Defining allowable total error limits in the clinical laboratory.

Allowable total error (ATE) are performance specification limits predefined for a variety of laboratory analytes. These limits define the maximum amount of error that is allowed for an assay when judging acceptability of a new assay during method verification/validation, evaluating patient or instrument comparison data, or in designing a quality control strategy. There are several widely available resources and models that can serve as a guide in selecting ATE. They may be based on legal requirements or set by providers of proficiency testing (PT) and external quality assessment schemes (EQAS). ATE can be also determined by professional expert groups or be based on biological variation of an analyte. Because there are several resources to choose from, there have been several attempts in reaching consensus on which ATE resource should be given preference. This chapter reviews several of these resources in more detail and discusses the difference between allowable total error (ATE) and observed total analytical error (TAE).

Total error in lymphocyte subpopulations by flow cytometry-based in state of the art using Spanish EQAS data.

OBJECTIVES: Flow cytometry analyses of lymphocyte subpopulations (T, B, NK) are crucial for enhancing clinical algorithms and research workflows. Estimating the total error (TE) values for the percentage and absolute number of lymphocyte subpopulations using the state-of-the-art (SOTA) approach with real data from an external proficiency testing (EPT) scheme was performed. A comparison with previously published Biological Variability (BV)-based specifications was carried out. METHODS: A total of 44,998 results from 86 laboratories over 10 years were analysed and divided into two five-year periods (2012-2016) and (2017-2021). Data come from the IC-1 Lymphocytes scheme of the Spanish External Quality Assurance System (EQAS) GECLID Program. This quantitative scheme includes percentages and absolute numbers of CD3+, CD3+CD4+, CD3+CD8+, CD19+, and CD3-CD56+CD16+ NK cells. The percentage of TE was calculated as: |reported value - robust mean|*100/robust mean for each laboratory and parameter. The cut-off for TE is set at 80 % best results of the laboratories. RESULTS: A significant reduction in the SOTA-based TE for all lymphocyte subpopulations in 2017-2021 was observed compared to 2012-2016. The SOTA-based TE fulfils the minimum BV-based TE for percentages of lymphocyte subpopulations. The parameter with the best analytical performance calculated with SOTA (2017-2021 period)-based TE was the percentage of CD3+ (TE=3.65 %). CONCLUSIONS: The values of SOTA-based specifications from external quality assurance program data are consistent and can be used to develop technical specifications. The technological improvement, quality commitment, standardization, and training, reduce TE. An update of TE every five years is therefore recommended. TE assessment in lymphocyte subsets is a helpful and reliable tool to improve laboratory performance and data-based decision-making trust.

Effects of time delay and blood storage methods on analysis of canine venous blood samples with an Element point-of-care analyzer.

BACKGROUND: Manufacturers of point-of-care (POC) analyzers recommend immediate processing and anaerobic collection of blood samples. However, it is not uncommon for clinical scenarios to result in delayed sample processing or room air exposure that could impact the test results. OBJECTIVE: To investigate the effect of time delay and sample storage method on key POC analytes in canine venous blood samples processed with an Element POC analyzer. METHODS: Blood gas analysis was performed on venous blood samples at times 0 (T0), 15, 30, and 60 minutes after sampling using three different storage methods: preheparinized plastic syringes and two different lithium heparin tubes. To determine clinical relevance, results were compared with allowable total error of the respective parameter. Significance was set at P < 0.05. RESULTS: Significant differences between the three storage methods at baseline were found for partial pressure of carbon dioxide (PCO2 ), partial pressure of oxygen (PO2 ), base excess, and total hemoglobin. No significant differences up to T60 were found within collection methods for actual bicarbonate (HCO3 - ), base excess, sodium, potassium, chloride, ionized calcium (iCa), glucose, and BUN. Significant differences within collection methods were found after T0 for creatinine, after 15 minutes for lactate, and after 30 minutes for pH and hematocrit. No significant differences were found for PO2 in samples stored in preheparinized plastic syringes at any time point. CONCLUSIONS: These results suggest that HCO3 - , sodium, potassium, chloride, iCa, glucose, and BUN are comparable within the three storage methods for up to 60 minutes after sampling without resulting in clinically relevant changes.

Study on performance specifications of 34 routine chemistry analytes in China / 中华检验医学杂志

Objective:The allowable total error ( TEa),allowable imprecision ( CV)and allowable bias( Bias)were recommended for 34 routine chemistry analytes in China. Methods:According to the performance specification setting mode newly determined at the Milan conference in Italy,the performance specification was derived based on components biological variation (BV)and current state of the art mode. The data(including EQA data and IQC data)of laboratories participating in the routine chemistry and lipids and lipoproteins EQA activities of the national center for clinical laboratories from 2019 to 2021 was collected through clinet-EQA. For the analytes with biological variation(BV)data,compared the'percentage difference′ of EQA data and the'in-control coefficient of variation of the month′ of IQC data of each research analyte with the three levels evaluation criteria derived based on BV,and calculated the percentage difference passing rate and CV passing rate of all batches in each year. When the passing rate reaches 80%,the performance specifications of this level met the requirements of the recommended performance specifications of the analyte. For the analytes without BV data or analytes whose performance specifications at three levels derived based on BV could not be used as recommended standards,the recommended performance specifications are derived based on the current state of the art. After obtaining the recommended TEa and allowable CV for each analyte,used the formula | Bias|≤ TEa-z? CV to derive the recommended allowable bias. Results:The results of TEa ( CV)% recommended by 34 analytes are as follows:K4.7(2),Na4(1.5),Cl4(1.4),Ca5(2),P9.6(3.9),Glu6.4(2.5),Urea8(3),UA12(4.1),Cre11(3.3),TP5(2),Alb5.2(2.4),TC8.6(2.7),TG13.5(5),HDL-C16.5(4.3),LDL-C20.5(6.2),ApoAⅠ16(5.3),ApoB 17.1(5.5),Lp(a) 24.1(10.4),TBil 12.4(5),DBil 20(7.3),ALT16(5),AST13.5(4.8),ALP17.5(4.8),AMY13.1(3.3),CK11.3(3.8),LDH11(3.9),CHE13.4(5.3),LIP20(6.9),Fe13.3(5.2),Mg14(4.5),Cu17.9(6.8),Zn15.1(6.4),γ-GGT10(3.3),α-HBDH18(5.8).The formula | Bias|≤ TEa-z? CV is used to derive the allowable bias of 34 analytes. Conclusions:For 34 clinical routine chemistry quantitative analytes,the allowable total error,allowable imprecision and allowable bias that meet the current state of the art of Chinese laboratories are recommended.

Application of Sigma metrics in the quality control strategies of immunology and protein analytes.

BACKGROUND: Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma. METHODS: Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C-reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values. RESULTS: While using the TEaNCCL , 90% analytes had a world-class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL , Sigma Method Decision Charts were constructed. For Cys C, five multi-rules (13s /22s /R4s /41s /6X , N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s , N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12). CONCLUSIONS: The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.

Evaluation of 20 clinical chemistry and 12 immunoassay analytes in terms of total analytical error and measurement uncertainty.

In analytical quality management, target setting models that are selected by the purpose together with the error models that are applied correctly have critical importance. In our study, we aimed to compare the analytical performance characteristics of routine clinical chemistry and immunoassay tests with different target-setting models proposed by various organizations. Our study was performed with internal and external quality control data obtained using Beckman Coulter AU680 for clinical chemistry analytes and Roche Cobas 8000 autoanalyzer for immunoassay analytes. The total analytical error (TAE) was calculated by the formula TAH%=1.65×(CV%)+Bias%. Measurement uncertainty (MU) has been calculated adhering to the Nordtest guideline. Results were compared with BVEFLM, CLIA, RCPA, PRDEQA%, pUEQAS%, and permissible MU (pU%) data to investigate analytical performance qualities. When we compare the results of TAE and MU, MU was found to be higher than TAE for all analytes. ALT, AST, glucose, K, and triglycerides met all target values, showing the best performance. Taken together, our results show that CLIA for total analytical error and PRDEQA% and pUEQAS% for measurement uncertainty can match better than BVEFLM, RCPA and pU%. These test results should be evaluated with measurement uncertainty to avoid misdiagnosis. Appropriate specification limits should be defined for the examination of test methods that meet the objectives for fitness for clinical purposes.

Establishing allowable total error for serum total folate in external quality assessment / 中华检验医学杂志

Objective:To establish the allowable total error (TEa) of the national external quality assessment (EQA) program in line with the current quality level of serum folate measurement in China.Methods:The data of serum total folate test in the clinical laboratory of a hospital in Beijing in 2016 were collected, and the Stata SE 15 software was used for Monte Carlo simulation to obtain the false-negative rate under different bias and inaccuracy conditions. The Origin Pro 9.1 software was used to make the contour figure. The TEa of serum total folate test is derived based on the acceptable false-negative rate. National EQA data of serum total folate in 2020 were collected to calculate the pass rate of participating laboratories and the laboratory pass rate of quality control products at each level under the five TEa derived from the analysis performance on clinical outcomes, biological variation, and the evaluation criterion of national EQA.Results:Based on the influence of analytical performance on clinical outcomes, the TEa was 10%. Under this TEa, the pass rate of the first EQA program of serum total folate in 2020 was more than 80%, and the pass rate of the second time was 73.1%. Under the minimum (46.57%) and appropriate level of TEa (15.52%) derived from biological variation and national EQA evaluation criterion, the pass rate of serum total folate in the two EQA programs in 2020 exceeded 85%.Conclusion:The analytical performance of serum total folate in China cannot meet the requirements of TEa derived based on the effect of analytical performance on clinical outcomes. An appropriate level of TEa derived based on biological variation (15.52%) is suggested as the recommended criterion for the TEa of serum total folate test.

Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing-based approach and an internal quality control data inter-laboratory comparison-based approach.

INTRODUCTION: Two methods were compared for evaluating the sigma metrics of clinical biochemistry tests using two different allowable total error (TEa) specifications. MATERIALS AND METHODS: The imprecision (CV%) and bias (bias%) of 19 clinical biochemistry analytes were calculated using a trueness verification proficiency testing (TPT)-based approach and an internal quality control data inter-laboratory comparison (IQC)-based approach, respectively. Two sources of total allowable error (TEa), the Clinical Laboratory Improvement Amendments of 1988 (CLIA '88) and the People's Republic of China Health Industry Standard (WS/T 403-2012), were used to calculate the sigma metrics (σCLIA, σWS/T ). Sigma metrics were calculated to provide a single value for assessing the quality of each test based on a single concentration level. RESULTS: For both approaches, σCLIA  > σWS/T in 18 out of 19 assays. For the TPT-based approach, 16 assays showed σCLIA  > 3, and 12 assays showed σWS/T  > 3. For the IQC-based approach, 19 and 16 assays showed σCLIA  > 3 and σWS/T  > 3, respectively. CONCLUSIONS: Both methods can be used as references for calculating sigma metrics and designing QC schedules in clinical laboratories. Sigma metrics should be evaluated comprehensively by different approaches.

Evaluation of automated erythrocyte methodology in new world camelids using the ADVIA 2120 hematology analyzer.

BACKGROUND: Accurate erythrocyte measurements with ADVIA hematology analyzers require isovolumetric cell sphering in one reaction and hemolysis in another. However, camelid erythrocytes are resistant to sphering and osmotic lysis, and no published evaluation of ADVIA methods for camelids exists. OBJECTIVES: The objectives were to demonstrate whether camelid erythrocytes sphere in the ADVIA red blood cell/platelet (RBC/PLT) reagent and lyse in the ADVIA cyanide HGB reagent, and to determine optimal ADVIA settings for camelids. METHODS: Camelid and canine blood were diluted to 1:625 in RBC/PLT reagent and evaluated microscopically for erythrocyte sphering. A camelid sample was incubated with the hemoglobin (HGB) reagent at varying dilutions to evaluate hemolysis. The RBC, hematocrit (HCT), mean cell volume (MCV), and mean corpuscular hemoglobin concentration (MCHC) using three ADVIA species settings (equine, bovine, and caprine) were compared to their respective reference methods: Z2 Coulter impedance counter, packed cell volume, calculated MCV (PCV × 10/Coulter RBC), and calculated MCHC (HGB × 100/PCV). Reference MCV was also compared to MCV calculated using the ADVIA equine RBC count. Comparisons were assessed using Passing-Bablok regression and Bland-Altman difference plots. RESULTS: Camelid erythrocytes did not sphere in the RBC/PLT reagent, but did lyse in the HGB reagent. The ADVIA equine setting RBC count was acceptably close to the Coulter count. Hematocrit, MCV, and MCHC from all settings were significantly different from the reference methods. Mean cell volumes calculated using the equine setting RBC counts were acceptably close to the reference MCV. CONCLUSIONS: Camelid ADVIA erythrogram results should be reported as follows: RBC counts and HGB concentrations using the equine setting, spun PCVs, MCVs calculated using the PCV and equine setting RBC, and MCHCs calculated using the PCV and equine setting HGB.

Application of Six Sigma for evaluating the analytical quality of tumor marker assays.

CONTEXT: The results of detection assays for the same specimen are usually quite different in different laboratories or when tested with different detection systems. OBJECTIVE: This study was designed to investigate the value of applying sigma metrics derived from different standards for allowable total error (TEa) in evaluating the analytical quality of tumor marker assays. METHODS: Assays were evaluated for these six tumor markers: total prostate-specific antigen (tPSA), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA199), carbohydrate antigen 125 (CA125), and carbohydrate antigen 153 (CA153). Sigma values were calculated for two concentrations of quality control products to assess differences in quality of tumor marker assays. Improvement measures were recommended according to the quality goal index, and appropriate quality control rules were selected according to the sigma value. RESULTS: The sigma value was highest using the higher biological variation-derived "appropriate" TEa standard: it was sigma ≥6 or higher in 16.7% of tumor markers. Sigma was below 6 for all tumor markers using the other three TEa. CEA, AFP, CA199, CA125, and CA153 required improved precision. The marker tPSA required improve precision and accuracy. According to sigma values by using China's external quality assessment standards, CEA, AFP, CA125, and CA153 require 13s /22s /R4s /41s multirules for internal quality control, CA199 requires use of 13s /22s /R4s /41s /8x multirules, and tPSA requires maximum quality control rules. CONCLUSION: Six Sigma is useful for evaluating performance of tumor markers assays and has important application value in the quality control of these assays.

Mistaken assumptions drive new Six Sigma model off the road.

Oosterhuis and Coskun recently proposed a new model for applying the Six Sigma concept to laboratory measurement processes. In criticizing the conventional Six Sigma model, the authors misinterpret the industrial basis for Six Sigma and mixup the Six Sigma "counting methodology" with the "variation methodology", thus many later attributions, conclusions, and recommendations are also mistaken. Although the authors attempt to justify the new model based on industrial principles, they ignore the fundamental relationship between Six Sigma and the process capability indices. The proposed model, the Sigma Metric is calculated as the ratio CVI/CVA, where CVI is individual biological variation and CVA is the observed analytical imprecision. This new metric does not take bias into account, which is a major limitation for application to laboratory testing processes. Thus, the new model does not provide a valid assessment of method performance, nor a practical methodology for selecting or designing statistical quality control procedures.

Analytical quality goals-a review.

Analytical quality goals indicate how laboratory tests must perform to be clinically useful for their intended purpose. These goals have historically focused on analytical error assessment for quantitative methods and vary with measurand concentration or activity, and species. Although formalized quality goal models have been developed in human medicine, quality goals in veterinary medicine, to date, have not been formalized; use of human regulatory-based goals, consensus-based goals, or biologic variation-based goals have been reported most often. This review provides an overview of how quality goals are derived, how these may be used, and highlights challenges. Pending formal recommendations, individual veterinary laboratories should select quality goals that make the most sense clinically, logistically, and financially based on their individual needs and the needs of the clients that they serve.

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches: Electronic supplementary material available online for this article.

INTRODUCTION: Two approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory. MATERIALS AND METHODS: Sigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa - bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis. RESULTS: For the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values. CONCLUSIONS: Both methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.

Expected long-term defect rate of analytical performance in the medical laboratory: Assured Sigma versus observed Sigma.

Reliability of laboratory results is determined by the ratio of incorrect results expected in long-term. Sigma is a measure of defect ratio, therefore long-term Sigma is a measure of the reliability of laboratory results. Commonly, long-term Sigma is estimated based on the short-term Sigma. The Six Sigma methodology assumes that in long-term performances will shift up to 1.5 Sigma, and therefore the long-term Sigma is considered 1.5 Sigma less than short-term Sigma. Analytical performance in the medical laboratory is prone to shifts larger than 1.5 Sigma. Thus, the 1.5 Sigma shift assumed in the Six Sigma is not a correct estimate in the medical laboratory. On the other hand, in the medical laboratory statistical quality control procedure (SQC) is applied to detect and correct shifts. Since SQC can be planned to trap shifts of different sizes, the threshold set for SQC determines the defect rate expected for long-term.

Verification of the Heska Element Point-of-Care blood gas instrument for use with venous blood from alpacas and llamas, with determination of reference intervals.

BACKGROUND: The Heska Element POC ("EPOC") is a blood gas instrument intended for use with canine, feline, and equine whole blood; no verification for use with camelid specimens has been reported. OBJECTIVES: Using camelid specimens and commercial quality control materials (QCM), we investigatee EPOC analytical performance and establish EPOC camelid reference intervals (RIs). METHODS: Camelid blood (n = 124) was analyzed using the EPOC (pH, pCO2 , pO2 , HCO3 , base excess, SO2 , sodium, potassium, chloride, ionized calcium, TCO2 , anion gap, HCT, HGB, glucose, lactate, and creatinine); plasma was analyzed using a Roche Cobas c501 (sodium, potassium, chloride, TCO2 , anion gap, glucose, and creatinine). Method comparison data were assessed using Pearson's correlation, Passing-Bablok regression, and Bland-Altman plots. EPOC precision was evaluated using QCM and camelid blood. RESULTS: For all measurands except anion gap, the EPOC vs Cobas instrument correlation was r > .85. Except for pO2 and pCO2 , EPOC precision (QCM and blood) ranged from a repeatability CV <1%-6.3%. Mild constant bias for chloride, glucose, TCO2 , anion gap, and creatinine, and mild proportional bias for chloride, glucose, and anion gap were present. The total error (QCM data) for the EPOC instrument was below the ASVCP-recommended allowable total error. Alpacas had higher potassium and lactate, while llamas had higher creatinine, sodium, chloride, ionized calcium, pO2 , and SO2 . Statistical RIs based on alpaca (n = 74-96) and llama data (n = 12-17) are reported descriptively. CONCLUSIONS: The EPOC analyzer shows good performance with camelid blood. A lack of complete agreement with automated chemistry analyzers highlights the importance of interpreting patient data using instrument-specific RIs.

Quality specifications of routine clinical chemistry methods based on sigma metrics in performance evaluation.

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.

Measuring Analytical Quality: Total Analytical Error Versus Measurement Uncertainty.

To characterize analytical quality of a laboratory test, common practice is to estimate Total Analytical Error (TAE) which includes both imprecision and trueness (bias). The metrologic approach is to determine Measurement Uncertainty (MU), which assumes bias can be eliminated, corrected, or ignored. Resolving the differences in these concepts and approaches is currently a global issue.

Rhetoric Versus Reality? Laboratory Surveys Show Actual Practice Differs Considerably from Proposed Models and Mandated Calculations.

The scientific debate on goals, measurement uncertainty, and individualized quality control plans has diverged significantly from the reality of laboratory operation. Academic articles promoting certain approaches are being ignored; laboratories may be in compliance with new regulations, mandates, and calculations, but most of them still adhere to traditional quality management practices. Despite a considerable effort to enforce measurement uncertainty and eliminate or discredit allowable total error, laboratories continue to use these older, more practical approaches for quality management.

Total Error and Measurement Uncertainty in Clinical Laboratory Medicine / 现代检验医学杂志

In the clinical laboratory medicine,the measurement uncertainty (MU)is a relatively new concept.Over the years, experts of clinical laboratory medicine from all over the world made a great number of further researches and promote the development of MU,which led clinical laboratories to pay more and more attention to the meanings and functions of MU at the same time.However,because of the habitual using of the total error (TE)in clinical laboratories and similarities between concepts of MU and TE which easily resulted in confusion,a lot of laboratories still cannot completely accept MU.By explai-ning concepts of TE and MU and analyzing the pros and cons of models of TE and MU as well as their functions,the obj ec-tive of this paper is to help clinical laboratories make further comprehensions of TE and MU and understand how to properly use them in practice.

Application Comparison of Two Source of Allowable Total Errors inσMetrics Assessing the Analytical Quality and Selecting Quality Control Procedures for Automated Clinical Chemistry / 现代检验医学杂志

Objective To evaluate the difference of two sources of allowable total errors provided by National Health Industry Standard (WS/T 403-2012,analytical quality specification for routine analytes in clinical biochemistry)and National Stand-ard (GB/T 20470-2006,requirements of external quality assessment for clinical laboratories)in assessing the analytical qual-ity byσmetrics,and selecting quality control procedures using operational process specifications graphs.Methods Selected one of the laboratories participating in the internal quality control activity of routine chemistry of February,2014 and the first time external quality assessment activity of routine chemistry in 2014 organized by National Center for Clinical Labora-tories for its coefficient of variation and the bias of nineteen clinical chemistry tests.With the CV% and Bia%,σmetrics of controls at two analyte concentrations were calculated using two different allowable total errors targets (National Health In-dustry Standard (WS/T 403-2012)and National Standard (GB/T 20470-2006).Could obtain a operational process specifica-tions graph by which Could select quality control procedures using the Quality control computer simulat software developed by National Center for Clinical Laboratories and the company zhongchuangyida.Results The σ metrics under National Health Industry Standard (WS/T 403-2012)were from 0 to 7.Most of the values (86% and 76.2%)under National Stand-ard (GB/T 20470-2006)were from 3 to 15.On the normalized method decision chart,the assay quality using the allowable total errors targets of National Standard (GB/T 20470-2006)was at least one hierarchy more than one using National Health Industry Standard (WS/T 403-2012).The quality control rules under National Health Industry Standard (WS/T 403-2012)were obviously more strict than that under National Standard (GB/T 20470-2006).Among the control procedures using National Health Industry Standard (WS/T 403-2012),multirule (n=4):ALB,ALP,Ca,Cl,TC,Crea,Glu,LDH,K, Na,TP,TG and Urea;13s (n=4):Mg;12.5s (n=2):CK,AMY ang Fe;13s (n=2):TBIL;13.5s (n=2):ALT,AST and UA.Conclusion The allowable total errors provided by National Health Industry Standard (WS/T 403-2012)are more stringent than that from National Standard (GB/T 20470-2006).So Laboratories need to improve the analytical quality of their tests furthermore.