An Assessment of HbA1c in Diabetes Mellitus and Pre-diabetes Diagnosis: a Multi-centered Data Mining Study.

HbA1c test has been widely used to evaluate glycemic control in patients with diabetes. However, there are controversial results regarding the value of HbA1c in the diagnosis of diabetes mellitus (DM). The present study investigates the diagnostic effectiveness of HbA1c in a large patient group. The oral glucose tolerance test and HbA1c results of 6551 patients (4704 healthy, 1345 pre-diabetes, 502 DM) in 12 different medical centers in Turkey between 2010 and 2016 were examined to understand the effectiveness of HbA1c in the diagnosis of DM. Different Roche systems were used for measuring HbA1c via the immunoturbidimetric method. The DM ROC curves revealed the diagnostic sensitivity, specificity, and AUC of 74.5%, 87.1%, and 0.866 (CI 95% 0.858-0.875), respectively, for HbA1c (at the cut-off 41 mmol/mol, 5.9%). For HbA1c at the universal diagnostic decision value of 48 mmol/mol (6.5%), the sensitivity and specificity were determined as 32.4% and 99.9%, respectively. The ROC curves for fasting plasma glucose (FPG) revealed the diagnostic sensitivity, specificity, and AUC of 71.3%, 85.3%, and 0.853 (CI 95% 0.844-0.861), respectively. However, the ROC curve results for pre-diabetes (HbA1c at the cut-off value of 39 mmol/mol, 5.7%) revealed the diagnostic sensitivity, specificity, and AUC of 45.7%, 76.4%, and 0.641, respectively. Furthermore, it was shown that the changes in HbA1c values due to gender and age had no clinical effect on the diagnosis. According to our results, it remains challenging to suggest HbA1c measurements can have a significant contribution to the FPG measurements. It was found that the sensitivity is specifically low in the assessment of the pre-diabetes data. Additionally, considering the problems associated with Hb1Ac measurements, further studies conducted in different regions by using different methods are required.

Biological variation data for lipid cardiovascular risk assessment biomarkers. A systematic review applying the biological variation data critical appraisal checklist (BIVAC).

BACKGROUND: Biological variation (BV) data can be used to set analytical performance specifications (APS) for lipid assays. Poor performance will impact upon the efficacy of international guidelines for cardiovascular risk assessment (CVR) and relevant clinical decision limits. This systematic review applies the Biological Variation Data Critical Appraisal Checklist (BIVAC) to published studies of BV of CVR biomarkers enabling metanalysis of the data. METHODS: Studies of BV of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides and apolipoproteins A1 and B, retrieved using a systematic literature search, were evaluated and graded using the BIVAC. Meta-analysis of CVI and CVG estimates were performed utilizing weightings based upon BIVAC grades and the width of the data confidence intervals. RESULTS: Applying the BIVAC, ten publications were graded as D, 43 as C, 5 as B and 1 as A (fully compliant). A total of 196 CVI and 87 CVG estimates were available for the different lipid measurands. The meta-analysis-derived BV data estimates were generally concordant with those in the online 2014 BV database. CONCLUSIONS: Application of BIVAC identifies BV data suitable for many important applications including setting APS. Additionally, this review identifies a need for new BIVAC compliant studies to deliver BV reference data in different subpopulations.

Sigma metric revisited: True known mistakes.

Six Sigma methodology has been used successfully in industry since the mid-1980s. Unfortunately, the same success has not been achieved in laboratory medicine. In this case, although the multidisciplinary structure of laboratory medicine is an important factor, the concept and statistical principles of Six Sigma have not been transferred correctly from industry to laboratory medicine. Furthermore, the performance of instruments and methods used in laboratory medicine is calculated by a modified equation that produces a value lower than the actual level. This causes unnecessary, increasing pressure on manufacturers in the market. We concluded that accurate implementation of the sigma metric in laboratory medicine is essential to protect both manufacturers by calculating the actual performance level of instruments, and patients by calculating the actual error rates.