Investigation of 2 models to set and evaluate quality targets for hb a1c: biological variation and sigma-metrics.
ABSTRACT: A major objective of the IFCC Task Force on Implementation of HbA1c Standardization is to develop a model to define quality targets for glycated hemoglobin (Hb A1c).Two generic models, biological variation and sigma-metrics, are investigated. We selected variables in the models for Hb A1c and used data of external quality assurance/proficiency testing programs to evaluate the suitability of the models to set and evaluate quality targets within and between laboratories.In the biological variation model, 48% of individual laboratories and none of the 26 instrument groups met the minimum performance criterion. In the sigma-metrics model, with a total allowable error (TAE) set at 5 mmol/mol (0.46% NGSP), 77% of the individual laboratories and 12 of 26 instrument groups met the 2? criterion.The biological variation and sigma-metrics models were demonstrated to be suitable for setting and evaluating quality targets within and between laboratories. The sigma-metrics model is more flexible, as both the TAE and the risk of failure can be adjusted to the situation-for example, requirements related to diagnosis/monitoring or international authorities. With the aim of reaching (inter)national consensus on advice regarding quality targets for Hb A1c, the Task Force suggests the sigma-metrics model as the model of choice, with default values of 5 mmol/mol (0.46%) for TAE and risk levels of 2? and 4? for routine laboratories and laboratories performing clinical trials, respectively. These goals should serve as a starting point for discussion with international stakeholders in the field of diabetes.
Project description:<h4>Introduction</h4>Analytical quality is an essential requirement for best practice in any medical laboratory. Lack of a harmonized approach for sigma calculation is considered an obstacle in the objective comparability of analytical performance among laboratories adopting sigma metrics. It is urgently needed that all laboratory professionals interested in the analytical quality to work hard towards harmonization protocol for sigma calculation in order to properly select their analytical goals. This study aims at harmonization of Sigma metrics calculation in four accredited Egyptian laboratories.<h4>Materials and methods</h4>This observational cross sectional study compared the sigma levels for certain biochemical parameters in the four participating laboratories.<h4>Results</h4>Coefficient of variation (CV) and bias were determined for some biochemical analytes, data assayed by different automated analysers in the four different accredited laboratories. The sigma level for the four medical laboratories was calculated for each biomedical parameter with changed sigma level after total allowable error (Tea) unification among participating laboratories.<h4>Conclusion</h4>Each laboratory should select the TEa goal based on clear standardized criteria of selection without any subjective preferences as either under or over estimation of Sigma metrics will affect the patient centred care negatively if laboratories use quality control procedures wrongly based on incorrect Sigma metrics calculation with subsequent misleading medical decisions.
Project description:INTRODUCTION:Sigma metrics were applied to evaluate the performance of 20 routine chemistry assays, and individual quality control criteria were established based on the sigma values of different assays. METHODS:Precisions were expressed as the average coefficient variations (CVs) of long-term two-level chemistry controls. The biases of the 20 assays were obtained from the results of trueness programs organized by National Center for Clinical Laboratories (NCCL, China) in 2016. Four different allowable total error (TEa) targets were chosen from biological variation (minimum, desirable, optimal), Clinical Laboratory Improvements Amendments (CLIA, US), Analytical Quality Specification for Routine Analytes in Clinical Chemistry (WS/T 403-2012, China) and the National Cholesterol Education Program (NECP). RESULTS:The sigma values from different TEa targets varied. The TEa targets for ALT, AMY, Ca, CHOL, CK, Crea, GGT, K, LDH, Mg, Na, TG, TP, UA and Urea were chosen from WS/T 403-2012; the targets for ALP, AST and GLU were chosen from CLIA; the target for K was chosen from desirable biological variation; and the targets for HDL and LDL were chosen from the NECP. Individual quality criteria were established based on different sigma values. CONCLUSIONS:Sigma metrics are an optimal tool to evaluate the performance of different assays. An assay with a high value could use a simple internal quality control rule, while an assay with a low value should be monitored strictly.
Project description:<h4>Background</h4>Unreliable and ingenuine results issued by clinical laboratories have serious consequences for the patients. Sigma metrics is a standardized tool for Quality assessment for test performance in a laboratory.<h4>Objective</h4>To evaluate the performance of routine biochemistry laboratory at MMIMSR, Mullana in terms of Sigma metrics and Quality Goal Index.<h4>Material and methods</h4>This cross sectional study evaluated performance of 14 routine chemistry parameters using retrospective Internal Quality Control data of two levels on Siemens Dimension Rxl from Feb to Jul 2019 for CV% and EQAS reports from CMC, Vellore for Bias%. Sigma metrics was calculated using total allowable error targets as per CLIA and Biological Variability database guidelines.<h4>Results</h4>For level-2 IQC; TG, Chol, ALP showed excellent performance with ? ?> ?6 while ? ?< ?3 was observed for AST, Total Protein, Glucose, BUN and ALT using CLIA guidelines while in IQC Level-3 poor performers were only BUN and ALT with Ca, TG and Chol showing ? ?> ?6. Further by using Biological Variability data guidelines; 10 parameters of IQC Level-2 and 5 of IQC level-3 were poor performers with ? ?< ?3.<h4>Conclusion</h4>Sigma metrics is an excellent tool for performance analysis of tests performed in a clinical laboratory. Lack of precision in terms of CV% was seen for majority of the poor performers. Total allowable error targets using Biological Variability data revealed ? ?< ?3 for 10 parameters while using CLIA guidelines ? ?< ?3 was seen for only 5 parameters of IQC level-2.
Project description:Six Sigma involves a structured process improvement strategy that places processes on a pathway to continued improvement. The data presented here summarizes a project that took three clinical laboratories from autoverification processes that allowed between about 40% to 60% of tests being auto-verified to more than 90% of tests and samples auto-verified. The project schedule, metrics and targets, a description of the previous system and detailed information on the changes made to achieve greater than 90% auto-verification is presented for this Six Sigma DMAIC (Design, Measure, Analyze, Improve, Control) process improvement project.
Project description:Nonenzymatic glycation (NEG) of human hemoglobin (Hb A) consists of initial non covalent, reversible steps involving glucose and amino acid residues, which may also involve effector reagent(s) in the formation of labile Hb A1c (the conjugate acid of the Schiff base). Labile Hb A1c can then undergo slow, largely irreversible, formation of stable Hb A1c (the Amadori product). Stable Hb A1c is measured to assess diabetic progression after labile Hb A1c removal. This study aimed to increase the understanding of the distinctions between labile and stable Hb A1c from a mechanistic perspective in the presence of 2,3-bisphosphoglycerate (2,3-BPG). 2,3-Bisphosphoglycerate is an effector reagent that reversibly binds in the Hb A1c pocket and modestly enhances overall NEG rate. The deprotonation of C2 on labile Hb A1c in the formation of the Amadori product was previously proposed to be rate-limiting. Computational chemistry was used here to identify the mechanism(s) by which 2,3-BPG facilitates the deprotonation of C2 on labile Hb A1c. 2,3-Bisphosphoglycerate is capable of abstracting protons on C2 and the ?-nitrogen of labile Hb A1c and can also deprotonate water and/or amino acid residues, therefore preparing these secondary reagents to deprotonate labile Hb A1c. Parallel reactions not leading to an Amadori product were found that include formation of the neutral Schiff base, dissociation of glucose from the protein, and cyclic glycosylamine formation. These heretofore under appreciated parallel reactions may help explain both the selective removal of labile from stable Hb A1c and the slow rate of NEG.
Project description:<h4>Background</h4>(a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (?) model. (b) To redesign quality control strategies for performance improvement.<h4>Methods</h4>The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re-evaluated in September 2018 after a series of targeted corrective actions.<h4>Results</h4>Based on the initial sigma values, two analytes (FT3 and TSH) with ? > 6, only needed one QC rule (1<sub>3S</sub> ) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with ? < 4 at one QC material level or both needed multiple rules (1<sub>3S</sub> /2<sub>2S</sub> /R<sub>4S</sub> /4<sub>1S</sub> /10<sub>X</sub> ) with N6 and R10-500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re-evaluated sigma metrics of all the analytes increased significantly.<h4>Conclusions</h4>It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.
Project description:This paper describes and compares methods and analyzers used to measure hemoglobin (Hb) in clinical laboratories and field settings. We conducted a literature review for methods used to measure Hb in clinical laboratories and field settings. We described methods to measure Hb and factors influencing results. Automated hematology analyzer (AHA) was reference for all Hb comparisons using evaluation criteria of ±7% set by College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA). Capillary fingerprick blood usually produces higher Hb concentrations compared with venous blood. Individual drops produced lower concentrations than pooled capillary blood. Compared with the AHA: (1) overall cyanmethemoglobin (1.0-8.0 g/L), WHO Colour Scale (0.5-10.0 g/L), paper-based devices (5.0-7.0 g/L), HemoCue® Hb-201 (1.0-16.0 g/L) and Hb-301 (0.5-6.0 g/L), and Masimo Pronto® (0.3-14.0 g/L) overestimated concentrations; (2) Masimo Radical®-7 both under- and overestimated concentrations (0.3-104.0 g/L); and (3) other methods underestimated concentrations (2.0-16.0 g/L). Most mean concentration comparisons varied less than ±7% of the reference. Hb measurements are influenced by several analytical factors. With few exceptions, mean concentration bias was within ±7%, suggesting acceptable performance. Appropriate, high-quality methods in all settings are necessary to ensure the accuracy of Hb measurements.This paper describes and compares methods and analyzers used to measure hemoglobin (Hb) in clinical laboratories and field settings. With few exceptions, mean concentration bias was within ±7%, suggesting acceptable performance. Appropriate, high-quality methods in all settings are necessary to ensure the accuracy of Hb measurements.
Project description:OBJECTIVE:Observational studies show that higher hemoglobin A1c (A1C) predicts coronary artery disease (CAD). It remains unclear whether this association is driven entirely by glycemia. We used Mendelian randomization (MR) to test whether A1C is causally associated with CAD through glycemic and/or nonglycemic factors. RESEARCH DESIGN AND METHODS:To examine the association of A1C with CAD, we selected 50 A1C-associated variants (log10 Bayes factor ?6) from an A1C genome-wide association study (GWAS; n = 159,940) and performed an inverse-variance weighted average of variant-specific causal estimates from CAD GWAS data (CARDIoGRAMplusC4D; 60,801 CAD case subjects/123,504 control subjects). We then replicated results in UK Biobank (18,915 CAD case subjects/455,971 control subjects) and meta-analyzed all results. Next, we conducted analyses using two subsets of variants, 16 variants associated with glycemic measures (fasting or 2-h glucose) and 20 variants associated with erythrocyte indices (e.g., hemoglobin [Hb]) but not glycemic measures. In additional MR analyses, we tested the association of Hb with A1C and CAD. RESULTS:Genetically increased A1C was associated with higher CAD risk (odds ratio [OR] 1.61 [95% CI 1.40, 1.84] per %-unit, P = 6.9 × 10-12). Higher A1C was associated with increased CAD risk when using only glycemic variants (OR 2.23 [1.73, 2.89], P = 1.0 × 10-9) and when using only erythrocytic variants (OR 1.30 [1.08, 1.57], P = 0.006). Genetically decreased Hb, with concomitantly decreased mean corpuscular volume, was associated with higher A1C (0.30 [0.27, 0.33] %-unit, P = 2.9 × 10-6) per g/dL and higher CAD risk (OR 1.19 [1.04, 1.37], P = 0.02). CONCLUSIONS:Genetic evidence supports a causal link between higher A1C and higher CAD risk. This relationship is driven not only by glycemic but also by erythrocytic, glycemia-independent factors.
Project description:BACKGROUND:Emergency surgery or transarterial embolization (TAE) are options for the treatment of recurrent or refractory nonvariceal upper gastrointestinal bleeding. Surgery has the disadvantage of high rates of postoperative morbidity and mortality. Embolization has become more available and has the advantage of avoiding laparotomy in this often unfit and elderly population. OBJECTIVE:To carry out a systematic review and meta-analysis of all studies that have directly compared TAE with emergency surgery in the treatment of major upper gastrointestinal bleeding that has failed therapeutic upper gastrointestinal endoscopy. METHODS:A literature search of Ovid MEDLINE, Embase, and Google Scholar was performed. The primary outcomes were all-cause mortality and rates of rebleeding. The secondary outcomes were length of stay and postoperative complications. RESULTS:A total of nine studies with 711 patients (347 who had embolization and 364 who had surgery) were analyzed. Patients in the TAE group were more likely to have ischemic heart disease (odds ratio [OR] =1.99; 95% confidence interval [CI]: 1.33, 2.98; P=0.0008; I (2)=67% [random effects model]) and be coagulopathic (pooled OR =2.23; 95% CI: 1.29, 3.87; P=0.004; I (2)=33% [fixed effects model]). Compared with TAE, surgery was associated with a lower risk of rebleeding (OR =0.41; 95% CI: 0.22, 0.77; P<0.0001; I (2)=55% [random effects]). There was no difference in mortality (OR =0.70; 95% CI: 0.48, 1.02; P=0.06; I (2)=44% [fixed effects]) between TAE and surgery. CONCLUSION:When compared with surgery, TAE had a significant increased risk of rebleeding rates after TAE; however, there were no differences in mortality rates. These findings are subject to multiple sources of bias due to poor quality studies. These findings support the need for a well-designed clinical trial to ascertain which technique is superior.
Project description:High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived sigma-scaled electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values from sigma-scaled electron density maps into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license.