing hypoglycemia and have more glycemic variability than patients without renal failure.

Analytical and clinical performance of blood glucose monitors

BACKGROUND

The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature.

METHODS

Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors.

RESULTS

Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed.

CONCLUSIONS

When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.