David V. Tran

Seasonal Variation in Hemoglobin A1c: Is It the Same in Both Hemispheres?

Introduction: There are several reports from locations in the northern hemisphere of seasonal variation in hemoglobin A1c (HbA1c) levels with higher values noted in the cooler months. The variation has been attributed to holiday seasons, temperature differences, and changes in diet. This article describes the seasonal variation in both hemispheres and in a country on the equator with minimal temperature variation. Methods: The mean and median HbA1c by month was calculated for a maximum of 2 years for HbA1c data from the different locations: Edmonton and Calgary, Canada; Singapore; Melbourne, Australia; and Marshfield, Wisconsin. The mean monthly temperature for each location was found from available meteorological information. Results: In both northern and southern hemispheres, the HbA1c was higher in cooler months and lower in the warmer months. In Singapore, where there is minimal temperature variation, there is also minimal variation in HbA1c values over the year. The difference in HbA1c over a year appears to be related to the difference in temperature. Conclusion: Hemoglobin A1c is higher in cooler months and lower in the warmer months in both hemispheres. In a country with minimal monthly temperature variation, there is only minimal variation in HbA1c values through the year. In all locations, the mean and median HbA1c declined over the study period, possibly due to better glycemic control of patients with diabetes or an increase in use of HbA1c as a screening test for diabetes or a combination of both.

The use of serial patient blood gas, electrolyte and glucose results to derive biologic variation: a new tool to assess the acceptability of intensive care unit testing.

Abstract Background: Most estimates of biologic variation (sb) are based on periodically acquiring and storing specimens, followed by analysis within a single analytic run. We demonstrate for many intensive care unit (ICU) tests, only patient results need be statistically analyzed to provide reliable estimates of sb. Methods: Over 11 months, approximately 28,000 blood gas measurements (including electrolyte panels and glucose) were performed on one of two Radiometer ABL800 FLEX analyzers (Radiometer, Copenhagen, Denmark) from 1676 ICU patients. We tabulated the measurements of paired intra-patient blood samples drawn within 24 h of each other. After removal of outliers, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in 2-h intervals: 0–2 h, 2–4 h, 4–6 h, … 20–22 h and 22–24 h. The SDDs were then regressed against the time intervals of 2–14 h; extrapolation to zero time represents the sum of sb and short-term analytic variation (sa). Results: Substitution of experimentally derived analytic error permitted the calculation of coefficient of variation (biologic) (CVb) (100 sb/mean): pH, 0.3%; pCO2, 5.7%; pO2, 13%; Na+, 0.6%; K+, 4.8%; Cl–, 0.8%; HCO3–, 3.2%; iCa++, 2.4%; and glucose, 10.3%. The CVb of the electrolytes very closely matches the lowest estimates obtained in the usual manner. Conclusions: Derivation of the ratio of biologic to analytic variation indicates that the ABL800 is extremely suitable for ICU testing. This analysis should be extended to other point of care instrument systems. Clin Chem Lab Med 2010;48:1447–54.

Could Susceptibility to Low Hematocrit Interference Have Compromised the Results of the NICE-SUGAR Trial?

The recently published findings of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR)1 trial have dramatically changed clinician attitudes toward the achievement of euglycemia in intensive care unit (ICU) patients (1). In defending the proof-of-concept studies that validated the efficacy of normalizing blood glucose in the ICU, Van den Berghe et al. pointed out numerous variances between their original studies and those of the NICE-SUGAR trial(2). They included differences in blood glucose targets, insulin administration, blood sampling, nutritional strategies, clinician expertise, and the relative accuracy of the glucose measurement devices. Recently, Clinical Chemistry presented a very interesting Q&A on the use of blood glucose meters to achieve tight glucose control in patients in the ICU(3). Because one of our ICUs participated in the NICE-SUGAR trial, we report here some interesting and relevant data that shed more light on the NICE-SUGAR trial, data that yield more questions than answers. In our 30-bed general systems ICU at the University of Alberta Hospital, point-of-care glucose concentrations can be measured in 2 different ways: respiratory therapists measure arterial blood gases, hemoglobin, electrolytes, and glucose values with the Radiometer 800 blood gas system (BGA) and nurses measure arterial blood and …

Influence of Variables on Hemoglobin A1c Values and Nonheterogeneity of Hemoglobin A1c Reference Ranges

Introduction: Hemoglobin A1c (HbAlc) values are influenced by analytical interferences such as HbF and hemoglobin variants and clinical factors such as increased red cell turnover. Although less well-known, demographic factors such as race, age, and sex also influence HbA1c values. The HbA1c reference range should be homogenous in the United States based on the use of National Glycohemoglobin Standardization Program certified methods and the recommendations in the National Academy of Clinical Biochemistry guidelines. Methods: Data on age, race, sex, HbA1c, and glucose values were extracted from the National Health and Nutrition Examination study for a 3 year period. A search for reference range data for laboratories in the United States was performed using the Google search engine. Results: Extracted data agree with published data on the influence of age, sex, and smoking status on HbA1c values. There is substantial heterogeneity in HbA1c reference ranges in laboratories in the United States. Conclusion: Age, sex, and smoking status influence HbA1c values. Despite standardization of HbA1c methods and published recommendations, there is wide heterogeneity in HbA1c reference ranges in the United States.

Unique Approach to Derivation of Random Error in Laboratory Assays: Application to Glycohemoglobin Testing Demonstrates Poor Clinical Performance for Immunochemistry Assay

The measurement of glycohemoglobin is the best measure of mean glucose within a 3-4 month range. As it is used for patient education, counseling, feedback control, and ultimately for patient motivation, its measurement should be optimally accurate and precise. Duplicate hemoglobin A1c readings were used to determine physiological (changes over time between measurements) and analytic variation of two widely used laboratory assays: Bio-Rad Variant IIs high-performance liquid chromatography (HPLC) system and Roches immunoassay. The average variation of grouped duplicates was calculated and graphed against corresponding time intervals. Regression to the y-intercept (0 day separation between readings) was used to determine the analytic variation. Analytic coefficients of variation (CVs) for the HPLC and immunoassay were determined as 2.6% and 5.1%, respectively. The CV of the immunoassay method exceeds physiologically established limits of 2-3% and those of the National Glycohemoglobin Standardization Program (3-4%). The Bio-Rad HPLC system produces a CV within these limits.

Application of 3-D Δ Check Graphs to HbA1c Quality Control and HbA1c Utilization

Delta checking is a laboratory information system (LIS)-based tool that detects patient and laboratory quality control errors. By using hemoglobin A1c (HbA1c) data, we developed a novel approach to summarizing and presenting patient Delta values to address limitations of current Delta check algorithms. Delta values were calculated from intrapatient pairs of HbA1c (n = 55,327) measured during 2 years in a single referral or a university hospital laboratory. Three-dimensional Delta-time (DeltaT) and percentile limit graphs were constructed. Cumulative distribution function analysis was used to explore clinical utilization. The DeltaT graphs showed that HbA1c Delta values increase asymmetrically over time. Although the 2.5 to 97.5 and 5.0 to 95.0 percentile Delta check limits were similar for both sites, the referral laboratorys 0.5 to 99.5 percentile limits were wider. For acute patient care environments, we recommend limits of -3.5% and 1.8% for measurements between 0 and 60 days and -4.0% and 2.0% for measurements between 60 and 120 days. For the outpatient environment, we recommend limits of -4.2% and 2.1% and 5.0% and 2.5% for measurements between 0 and 60 days and 60 and 120 days, respectively.Delta checking can be significantly improved with customization of limits set by population and interobservation period. Because LIS systems are incapable of these customizations, customers must become advocates for these modifications.

Tighter precision target required for lactate testing in patients with lactic acidosis

Abstract Background: Allowable analytic errors are generally based on biologic variation in normal, healthy subjects. Some analytes like blood lactate have low concentrations in healthy individuals and resultant allowable variation is large when expressed as a coefficient of variation (CV). In Ricós’ compendium of biologic variation, the relative pooled intra-individual lactate variation (si) averages 27% and the desirable imprecision becomes 13.5%. We derived biologic variability (sb) from consecutive patient data and demonstrate that sb of lactate is significantly lower. Methods: A data repository provided lactate results measured over 18 months in the General Systems intensive care unit (ICU) at the University of Alberta Hospital in Edmonton, Canada. In total 54,000 lactate measurements were made on two point-of-care Radiometer 800 blood gas systems operated by Respiratory Therapy. The standard deviations of duplicates (SDD) were tabulated for the intra-patient lactates that were separated by 0–1, 1–2…up to 16 h. The graphs of SDD vs. time interval were approximately linear; the y-intercept provided by the linear regression represents the sum of sb and short-term analytic variation (sa):y0=(sa2+s)b212. \(({{\rm{s}}_{\rm{a}}}):{{\rm{y}}_{\rm{0}}} = {({\rm{s}}_{\rm{a}}^{\rm{2}} + {\rm{s}}{}_{\rm{b}}^{\rm{2}})^{{1 \over 2}}}.\) The short-term sa was determined from imprecisions provided by Radiometer and confirmed with onsite controls. The derivation of sb was performed for multiple patient ranges of lactate. Results: The relative desirable lactate imprecision for patients with lactic acidosis is about half that of normal individuals. Conclusions: As such, evaluations of lactate measurements must use tighter allowable error limits.

Is HbA1c a good screening test for diabetes mellitus

OBJECTIVES HbA(1c) has been recently recommended as the primary diagnostic test for diabetes. This study evaluated the positive predictive value (PPV) and negative predictive value (NPV) of HbA(1c) against the oral glucose tolerance test (OGTT) in three locations. DESIGN AND METHODS Three years of data with concurrent OGTT and HbA(1c) tests were extracted from Laboratory Information Systems (LIS) and receiver operator (ROC) curves and positive and negative predictive values calculated comparing the OGTT with the HbA(1c) values using a 10% prevalence of diabetes. RESULTS The recommended threshold HbA(1c) value of 6.5% did not give the optimal combination of NPV (0.93 to 0.92) and PPV (0.40 to 0.61) compared to a threshold HbA(1c) value of 7.0% (NPV 0.91 to 0.92, PPV 0.61 to 0.73). CONCLUSION The optimal HbA(1c) value for the diagnosis of diabetes is 7.0% but even at this HbA(1c) the PPV is suboptimal and may cause up to 12% of patients without diabetes, as defined by a normal OGTT, to be classified having diabetes mellitus.