Use of serial patient hemoglobin A1c differences to determine long-term imprecision of immunoassay and high-performance liquid chromatography analyzers

Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation

BACKGROUND

Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results.

METHODS

Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error.

RESULTS

ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25.

CONCLUSIONS

AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.

The Glucose Measurement Industry and Hemoglobin A1c: An Opportunity for Creative Destruction

The MyStar Extra self-monitoring blood glucose (SMBG) system provides moving estimates of the patient's hemoglobin A1c (HbA1c). There is a treasure trove of highly accurate glucose data available from highly accurate SMBG, CGM and FGM along with highly accurate HPLC HbA1c. If Nathan's criteria are used to select subjects whose glucoses can be correlated to the HbA1c, then algorithms can be developed for robustly transforming glucose into HbA1c. These algorithms can then be implemented in any SMBG or with the CGM and FGM software. This calculated HbA1c would even be accurate with Nathan's excluded population thus reducing the use of fructosamine and glycated protein. Finally, the developer of these new algorithms is advised to use a specific approach for testing her algorithm.

Association of pre-pregnancy BMI and postpartum weight retention with postpartum HbA1c among women with Type 1 diabetes

AIM

To examine the association of pre-pregnancy BMI and postpartum weight retention with postpartum HbA(1c) levels in women with Type 1 diabetes.

METHODS

We longitudinally evaluated 136 women with Type 1 diabetes who received prenatal, pregnancy, and postpartum care through Joslin Diabetes Center's Diabetes and Pregnancy Program between 2004 and 2009. Weight, BMI and HbA(1c) concentrations were assessed before the index pregnancy and repeatedly monitored after delivery until 12 months postpartum. We used linear mixed models to assess the association of postpartum HbA(1c) with pre-pregnancy BMI and postpartum weight retention.

RESULTS

The mean HbA(1c) concentration increased from 49 mmol/mol (6.6%) at 6 weeks postpartum to 58 mmol/mol (7.5%) by 10 months postpartum, a level similar to the mean pre-pregnancy HbA(1c) concentration. Postpartum weight retention showed a linearly decreasing trend of 0.06 kg/week (P < 0.0001), with -0.1 kg average postpartum weight retention by 1 year postpartum. Compared with women with a pre-pregnancy BMI ≥ 25 kg/m², women with a lower pre-pregnancy BMI maintained a 3.4 mmol/mol (0.31%) lower HbA(1c) concentration, after adjusting for several sociodemographic, reproductive and diabetes-related factors (P = 0.03). There was a suggestion of a time-varying positive association between HbA1c and postpartum weight retention, with the most significant difference of 3.7 mmol/mol (0.34%; P = 0.05) at 30 weeks postpartum among women with postpartum weight retention ≥ 5 kg vs those with postpartum weight retention < 5 kg.

CONCLUSIONS

Pre-pregnancy BMI and postpartum weight retention were positively associated with HbA(1c) during the first postpartum year in women with Type 1 diabetes. Interventions to modify the behaviours associated with these body weight factors before pregnancy and after delivery may help women with Type 1 diabetes maintain good glycaemic control after pregnancy.

Comparison of longitudinal point-of-care and high-performance liquid chromatography HbA1c measurements in a multi-centre trial

AIMS

Point-of-care HbA(1c) is routine in clinical practice. Comparison of point-of-care HbA(1c) against laboratory measurements across sites and over time is warranted.

METHODS

One hundred and twenty-one young persons with Type 1 diabetes from four centres provided 450 paired samples collected over 10 months for point-of-care HbA(1c) and central laboratory-based high-performance liquid chromatography (HPLC) HbA(1c) determinations. Change in HbA(1c) over time was assessed by difference from initial to final HbA(1c) and by growth modelling with annualized slope calculation. Change in HbA(1c) was categorized as improved (decrease of ≥ 0.5% or negative slope), no change (± 0.4% of initial HbA(1c) or slope = 0) or worsened (increase of ≥ 0.5% or positive slope).

RESULTS

The 450 paired samples (median of four pairs/patient) were highly correlated (r = 0.97, P < 0.0001), as were time-specific and site-specific pairs (r = 0.94 to 0.98, P < 0.0001). Initial-to-final point-of-care HbA(1c) and HPLC HbA(1c) changes were 0.3 ± 1.1% (range -2.7 to 4.1) and 0.4 ± 1.2% (-3.9 to 4.5), respectively, with 21% of patients (n = 26) discordant for change categories. ΔHbA(1c) by point-of-care HbA(1c) vs. HPLC HbA(1c) differed across the HbA(1c) range and by ≥ 0.5% absolute difference in ΔHbA(1c) in 14 (54%) of the 26 patients discordant for HbA(1c) change categories. Mean annual HbA(1c) slope was 0.4 ± 1.5% (-5.4 to 4.8) for point-of-care HbA(1c) and 0.4 ± 1.6% (-6.9 to 5.2) for HPLC HbA(1c), with 18% (n = 22 pairs) discordant for change categories.

CONCLUSIONS

Assessment of absolute HbA(1c) change may not be different for point-of-care HbA(1c) compared with HPLC HbA(1c); however, misclassification of patients by discrete cut-off values may occur with point-of-care HbA(1c) compared with HPLC HbA(1c) determinations.

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.