State of Harmonization of 24 Serum Albumin Measurement Procedures and Implications for Medical Decisions.

BACKGROUND: Measurements of serum and plasma albumin are widely used in medicine, including as indicators of quality of patient care in renal dialysis centers. METHODS: Pools were prepared from residual patient serum (n = 50) and heparin plasma (n = 48) from patients without renal disease, and serum from patients with kidney failure before hemodialysis (n = 53). Albumin was measured in all samples and in ERM-DA470k/IFCC reference material (RM) by 3 immunochemical, 9 bromcresol green (BCG), and 12 bromcresol purple (BCP) methods. RESULTS: Two of 3 immunochemical procedures, 5 of 9 BCG, and 10 of 12 BCP methods recovered the RM value within its uncertainty. One immunochemical and 3 BCG methods were biased vs the RM value. Random error components were small for all measurement procedures. The Tina-quant immunochemical method was chosen as the reference measurement procedure based on recovery and results of error analyses. Mean biases for BCG vs Tina-quant were 1.5% to 13.9% and were larger at lower albumin concentrations. BCP methods' mean biases were -5.4% to 1.2% irrespective of albumin concentration. Biases for plasma samples were generally higher than for serum samples for all method types. For most measurement procedures, biases were lower for serum from patients on hemodialysis vs patients without kidney disease. CONCLUSIONS: Significant differences among immunochemical, BCG, and BCP methods compromise interpretation of serum albumin results. Guidelines and calculations for clinical management of kidney and other diseases must consider the method used for albumin measurement until harmonization can be achieved.

Effects of measurement frequency on analytical quality required for glucose measurements in intensive care units: assessments by simulation models.

BACKGROUND: Total error allowances have been proposed for glucose meters used in tight-glucose-control (TGC) protocols. It is unclear whether these proposed quality specifications are appropriate for continuous glucose monitoring (CGM). METHODS: We performed Monte Carlo simulations of patients on TGC protocols. To simulate use of glucose meters, measurements were made hourly. To simulate CGM, glucose measurements were made every 5 min. Glucose was measured with defined bias (varied from -20% to 20%) and imprecision (0% to 20% CV). The measured glucose concentrations were used to alter insulin infusion rates according to established treatment protocols. Changes in true glucose were calculated hourly on the basis of the insulin infusion rate, the modeled patient's insulin sensitivity, and a model of glucose homeostasis. We modeled 18 000 patients, equally divided between the hourly and every-5-min measurement schemas and distributed among 45 combinations of bias and imprecision and 2 treatment protocols. RESULTS: With both treatment protocols and both measurement frequencies, higher measurement imprecision increased the rates of hypoglycemia and hyperglycemia and increased glycemic variability (SD). These adverse effects of measurement imprecision were lower at the higher measurement frequency. The rate of hypoglycemia at an imprecision (CV) of 5% with hourly measurements was similar to the rate of hypoglycemia at 10% CV when measurements were made every 5 min. With measurements every 5 min, imprecision up to 10% had minimal effects on hyperglycemia or glycemic variability. Effects of simulated analytical bias on glycemia were unaffected by measurement frequency. CONCLUSIONS: Quality specifications for imprecision of glucose meters are not transferable to CGM.

Failure of current laboratory protocols to detect lot-to-lot reagent differences: findings and possible solutions.

BACKGROUND: Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results. METHODS: For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions. RESULTS: Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results. CONCLUSIONS: The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

Fifteen-minute Frequency of Glucose Measurements and the Use of Threshold Alarms: Impact on Mitigating Dysglycemia in Critically Ill Patients.

BACKGROUND: The use of near-continuous blood glucose (BG) monitoring has the potential to improve glycemic control in critically ill patients. The MANAGE IDE trial evaluated the performance of the OptiScanner (OS) 5000 in a multicenter cohort of 200 critically ill patients. METHODS: An Independent Group reviewed the BG run charts of all 200 patients and voted whether unblinded use of the OS, with alarms set at 90 and 130 to 150 mg/dL to alert the clinical team to impending hypoglycemia and hyperglycemia, respectively, would have eliminated episodes of dysglycemia: hypoglycemia, defined as a single BG <70 mg/dL; hyperglycemia, defined as >4 hours of BG >150 mg/dL; severe hyperglycemia, defined as >4 hours of BG >200 mg/dL and increased glucose variability (GV), defined as coefficient of variation (CV) >20%. RESULTS: At least one episode of dysglycemia occurred in 103 (51.5%) of the patients, including 6 (3.0%) with hypoglycemia, 83 (41.5%) with hyperglycemia, 18 (9.0%) with severe hyperglycemia, and 40 (20.0%) with increased GV. Unblinded use of the OS with appropriate alarms would likely have averted 97.1% of the episodes of dysglycemia: hypoglycemia (100.0%), hyperglycemia (96.4%), severe hyperglycemia (100.0%), and increased GV (97.5%). Point accuracy of the OS was very similar to that of the point of care BG monitoring devices used in the trial. CONCLUSION: Unblinded use of the OS would have eliminated nearly every episode of dysglycemia in this cohort of critically ill patients, thereby markedly improving the quality and safety of glucose control.

Glucose meter performance criteria for tight glycemic control estimated by simulation modeling.

BACKGROUND: Glucose meter analytical performance criteria required for safe and effective management of patients on tight glycemic control (TGC) are not currently defined. We used simulation modeling to relate glucose meter performance characteristics to insulin dosing errors during TGC. METHODS: We used 29,920 glucose values from patients on TGC at 1 institution to represent the expected distribution of glucose values during TGC, and we used 2 different simulation models to relate glucose meter analytical performance to insulin dosing error using these 29,920 initial glucose values and assuming 10%, 15%, or 20% total allowable error (TEa) criteria. RESULTS: One-category insulin dosing errors were common under all error conditions. Two-category insulin dosing errors occurred more frequently when either 20% or 15% TEa was assumed compared with 10% total error. Dosing errors of 3 or more categories, those most likely to result in hypoglycemia and thus patient harm, occurred infrequently under all error conditions with the exception of 20% TEa. CONCLUSIONS: Glucose meter technologies that operate within a 15% total allowable error tolerance are unlikely to produce large (>or=3-category) insulin dosing errors during TGC. Increasing performance to 10% TEa should reduce the frequency of 2-category insulin dosing errors, although additional studies are necessary to determine the clinical impact of such errors during TGC. Current criteria that allow 20% total allowable error in glucose meters may not be optimal for patient management during TGC.

An appraisal of statistical procedures used in derivation of reference intervals.

When conducting studies to derive reference intervals (RIs), various statistical procedures are commonly applied at each step, from the planning stages to final computation of RIs. Determination of the necessary sample size is an important consideration, and evaluation of at least 400 individuals in each subgroup has been recommended to establish reliable common RIs in multicenter studies. Multiple regression analysis allows identification of the most important factors contributing to variation in test results, while accounting for possible confounding relationships among these factors. Of the various approaches proposed for judging the necessity of partitioning reference values, nested analysis of variance (ANOVA) is the likely method of choice owing to its ability to handle multiple groups and being able to adjust for multiple factors. Box-Cox power transformation often has been used to transform data to a Gaussian distribution for parametric computation of RIs. However, this transformation occasionally fails. Therefore, the non-parametric method based on determination of the 2.5 and 97.5 percentiles following sorting of the data, has been recommended for general use. The performance of the Box-Cox transformation can be improved by introducing an additional parameter representing the origin of transformation. In simulations, the confidence intervals (CIs) of reference limits (RLs) calculated by the parametric method were narrower than those calculated by the non-parametric approach. However, the margin of difference was rather small owing to additional variability in parametrically-determined RLs introduced by estimation of parameters for the Box-Cox transformation. The parametric calculation method may have an advantage over the non-parametric method in allowing identification and exclusion of extreme values during RI computation.

Common reference intervals for aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) in serum: results from an IFCC multicenter study.

BACKGROUND: Aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) measurements are important for the assessment of liver damage. The aim of this study was to define the reference intervals (RIs) for these enzymes in adults, paying attention to standardization of the methods used and careful selection of the reference population. METHODS: AST, ALT and GGT were measured with commercial analytical systems standardized to the IFCC-recommended reference measurement systems. Three centers (two in Italy and one in China) measured their own freshly collected samples; one of these centers also measured frozen samples from the Nordic Countries RI Project and from a Turkish center. RIs were generated using non-parametric techniques from the results of 765 individuals (411 females and 354 males, 18-85 years old) selected on the basis of the results of other laboratory tests and a specific questionnaire. RESULTS: AST results from the four regions (Milan, Beijing, Bursa and Nordic Countries) were statistically different, but these differences were too small to be clinically relevant. Likewise, differences between the upper reference limits for genders was only 1.7 U/L (0.03 µkat/L), allowing a single RI of 11-34 U/L (0.18-0.57 µkat/L) to be defined. Interregional differences were not statistically significant for ALT, but partitioning was required due to significant gender differences. RIs for ALT were 8-41 U/L (0.13-0.68 µkat/L) for females and 9-59 U/L (0.15-0.99 µkat/L) for males, respectively. The upper reference limits for GGT from the Nordic Country population were higher than those from the other three regions and results from this group were excluded from final calculations. The GGT RIs were 6-40 U/L (0.11-0.66 µkat/L) for females and 12-68 U/L (0.20- 1.13 µkat/L) for males, respectively. CONCLUSIONS: For AST and ALT, the implementation of common RIs appears to be possible, because no differences between regions were observed. However, a common RI for GGT that is applicable worldwide appears unlikely due to differences among populations.

The use of urinary dipstick tests to exclude urinary tract infection: a systematic review of the literature.

Several systematic reviews have examined the use of dipstick tests to diagnose or rule in urinary tract infection (UTI). We examined the evidence relating to the use of urine leukocyte esterase and nitrite tests in adults to exclude or rule out UTI. A search of the literature from 1966 to 2003 revealed 30 studies as containing relevant and suitable information and 23 of these, which used a cut-off of 108 colony-forming units per liter, were combined in a meta-analysis. The leukocyte esterase or nitrite test combination, with one or the other test positive, was used in 14 studies, showed the highest sensitivity and the lowest negative likelihood ratio. While there was significant heterogeneity between the studies, 7 of 14 demonstrated significant decreases in pretest to posttest probability with a pooled posttest probability of 5% for the negative result. In certain circumstances, there is evidence for the use of urinalysis as a rule-out test for UTI.

Imported platelets demonstrate decreased pH and glucose by reagent strip testing when compared to locally derived platelets.

The most common infectious risk from blood transfusion in the United States is bacterial contamination of platelet components. Although detection of bacterially contaminated platelet components is best achieved with a culture system, AABB standards permit alternatives including the use of staining methods or reagent strips. In this study, 13,216 consecutive platelet components were screened using reagent strips for evidence of the presence of bacteria. Testing was performed immediately prior to release of the platelet components for transfusion; 10,836 were collected locally and 2,380 were imported from other blood banks. A mix of whole-blood-derived and apheresis products was included. If either the glucose concentration was <250 mg/ml or the pH was <7.0, the platelet component was quarantined and a specimen was obtained for Gram's stain and culture. Every transfused platelet component that was associated with a reported transfusion reaction was also tested by Gram's stain and culture. Overall, 1.47% of imported platelet components were reactive while only 0.12% of locally collected platelets were reactive. Of 48 reactive platelet components, 44 were tested by Gram's stain and culture. None was found to be bacterially contaminated. In summary, imported platelet components were significantly more likely to be falsely reactive by reagent strip screening as compared to locally prepared platelet components.

Evaluation of analytical methods and workflow performance of the Architect ci8200 integrated serum/plasma analyzer system.

BACKGROUND: The Architect ci8200 is an integrated serum analyzer for photometric, electrochemical and immunological assays. Several assays of each category and the workflow performance of the system were compared with established laboratory procedures in two laboratories. METHODS: Measurements were compared with the ELECSYS 2010 (Roche Diagnostics) for CEA, PSA, FPSA, AFP, folate, vitamin B12, with the CENTAUR (Bayer) for TSH, T4, FT4, FSH and Estradiol, with the LIAISON (DiaSorin) for TSH, FT4 and FT3, with the Behring Nephelometer BN II (Dade-Behring) for ferritin, and with the INTEGRA 800 (Roche Diagnostics), and the AU640 (Olympus) for clinical chemistry assays. Workflow studies were performed to compare times of analysis required for defined analytical workloads. RESULTS: The coefficients of variation (CVs) for within-run imprecision were between 3% and 6% for CEA, PSA, FPSA, AFP and ferritin, and between 3% and 11% for TSH, FT4, FT3, folate and vitamin B12. The CVs for day-to-day imprecision for immunoassays were between 3% and 10%, except for vitamin B12 (CVs 11-13%) and FT4 (CV 10% -13%). For clinical chemistry tests corresponding CVs for within-run imprecision were < 1%, except for HDL, triglyceride, creatinine, ALT, LD and lipase (CVs<2%) and bicarbonate (CV 3%-6%) and magnesium (CV < 3%). The CVs for day-to-day imprecision for clinical chemistry tests were < 1%, except for sodium, CO(2), magnesium, phosphorus, glucose, uric acid, HDL, triglyceride, ALT, AST CK, lipase with CVs < 6% and for CO(2)<11%. Dilutional linearity testing of seven immunoassays and five clinical chemistry analytes resulted in recovery rates of 90-110%. Correlation studies with 15 immunoassays and 25 clinical chemistry tests showed acceptable agreements with established methods. Work flow analyses demonstrated a net gain in time of analysis up to 109 min depending on the size of the sample batch analyzed with the Architect ci8200 as the main analyzer as compared to the currently installed routine laboratory equipment. Median turn-around times were 7 and 30 min for chemistry assays and immunoassays, respectively, when ordered as STAT analyses, and 18 min when chemistry assays were ordered as routine determinations. CONCLUSIONS: Assays on the Architect ci8200 performed well, fulfilling quality control requirements as defined for instance by German quality control guidelines (RiliBAK). Method comparisons showed acceptable agreements with established assays. Workflow studies using the Architect ci8200 documented shorter times of analyses as compared with the conventionally established laboratory routine demonstrating the potential of integrated chemistry/immunoassay analyzers to provide faster and more efficient performance.

The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.

The role of blood glucose (BG) measurement frequency on the domains of glycemic control is not well defined. This Monte Carlo mathematical simulation of glycemic control in a cohort of critically ill patients modeled sets of 100 patients with simulated BG-measuring devices having 5 levels of measurement imprecision, using 2 published insulin infusion protocols, for 200 hours, with 3 different BG-measurement intervals-15 minutes (Q15'), 1 hour (Q1h), and 2 hours (Q2h)-resulting in 1,100,000 BG measurements for 3000 simulated patients. The model varied insulin sensitivity, initial BG value and rate of gluconeogenesis. The primary outcomes included rates of hyperglycemia (BG > 180 mg/dL), hypoglycemia (BG < 70 and 40 mg/dL), proportion of patients with elevated glucose variability (within-patient coefficient of variation [CV] > 20%), and time in range (BG ranges 80-150 mg/dL and 80-180 mg/dL). Percentages of hyperglycemia, hypoglycemia at both thresholds, and patients with elevated glucose variability as well as time outside glycemic targets were substantially higher in simulations with measurement interval Q2h compared to those with measurement interval Q1h and moderately higher in simulations with Q1h than in those with Q15'. Higher measurement frequency mitigated the deleterious effect of high measurement imprecision, defined as CV ≥ 15%. This Monte Carlo simulation suggests that glycemic control in critically ill patients is more optimal with a BG measurement interval no longer than 1h, with further benefit obtained with use of measurement interval of 15'. These findings have important implications for the development of glycemic control standards.