Van den Berghe et al. (1) reported a significant reduction in mortality with normoglycemia (target value 80–110 mg/dl) in patients whose medical intensive care unit (ICU) stay was >72 h and reduced morbidity in all patients, regardless of the duration of ICU stay. Although severe hypoglycemia did not occur in the Van den Berghe et al. study, 18.7% of patients in the intensive treatment group compared with 3.1% of those who received conventional therapy did experience hypoglycemia (defined as glucose <40 mg/dl), albeit with no adverse consequences reported. However, altered consciousness is common in the ICU, and even severe hypoglycemia may be unrecognized. Other studies (2,3) examining intensive insulin protocols in various inpatient settings have suggested benefits in clinical outcomes associated with improved glycemic control. In a mixed ICU population, Van den Berghe et al. (2) previously demonstrated reduced morbidity and mortality with three- to fourfold less hypoglycemia than the medical ICU population (2). Thus, careful assessment of glucose measurement and how it may impact the targets selected in the hospital are critical safety issues in intensive management of hyperglycemia. As a result of increasing evidence that tight glycemic control is beneficial in the management of inpatients with diabetes, the American Diabetes Association (ADA) currently recommends a glucose target “as close to 110 mg/dl as possible and generally <180 mg/dl” for critically ill patients (4). The American Association of Clinical Endocrinologists recommends the “upper limits for glycemic targets” of 110 mg/dl in critically ill patients (5).

In practice, it may be difficult to obtain the level of glycemic control (average glucose 111 mg/dl in the intensively managed group) achieved by Van den Berghe et al. Though a wider range of glucose values has been targeted, rarely have mean glucose values between 80 and 110 mg/dl been achieved, particularly in those studies involving patients with diabetes (6). In many hospitals, samples for laboratory glucose determination are obtained from either venous or arterial sites to determine serum or plasma glucose. These laboratory values are generally obtained less frequently than bedside capillary glucose values using point-of-care (POC) systems that report whole-blood glucose or plasma glucose values. In the Van den Berghe et al. study, a HemoCue B glucose analyzer was used to report the values of arterial whole-blood glucose.

Variability is introduced into the reporting of glucose values because of patient variables and also because of differences between assays (Table 1). Patient variables may include issues of physiology and interfering substances. These variables may be of importance when there are unexpected laboratory results. Among institutional variables, there are differences between assay characteristics, performance of commercial products, the source of the sample, and specimen matrix (i.e., plasma versus whole blood). This study will review assay principles, patient variables, and systematic variables and then encourage clinicians to carefully consider how standard recommendations regarding glycemic targets, particularly in the ICU, should be implemented in their individual health care facilities.

In this review, we will signify reference laboratory methods with the term “central laboratory method.” “POC” refers to hand-held devices or portable ward-based analyzers. We recognize that some of these devices are also used in the ambulatory setting. “Plasma correlated” refers to glucose concentrations measured in samples of whole blood but are converted to values that would be expected of plasma measurements.

Enzymatic reaction

Glucose measurements are based on one of three enzymes: glucose oxidase, glucose-1-dehydrogenase (GD), or hexokinase (7). For POC devices, glucose oxidase is the classic methodology. Glucose oxidase requires oxygen and water and is therefore susceptible to extremes of hydration or oxygenation. Glucose oxidase–catalyzed reactions result in the production of gluconic acid and hydrogen peroxide, the latter of which is detected by various means. GD, like glucose oxidase, is specific for β-d-glucose but may have less interference than glucose oxidase–based techniques. Hexokinase, the basis for many central laboratory methods, phosphorylates d-glucose to form glucose-6-phosphate, which is then oxidized with concurrent reduction of NAD to NADH.

Detection method

The enzymatic reaction is either colorimetrically or amperometrically detected. Colorimetric detection is available for techniques using glucose oxidase, in which H2O2 reacts with various hydrogen donors to produce a color change that is proportional to the glucose concentration. Most POC colorimetric reactions are measured using a reflectance photometer that converts the reflected light to an electronic signal for digital display. Amperometric detection is available for either glucose oxidase–or GD-based POC devices, in which the electrical current produced from the reaction is directly mearsured. In the case of hexokinase, NADH reacts with the dye to produce the color change.

POC techniques

POC devices typically use test strips (biosensors) with a porous layer that separates blood cells from the enzyme-impregnated reagent layer (7). In general, biosensor technology is less precise and less accurate than the wet chemistry methods used in most central laboratory methods. Blood gas analyzers are often used at the bedside and generally use wet chemistry techniques that more closely approximate central laboratory methods (8).

A notable exception to this biosensor technology is the HemoCue B analyzer used in the Van den Berghe et al. studies, a POC method that measures glucose via GD using a disposable microcuvette instead of a traditional biosensor (9). The HemoCue B Glucose Analyzer (HemoCue AB, Angelholm, Sweden) measures glucose via absorbance of reaction products at unique wavelengths. The method allows colorimetric measurement from a whole-blood sample.

Interstitial fluid glucose monitoring

Other investigators have focused on continuous interstitial fluid glucose measurements in order to simplify the need for frequent capillary sampling (10). However, the measurement of glucose in interstitial fluid is complex and affected by tissue perfusion, temperature, and local humoral factors (11). A detailed discussion of this technology is beyond the scope of this review.

Patient factors

Hypotension.

In the ICU, multiple variables that may affect bedside glucose measurements may be present all at once. In particular, hypotension may result in a reduction of perfusion and an increase in glucose utilization, potentially obscuring the true result for capillary whole-blood samples. A GD-based POC device demonstrated that in 31 hypotensive patients (systolic blood pressure <90 mg/dl), capillary whole-blood values differed from the central laboratory venous plasma glucose to a greater extent than those of normal control subjects (−61.7 ± 12.4 vs. −14.1 ± 2.0 mg/dl, P < 0.001) (12). Sixty-four percent of values fell outside the acceptable range of 20% compared with 10% of the control group. On the other hand, venous samples measured with the POC meter correlated well with the central laboratory method. A glucose oxidase methodology fared no better in 38 patients with shock (13). Capillary whole-blood glucose was significantly higher than the venous plasma glucose determined by the central laboratory method (mean difference 77 mg/dl, P = 0.04), but venous whole-blood glucose on the POC device was no different (13). In addition, 31.6% of the capillary glucose measurements were outside of the allowable 20% variance. Other studies (8,14) that did not show an effect were limited by sample size. More recently, Kulkarni et al. (15) reported that in cases of hypoperfusion, the accuracy of agreement between an arterial blood gas POC method and GD-based POC capillary glucose readings may still result in undetected hypoglycemia when a lower limit of 80 mg/dl is targeted. This occurs despite what would otherwise be considered low bias (4.0 mg/dl) and imprecision (16.2 mg/dl).

Hematocrit.

In general, increases in hematocrit are known to decrease glucose measurements and vice versa. Although manufacturers set acceptable testing limits for hematocrit, POC devices do not exclude samples by hematocrit, and hematocrit is not always known at the time of testing. Proposed mechanisms include mechanical impedance of plasma diffusion into the reagent layer of the strip at higher hematocrit and increased relative plasma volume at higher viscosity, resulting in slower diffusion of glucose (16). The net result would potentially mask hypoglycemia in patients with anemia and underestimate glucose in patients with polycythemia. A POC glucose meter that measures and automatically corrects for hematocrit was recently described and had less error than other devices (17).

An in vitro study examined the effects of hematocrit on six different POC glucose meters (18). At low hematocrit, most POC systems yielded a higher glucose result (5–15%) relative to venous plasma, and the opposite was true at higher hematocrit (−10 to 30%), with the exception of amperometric glucose oxidase methods, which yielded lower values at all three hematocrit levels.

Differences have been observed in clinical studies as well (19). Surgical patients may be most at risk for errors in glucose measurement as a result of fluctuations in hematocrit (2022).

The HemoCue system, which determines glucose concentration on lysed whole blood instead of measurement based on membrane separation of plasma from red cells, does not show significant hematocrit dependence (23). However, this GD-based POC system has been shown to falsely produce decreased glucose values in patients with methemoglobin values >10% (24).

Oxygenation.

High oxygen tension, i.e., pO2 >100 mmHg, can falsely lower glucose readings on some glucose oxidase–based POC instruments, particularly in patients on oxygen therapy. Oxygen levels as high as 400 mmHg may be seen with surgical patients, particularly those undergoing cardiopulmonary bypass (25). Conversely, higher altitudes overestimate glucose readings by 15% with glucose oxidase methods (26). As might be expected, the effect is largest in arterial blood and smallest in venous blood, but there is little data on the effect of pO2 on capillary whole blood (27).

Tang et al. (28) evaluated six POC glucose meter systems with respect to effects of oxygenation using venous whole blood and venous plasma. Measurements at pO2 >100 mmHg were outside of error tolerances (15 mg/dl for glucose <100 mg/dl or 15% for glucose >100 mg/dl) 14.3–31.6% of the time. Overall, lower oxygen tension (40 mmHg) had a negligible effect. An older study reported errors at lower pO2 (29).

Kurahashi et al. (30) found that arterial whole blood from surgical patients using an amperometric glucose oxidase–based POC meter underestimated glucose by 39 mg/dl. Similar results were reported elsewhere with some glucose oxidase–but not GD-based POC devices in mixed hospital patients (19,31).

pH.

As with any enzymatic reaction, changes in pH may affect the performance of the POC meter. This has not been shown to be a major source of error at a pH range of 6.97–7.84 (32) or at lower pH (6.8–7.55) (31). However, Kilpatrick et al. (29) found significant deviation in glucose measurement at pH <6.95 and >7.85, with >15% from the central laboratory whole-blood method using an older POC method. Nonetheless, this may be cause for concern in cases of severe acidosis (e.g., diabetic ketoacidosis), or where other factors may contribute, leading to clinically significant interpretation errors.

Temperature.

Some data suggest that cold temperatures may produce discrepant results (26,33). Active warming may improve measurements; conversely, the effects of fever are unknown.

Interfering substances

The majority of substances that interfere with glucose oxidase–based POC devices do so at the peroxide reduction detection step and not at the level of the enzyme itself (which is very specific for β-d-glucose). Table 1 lists some examples. In the case of the photometric strips, reducing agents such as acetaminophen and ascorbic acid may consume peroxide and diminish its reaction with the dye, thus resulting in lower readings (34). Newer amperometric POC devices have attempted to compensate for this by introducing a third electrode that reduces background current (34). Devices that use GD as the catalyst tend to have less interference but may occasionally falsely increase POC readings through direct oxidation at the electrode (34). Blood gas analyzers may also give more accurate POC results in patients with possible drug interferences (35).

Drugs.

Tang et al. (34) examined the effects of therapeutic and toxic concentrations of 30 different drugs on glucose readings from six different POC glucose meters. In this study, a comparatively low error threshold of ±6 mg/dl was used. Interferences were found for ascorbic acid, acetaminophen, dopamine, and mannitol. At high doses, ascorbic acid increased GD-based POC readings but decreased those that used glucose oxidase (34). False low glucose readings were reported with other glucose oxidase–based POC devices (36,37) but not with testing based on hexokinase or other GD-based methods (36).

Acetaminophen increased POC glucose readings with GD meters but decreased readings with some, but not all, glucose oxidase–based meters at therapeutic drug levels (34). This may be particularly problematic in overdose patients, in whom hypoglycemia may develop in the presence of hepatic failure. Other reports (36,38) had similar findings, and there may be a reduction in glucose measurements in patients given only 1.5–2 g acetaminophen (39).

Dopamine increased glucose values on GD-based POC systems, primarily at high drug concentrations (34,40). Mannitol increased glucose oxidase–based POC readings, possibly through detection by the analyzer or by a nonspecific osmotic effect (34,35). Finally, interferences with salicylates (36) and nitroprusside (41) have been described in past literature but not more recently (34).

Other substances.

Most GD-based POC devices display large overestimations of glucose in patients undergoing peritoneal dialysis using icodextrin as an osmotic agent (4244). Icodextrin is metabolized to maltose and is indistinguishable from glucose on GD-based POC devices. A similar mechanism of interference prompted U.S. Food and Drug Administration warnings for intravenous immunoglobulin solutions (45). Skin preparations have been reported to interfere (46). Other patient factors, such as bilirubin (9,47), triglycerides (9,47), and paraproteinemias (4851), may also cause “pseudohypoglycemia.”

When the method of measurement of circulating glucose differs between institutions, the absolute values and variability of glucose measurements will systematically differ. These systematic differences have implications for the appropriate glucose targets and algorithms of care developed on the basis of demonstrated risks and benefits of interventions in published studies; appropriate targets in one site with one methodology may not be generalizeable.

Standards for comparison

Much of the difficulty with assessing the performance of POC glucose measuring devices lies in the lack of consensus among professional and regulatory groups regarding allowable error (5255). As a result, published studies are often difficult to directly compare. Of these, the ADA guidelines established in 1996 are the most stringent, calling for total error (bias plus imprecision) of <10% for current devices and <5% for future devices (55). Error grids have been used in an attempt to predict clinically important errors; however, they are comparatively inaccurate (56).

Standards do not specify differences for POC devices that are intended for hospital use versus those meters intended for home use. Despite a strong correlation between capillary whole-blood glucose and central laboratory methods in an ICU population as a whole, bedside POC devices may be unreliable for use in the individual patient in the ICU (15). A simulation modeling study showed that for glucose meters that achieve both coefficient of variation (CV) and bias ≤5–6% (total <14%), major errors in insulin dosing are rare, but up to 23% of measurements would result in small errors (57). Therefore, it would seem that the ADA guidelines should serve as the minimum proficiency standard in the hospital.

Performance of POC devices

Over the past decade, POC devices for measuring glucose have become more user friendly, resulting in greater accuracy (58). In hospital patients, recent studies report 91–100% accuracy of various POC devices (30,31). Although the accuracy may have significantly improved in published studies under controlled conditions, this may not be true in the typical clinical setting, particularly among hospitalized patients. The latest College of American Pathologists (CAP) proficiency results demonstrate large CVs for mean glucose values obtained from all POC instruments at all institutions combined (59). At glucose levels of 120–170 mg/dl (mean 143.8 mg/dl), the overall interlaboratory CV is 15.1%; in the hypoglycemic range, the CV is 31.9% (26.3–66.6 mg/dl, mean 45.7 mg/dl). This variability is at least in part due to differences between instruments because CVs for individual instruments are lower, ranging from 3.9 to 10.9% in the mid-100 range and 6.2 to 13.3% in the hypoglycemic range. Depending on the type of device used, the mean glucose measurement for a particular unknown test sample reported by an institution varies by >30% at glucose levels >150 mg/dl and by 60% in the hypoglycemic range. In comparison, interlaboratory CVs for various central laboratory methods are uniformly <5%. The variability among POC devices may be due to analytical differences in instruments or due to user interfaces that are more susceptible to operator error. For an institution to be considered proficient, results should deviate by no more than 12 mg/dl or 20% from the peer group mean, but this may be inadequate as institutions aim to establish tighter glycemic control using recent standardized guidelines of inpatient management.

Operator error

Unfortunately, operator error is incompletely captured with CAP data, as well as with studies that evaluate POC devices based on aqueous controls, venous samples, or prepared blood samples (60,61). However, the potential for operator error still exists and remains the largest source of error (up to 91–97%) overall (46,6264). Sources of error such as differences between lots of test strips (up to 14.5 mg/dl) in some (28,65) but not all (19,31) studies may be unrecognized. It is advisable to regularly test split-sample controls referenced to the central laboratory method to detect both performer error and instrument accuracy (62). Quality control may be particularly challenging in ICU and surgical patients (62,63). Programs that use training, quality control procedures combined with national interhospital proficiency surveys, and newer technology have produced significant improvements in precision (62,66,67).

Source of sample

Differences in measurements among blood sources (i.e., arterial, capillary, or venous) may be attributable to variations in glucose extraction by tissues, perfusion, oxygenation, pH, feeding, and temperature (see patient factors above), as well as theoretically neurovascular function (68). It has been suggested that on average, arterial glucose concentrations at normal pO2 are 5 mg/dl higher than capillary blood and ∼10 mg/dl greater than venous concentrations (69). In recent studies, assessments are limited due to a lack of data comparing all sources of blood, particularly arterial versus venous blood.

Arterial samples compared with capillary samples.

Some ICU studies using arterial samples measured with the POC device show acceptable agreement with capillary blood (70,71). A recent abstract found that with newer POC devices in ICU patients, arterial samples had greater accuracy than capillary whole-blood compared with the central whole-blood method (72). However, an older GD-based POC device reported no greater accuracy with arterial whole blood than with capillary whole blood in 50 postcardiothoracic surgery patients, resulting in potential errors of insulin dosing in 31 of 50 patients (20). Using a plasma-correlated glucose oxidase method in 30 critically ill patients, arterial measurements were 8.8 ± 17.8% higher, and capillary measurements were 3.6 ± 15% higher on the POC meter than on the arterial plasma central laboratory method (14). On error grid analysis, only 88% of arterial and 73% of capillary readings fell within target range using the POC meter. Arterial blood gas analysis performed better than the POC device (14).

Venous samples compared with capillary samples.

A POC GD device in 31 patients with diabetes reported venous whole-blood measurements exceeding capillary whole blood by 9.6% (72). In mixed hospital patients (31) and hypotensive patients (12,13), venous whole blood measured on POC devices was found to be superior to capillary whole blood on the same device, with the exception of one study (73). However, in a recent study (74) using a POC GD-based method, glucose measured from the same site showed better agreement with the central laboratory (POC venous whole blood vs. central laboratory venous plasma, R2 = 0.83) than glucose measured from different sites (POC capillary whole blood vs. central laboratory venous plasma, R2 = 0.55). The authors argue that anatomical site is more important in determining glucose values than specimen matrix.

Postprandial state.

Differences between sources of blood may be amplified in the postprandial state (72,7577). During periods of fasting, capillary glucose may be only slightly (2–5 mg/dl) higher than venous plasma glucose. After a glucose load, however, capillary glucose values may be 20–25% higher than venous plasma values (75). Conversely, hyperglycemia may be misdiagnosed in blood samples drawn from intravenous lines carrying dextrose.

Differences between plasma and whole blood (specimen matrix)

The difference between plasma and whole blood is the most important variable that clinicians must consider when setting targets for inpatient glucose measurement. These differences are a consequence of variables in specimen matrix, including water content, lipid and protein concentrations, and cellular elements (see patient factors). Although the glucose concentration in the water that makes up plasma is equal to that of erythrocytes, plasma has greater water content than erythrocytes and therefore exhibits higher glucose levels than whole blood (78). The World Health Organization uses a conversion factor of 1.12 that has been mathematically derived assuming a hematocrit of 45% and a red cell–to–plasma water ratio of ∼0.80 (79). The conversion factor is less appropriate in patients with severe perturbations in hydration, osmolarity, or hemoglobin. In general, manufacturer specifications describe limitations in methodologies under these conditions, but the clinician must be aware that POC devices are not capable of excluding such samples. Furthermore, based on simple regression analyses, the conversion between plasma and whole blood is dependent on the glucose level itself and may vary considerably at extremes of glucose measurement (76,80). Whole blood may be tested with the POC meter but converted to equivalent plasma glucose values obtained from donor blood samples supplemented with glucose; therefore, measurements of plasma samples are inaccurate on such devices (81). On the other hand, meters may attempt to approximate plasma glucose directly via ultrafiltration of erythrocytes from samples with the use of a specialized porous membrane (74). Finally, some POC devices have the capability of reporting values as whole-blood or plasma equivalents, and this is not always specified in studies (82).

Arterial whole blood compared with arterial plasma.

Limited data exists for this important comparison. The conversion of arterial whole-blood glucose to plasma-correlated results may not be valid using POC measurements in cardiothoracic surgery patients (20). A glucose oxidase–based device in 10 ICU patients found only a small difference (0.76 mg/dl) between POC arterial whole-blood values compared with the arterial plasma central laboratory method, but wide CIs negate this finding (14).

Venous whole blood compared with venous plasma.

Using four amperometric and two colorimetric glucose oxidase–based devices in 31 patients with diabetes, Kuwa et al. (72) found that venous whole blood measured with the central laboratory method was 11.3% less than venous plasma measured with the central laboratory method. A 13% difference was reported in 126 healthy volunteers (81).

Capillary whole blood compared with venous plasma.

In recent studies, variable results from POC devices are in part attributable to manufacturers’ efforts to convert results of measurements made on samples of whole blood to plasma-correlated values (72). In the Kuwa et al. (72) study, the mean capillary whole-blood glucose measurements from several POC devices combined was actually 3.2% higher than venous plasma glucose determined by the central laboratory method (contrary to the expected relationship that would be created by the difference in matrix but consistent with the difference that would be created by site of sampling). Other studies using plasma-correlated POC devices in ICU (83) and mixed hospital (80) patients also showed similar results. Therefore, the site of sampling may outweigh the importance of matrix in determining systematic differences. Conversely, the HemoCue B glucose meter (which reports whole-blood glucose) produced results that were contrary to expectation based on site of sampling but were consistent with expectation based on the matrix (77).

Ramifications for the clinician

Unfortunately, studies that directly compare plasma and whole-blood glucose measurements from all sources (arterial, capillary, and venous) are lacking. However, it should be assumed that under physiologic conditions, glucose measurement determined from arterial sites generally exceeds that of capillary sites, which, in turn, is greater than venous sites. Glucose from plasma generally exceeds that of whole blood. In 2001, the International Federation of Clinical Chemistry recommended that glucose meters be calibrated to plasma glucose, using a constant factor of 1.11 (78). In fact, most, but not all, meters today are calibrated to report plasma glucose values. A notable exception is the HemoCue B glucose analyzer used in the Van den Berghe et al. studies, which reports whole-blood values. Based on CAP data, most hospitals do use plasma-correlated methods. Therefore, it is imperative that hospitals using these devices set targets that reflect plasma glucose rather than whole-blood glucose. Failure to do so may result in more significant hypoglycemia than was reported in the Van den Berghe et al. data.

Manufacturers have improved the accuracy of glucose measurement with many (84) but not all (85) newer generation devices, mainly through improvements in user interfaces that reduce operator error. However, for individuals in the hospital, variables that are unique to the patient must be considered, particularly in situations where discrepancies arise between the bedside measurement and the clinical scenario. Nowhere else is there greater potential for multiple confounding factors to be present at once than in the hospital setting. Furthermore, the accuracy of POC devices may not be sufficient to achieve tight glycemic control in hospital patients, and studies are not standardized in methods of glucose measurement, despite well-characterized differences in specimen source and matrix. Unfortunately, the unacceptable time delay imposed by central reference laboratory measurements mandates the use of POC in the ICU. Accurate, well-validated blood sensors, particularly those that provide continuous readings, are sorely needed. In the meantime, providers should use caution when selecting patients for monitoring glucose with the use of bedside monitors. If the whole-blood glucose targets of the Van den Berghe et al. study (80–110 mg/dl) are to be applied to venous plasma-correlated values used in many hospitals, a more appropriate target range might be 90–120 mg/dl. Targets should be individualized in each institution and in each setting based on the methodology of glucose testing and the needs of a given patient population to reflect, at a minimum, the 1.11 whole blood–to–plasma glucose conversion factor recommended by the International Federation of Clinical Chemistry.

Table 1—

Confounding variables in glucose measurement

VariableMethodology affected*
GOGD
Whole blood ↓ ↓ 
Arterial ↑ ↑ 
Capillary ↑ ↑ 
Postprandial state ↑ ↑ 
Hematocrit   
    Anemia ↑ ↑ 
    Polycythemia ↓ ↓ 
Oxygen concentration   
    Hypoxia ↑ — 
    Oxygen therapy ↓ — 
pH (6.8–7.55) — — 
    Low pH −/↓ — 
    High pH −/↑ — 
Hypothermia ↑ ↓/↑ 
Hypotension ↑ ↑/↓ 
Drugs   
    Ascorbic acid ↓ ↑/− 
    Acetaminophen ↓ ↑ 
    Dopamine — ↓ 
    Icodextrin — ↑ 
    Mannitol ↑ — 
VariableMethodology affected*
GOGD
Whole blood ↓ ↓ 
Arterial ↑ ↑ 
Capillary ↑ ↑ 
Postprandial state ↑ ↑ 
Hematocrit   
    Anemia ↑ ↑ 
    Polycythemia ↓ ↓ 
Oxygen concentration   
    Hypoxia ↑ — 
    Oxygen therapy ↓ — 
pH (6.8–7.55) — — 
    Low pH −/↓ — 
    High pH −/↑ — 
Hypothermia ↑ ↓/↑ 
Hypotension ↑ ↑/↓ 
Drugs   
    Ascorbic acid ↓ ↑/− 
    Acetaminophen ↓ ↑ 
    Dopamine — ↓ 
    Icodextrin — ↑ 
    Mannitol ↑ — 
*

Change relative to venous plasma measured at central laboratory. GO, glucose oxidase.

1.
Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H, Bouillon R: Intensive insulin therapy in the medical ICU.
N Engl J Med
354
:
449
–461,
2006
2.
Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R: Intensive insulin therapy in the critically ill patients.
N Engl J Med
345
:
1359
–1367,
2001
3.
Malmberg K, Ryden L, Efendic S, Herlitz J, Nicol P, Waldenstrom A, Wedel H, Welin L: Randomized trial of insulin-glucose infusion followed by subcutaneous insulin treatment in diabetic patients with acute myocardial infarction (DIGAMI study): effects on mortality at 1 year.
J Am Coll Cardiol
26
:
57
–65,
1995
4.
American Diabetes Association: Standards of medical care in diabetes—2006.
Diabetes Care
29 (Suppl. 1)
:
S4
–S42,
2006
5.
American College of Endocrinology Task Force on Inpatient Diabetes and Metabolic Control: American College of Endocrinology position statement on inpatient diabetes and metabolic control.
Endocr Pract
10
:
77
–82,
2004
6.
Meijering S, Corstjens AM, Tulleken JE, Meertens JH, Zijlstra JG, Ligtenberg JJ: Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature.
Crit Care
10
:
R19
,
2006
7.
Price C: Point-of-care testing in diabetes mellitus.
Clin Chem Lab Med
41
:
1213
–1219,
2003
8.
Kanji S, Buffie J, Hutton B, Bunting PS, Singh A, McDonald K, Fergusson D, McIntyre LA, Hebert PC: Reliability of point-of-care testing for glucose measurement in critically ill adults.
Crit Care Med
33
:
2778
–2785,
2005
9.
Ashworth L, Gibb I, Alberti KGMM: HemoCue: evaluation of a portable photometric system for determining glucose in whole blood.
Clin Chem
38
:
1479
–1482,
1992
10.
Ellmerer M, Haluzik M, Blaha J, Kremen J, Svacina S, Toller W, Mader J, Schaupp L, Plank J, Pieber T: Clinical evaluation of alternative-site glucose measurements in patients after major cardiac surgery.
Diabetes Care
29
:
1275
–1281,
2006
11.
Heinemann L, the Glucose Monitoring Study Group: Continuous glucose monitoring by means of the microdialysis technique: underlying fundamental aspects.
Diabetes Technol Ther
5
:
545
–561,
2003
12.
Atkin SH, Dasmahapatra A, Jaker MA, Chorost MI, Reddy S: Fingerstick glucose determination in shock.
Ann Intern Med
114
:
1020
–1024,
1991
13.
Sylvain HF, Pokorny ME, English SM, Benson NH, Whitley TW, Ferenczy CJ, Harrison JG: Accuracy of fingerstick glucose values in shock patients.
Am J Crit Care
4
:
44
–48,
1995
14.
Ray JG, Hamielec C, Mastracci T: Pilot study of the accuracy of bedside glucometry in the intensive care unit.
Crit Care Med
29
:
2205
–2207,
2001
15.
Kulkarni A, Saxena M, Price G, O’Leary MJ, Jacques T, Myburgh JA: Analysis of blood glucose measurements using capillary and arterial blood samples in intensive care patients.
Intensive Care Med
31
:
142
–145,
2005
16.
Dacombe CM, Dalton RG, Goldie DJ, Osborne JP: Effect of packed cell volume on blood glucose estimations.
Arch Dis Child
56
:
789
–791,
1981
17.
Rao LV, Jakubiak F, Sidwell JS, Winkelman JW, Snyder ML: Accuracy evaluation of a new glucometer with automated hematocrit measurement and correction.
Clin Chim Acta
356
:
178
–183,
2005
18.
Tang Z, Lee JH, Louie RF, Kost GJ: Effects of different hematocrit levels on glucose measurements with handheld meters for point-of-care testing.
Arch Pathol Lab Med
124
:
1135
–1140,
2000
19.
Louie RF, Tang Z, Sutton DV, Lee JH, Kost GJ: Point-of-care glucose testing: effects of critical care variables, influence of reference instruments, and a modular glucose meter design.
Arch Pathol Lab Med
124
:
257
–266,
2000
20.
Maser RE, Butler MA, DeCherney GS: Use of arterial blood with bedside glucose reflectance meters in an intensive care unit: are they accurate?
Crit Care Med
22
:
595
–599,
1994
21.
Smith EA, Kilpatrick ES: Intra-operative blood glucose measurements: the effect of haematocrit on glucose test strips.
Anaesthesia
49
:
129
–132,
1994
22.
Cross MH, Brown DG: Blood glucose reagent strip tests in the operating room: influence of hematocrit, partial pressure of oxygen, and blood glucose level: a comparison of the BM-test 1–44, BM-Accutest, and Satellite G reagent strip systems.
J Clin Monit
12
:
27
–33,
1996
23.
Wiener K: An assessment of the effect of haematocrit on the HemoCue blood glucose analyser.
Ann Clin Biochem
30
:
90
–93,
1993
24.
Patrick L, Lynch M, O’Kane MJ: Methemoglobin interferes with the HemoCue B-Glucose Analyzer.
Clin Chem
48
:
581
–583,
2002
25.
Soller BR, Idwasi PO, Balaguer J, Levin S, Simsir SA, Vander Salm TJ, Collette H, Heard SO: Noninvasive, near infrared spectroscopic-measured muscle pH and PO2 indicate tissue perfusion for cardiac surgical patients undergoing cardiopulmonary bypass.
Crit Care Med
31
:
2324
–2331,
2003
26.
Oberg D, Ostenson CG: Performance of glucose dehydrogenase–and glucose oxidase–based blood glucose meters at high altitude and low temperature (Letter).
Diabetes Care
28
:
1261
,
2005
27.
Chun TY, Hirose M, Sawa T, Harada M, Hosokawa T, Tanaka Y, Miyazaki M: The effect of the partial pressure of oxygen on blood glucose concentration examined using glucose oxidase with ferricyan ion.
Anesth Analg
79
:
993
–997,
1994
28.
Tang Z, Louie RF, Payes M, Chang KC, Kost GJ: Oxygen effects on glucose measurements with a reference analyzer and three handheld meters.
Diabetes Technol Ther
2
:
349
–362,
2000
29.
Kilpatrick ES, Rumley AG, Smith EA: Variations in sample pH and pO2 affect ExacTech meter glucose measurements.
Diabet Med
11
:
506
–509,
1994
30.
Kurahashi K, Maruta H, Usuda Y, Ohtsuka M: Influence of blood sample oxygen tension on blood glucose concentration measured using an enzyme-electrode method.
Crit Care Med
25
:
231
–235,
1997
31.
Kost GJ, Vu HT, Inn M, DuPlantier R, Fleisher M, Kroll MH, Spinosa JC: Multicenter study of whole-blood creatinine, total carbon dioxide content, and chemistry profiling for laboratory and point-of-care testing in critical care in the United States.
Crit Care Med
28
:
2379
–2389,
2000
32.
Tang Z, Du X, Louie RF, Kost GJ: Effects of pH on glucose measurements with handheld glucose meters and a portable glucose analyzer for point-of-care testing.
Arch Pathol Lab Med
124
:
577
–582,
2000
33.
Haupt A, Berg B, Paschen P, Dreyer M, Haring HU, Smedegaard J, Matthaei S: The effects of skin temperature and testing site on blood glucose measurements taken by a modern blood glucose monitoring device.
Diabetes Technol Ther
7: 597–601, 2005
34.
Tang Z, Du X, Louie RF, Kost GJ: Effects of drugs on glucose measurements with handheld glucose meters and a portable glucose analyzer.
Am J Clin Pathol
113
:
75
–86,
2000
35.
Kost GJ, Nguyen TH, Tang Z: Whole-blood glucose and lactate: trilayer biosensors, drug interference, metabolism, and practice guidelines.
Arch Pathol Lab Med
124
:
1128
–1134,
2000
36.
Sylvester EC, Price CP, Burrin JM: Investigation of the potential for interference with whole blood glucose strips.
Ann Clin Biochem
31
:
94
–96,
1994
37.
Siest G, Appel W, Blijenberg GB, Capolaghi B, Galteau MM, Heusghem C, Hjelm M, Lauer KL, Le Perron B, Loppinet V, Love C, Royer RJ, Tognoni C, Wilding P: Drug interference in clinical chemistry: studies on ascorbic acid.
J Clin Chem Clin Biochem
16
:
103
–110,
1978
38.
Cartier LJ, Leclerc P, Pouliot M, Nadeau L, Turcotte G, Fruteau-de-Laclos B: Toxic levels of acetaminophen produce a major positive interference on Glucometer Elite and Accu-Chek Advantage glucose meters.
Clin Chem
44
:
893
–894,
1998
39.
Kaufmann-Raab I, Jonen HG, Jahnchen E, Kahl GF, Groth U: Interference by acetaminophen in the glucose oxidase-peroxidase method for blood glucose determination.
Clin Chem
22
:
1729
–1731,
1976
40.
Keeling AB, Schmidt P: Dopamine influence on whole-blood glucose reagent strips (Letter).
Diabetes Care
10
:
532
,
1987
41.
Randell EW, St Louis P: Interference in glucose and other clinical chemistry assays by thiocyanate and cyanide in a patient treated with nitroprusside.
Clin Chem
42
:
449
–453,
1996
42.
Janssen W, Harff G, Caers M, Schellekens A: Positive interference of icodextrin metabolites in some enzymatic glucose methods.
Clin Chem
44
:
2379
–2380,
1998
43.
Oyibo SO, Pritchard GM, McLay L, James E, Laing I, Gokal R, Boulton AJ: Blood glucose overestimation in diabetic patients on continuous ambulatory peritoneal dialysis for end-stage renal disease.
Diabet Med
19
:
693
–696,
2002
44.
Wens R, Taminne M, Devriendt J, Collart F, Broeders N, Mestrez F, Germanos H, Dratwa M: A previously undescribed side effect of icodextrin: overestimation of glycemia by glucose analyzer.
Perit Dial Int
18
:
603
–609,
1998
45.
U.S. Food and Drug Administration: FDA reminders for falsely elevated glucose readings from use of inappropriate test method [article online],
2005
. Available from http://www.fda.gov/cdrh/oivd/news/glucosefalse.html. Accessed 4 August 2006
46.
Larsen CL, Jackson C, Lyon ME: Interference of Accel wipes with LifeScan SureStep Flexx glucose meters.
Clin Biochem
39
:
414
–416,
2005
47.
Randall AG, Garcia-Webb P, Beilby JP, Randall AG: Interference by haemolysis, icterus and lipaemia in assays on the Beckman Synchron CX5 and methods for correction.
Ann Clin Biochem
27
:
345
–352,
1990
48.
Berth M, Delanghe J: Protein precipitation as a possible important pitfall in the clinical chemistry analysis of blood samples containing monoclonal immunoglobulins: 2 case reports and a review of the literature.
Acta Clin Belg
59
:
263
–273,
2004
49.
Tokmakjian S, Moses G, Haines M: Excessive sample blanking in two analyzers generate reports of apparent hypoglycemia and hypophosphatemia in patients with macroglobulinemia (Letter).
Clin Chem
36
:
1261
–1262,
1990
50.
Dimeski G, Carter A: Rare IgM interference with Roche/Hitachi Modular glucose and gamma-glutamyltransferase methods in heparin samples.
Clin Chem
51
:
2202
–2204,
2005
51.
Wenk RE, Yoho S: Pseudohypoglycemia with monoclonal immunoglobulin M.
Arch Pathol Lab Med
129
:
454
–455,
2005
52.
Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M: Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus.
Clin Chem
48
:
436
–472,
2002
53.
Sacks DB, Bernhardt P, Dunka LJ, Goldstein DE, Hortin GL, Mueller P: Point-of-Care Blood Glucose Testing in Acute and Chronic Care Facilities:
Approved Guideline
. 2nd ed. Wayne, PA, National Committee for Clinical Laboratory Standards,
2002
(NCCLS doc. no. C30–A2)
54.
International Organisation for Standardisation: Requirements for In Vitro Blood Glucose Monitoring Systems for Self-Testing in Managing Diabetes Mellitus: ISO/TC 212/WG3: Draft International Standard
ISO/DIS 15197
. Geneva, International Organisation for Standardisation,
2001
55.
American Diabetes Association: Self-monitoring of blood glucose (Consensus Statement).
Diabetes Care
19 (Suppl. 1)
:
S62
–S66,
1996
56.
Clarke WL: The original Clarke error grid analysis (EGA).
Diabetes Technol Ther
7
:
776
–779,
2005
57.
Boyd JC, Bruns DE: Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose.
Clin Chem
47
:
209
–214,
2001
58.
Weitgasser R, Gappmayer B, Pichler M: Newer portable glucose meters: analytical improvement compared with previous generation devices?
Clin Chem
45
:
1821
–1825,
1999
59.
College of American Pathologists:
Participant Summary
. Northfield, IL, College of American Pathologists,
2005
60.
Singh Dhatt G, Agarwal M, Bishawi B: Evaluation of a glucose meter against analytical quality specifications for hospital use.
Clin Chim Acta
343
:
217
–221,
2004
61.
Yuoh C, Tarek Elghetany M, Petersen JR, Mohammad A, Okorodudu AO: Accuracy and precision of point-of-care testing for glucose and prothrombin time at the critical care units.
Clin Chim Acta
307
:
119
–123,
2001
62.
Nobels F, Beckers F, Bailleul E, De Schrijver P, Sierens L, Van Crombrugge P: Feasibility of a quality assurance programme of bedside blood glucose testing in a hospital setting: 7 years’ experience.
Diabet Med
21
:
1288
–1291,
2004
63.
Kavsak PA, Zielinski N, Li D, McNamara PJ, Adeli K: Challenges of implementing point-of-care testing (POCT) glucose meters in a pediatric acute care setting.
Clin Biochem
37
:
811
–817,
2004
64.
Bergenstal R, Pearson J, Cembrowski GS, Bina D, Davidson J, List S: Identifying variables associated with inaccurate self-monitoring of blood glucose: proposed guidelines to improve accuracy.
Diabetes Educ
26
:
981
–989,
2000
65.
Kristensen GB, Christensen NG, Thue G, Sandberg S: Between-lot variation in external quality assessment of glucose: clinical importance and effect on participant performance evaluation.
Clin Chem
51
:
1632
–1636,
2005
66.
Lewandrowski K, Cheek R, Nathan DM, Godine JE, Hurxthal K, Eschenbach K, Laposata M: Implementation of capillary blood glucose monitoring in a teaching hospital and determination of program requirements to maintain quality testing.
Am J Med
93
:
419
–426,
1992
67.
Sanchez-Margalet V, Rodriguez-Oliva M, Sanchez-Pozo C, Fernandez-Gallardo MF, Goberna R: Educational intervention together with an on-line quality control program achieve recommended analytical goals for bedside blood glucose monitoring in a 1200-bed university hospital.
Clin Chem Lab Med
43
:
876
–879,
2005
68.
Vinik AI, Erbas T, Park TS, Stansberry KB, Scanelli JA, Pittenger GL: Dermal neurovascular dysfunction in type 2 diabetes.
Diabetes Care
24
:
1468
–1475,
2001
69.
Blake DR, Nathan DM: Point-of-care testing for diabetes.
Crit Care Nurs Q
27
:
150
–161,
2004
70.
Pressly KB, Batteiger TH, Barnett DZ, Woodie ME: Use of arterial blood for glucose measurement by reflectance.
Nurs Res
39
:
371
–373,
1990
71.
Slater-MacLean L, Cembrowski G, Binette T, Shalapay C, Newburn-Cook C, Hegadoren K, Chin D: Evaluation of the accuracy of the LifeScan SureStepFlexx, Roche Accu-Chek Inform and Abbott Freestyle glucose meters in arterial and capillary blood samples from critically ill patients. Abstract presented at 5th Annual Diabetes Technology Meeting of the Diabetes Technology Society,
2005
, San Francisco, California
72.
Kuwa K, Nakayama T, Hoshino T, Tominaga M: Relationships of glucose concentrations in capillary whole blood, venous whole blood and venous plasma.
Clin Chim Acta
307
:
187
–192,
2001
73.
Boyd R, Leigh B, Stuart P: Capillary versus venous bedside blood glucose estimations.
Emerg Med J
22
:
177
–179,
2003
74.
Stahl M, Brandslund I: Measurement of glucose content in plasma from capillary blood in diagnosis of diabetes mellitus.
Scand J Clin Lab Invest
63
:
431
–440,
2003
75.
Farrer M, Albers CJ, Neil HA, Adams PC, Laker MF, Alberti KG: Assessing the impact of blood sample type on the estimated prevalence of impaired glucose tolerance and diabetes mellitus in epidemiological surveys.
Diabet Med
12
:
325
–329,
1995
76.
Colagiuri S, Sandbaek A, Carstensen B, Christensen J, Glumer C, Lauritzen T, Borch-Johnsen K: Comparability of venous and capillary glucose measurements in blood.
Diabet Med
20
:
953
–956,
2003
77.
Larsson-Cohn U: Differences between capillary and venous blood glucose during oral glucose tolerance tests
Scan J Clin Lab Invest
36
:
805
–808,
1976
78.
D’Orazio P, Burnett RW, Fogh-Andersen N, Jacobs E, Kuwa K, Kulpmann WR, Larsson L, Lewenstam A, Maas AH, Mager G, Naskalski JW, Okorodudu AO, the International Federation of Clinical Chemistry Scientific Division Working Group on Selective Electrodes and Point of Care Testing: Approved IFCC recommendation on reporting results for blood glucose (abbreviated).
Clin Chem
51
:
1573
–1576,
2005
79.
Rainey PM, Jatlow P: Monitoring blood glucose meters.
Am J Clin Pathol
103
:
125
–126,
1995
80.
Holtkamp HC, Verhoef NJ, Leijnse B: The difference between the glucose concentrations in plasma and whole blood.
Clin Chim Acta
59
:
41
–49,
1975
81.
Kempe KC, Czeschin LI, Yates KH, Deuser SM, Scott MG: A hospital system glucose meter that produces plasma-equivalent values from capillary, venous, and arterial blood.
Clin Chem
43
:
1803
–1804,
1997
82.
LifeScan: LifeScan OneTouch II package insert [online]. Available from www.lifescan.com. Accessed 7 August
2006
83.
Finkielman JD, Oyen LJ, Afessa B: Agreement between bedside blood and plasma glucose measurement in the ICU setting.
Chest
127
:
1749
–1751,
2005
84.
Binette T, Cembrowski G, Carey N, Slater-MacLean L, Shalapay C, Chin D: Determination of allowable error for glucose monitoring in ICU patient. Abstract presented at 5th Annual Diabetes Technology Meeting of the Diabetes Technology Society,
2005
, San Francisco, California
85.
Hawkins RC: Evaluation of Roche Accu-Chek Go and Medisense Optium blood glucose meters.
Clin Chim Acta
353
:
127
–131,
2005

J.B. has received research support from BD Research Laboratories and Dexcom and has been a paid consultant for Johnson & Johnson.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C Section 1734 solely to indicate this fact.