Analytical Performance Evaluation of Hematology Analyzer Using Various TEa Sources and Sigma Metrics (original) (raw)
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Analytical performance evaluation of Hematology analyzers using Sigma metrics
Introduction: In clinical laboratory, the performance of the hematology analyzers should be checked routinely to ensure that the desired quality is achieved. Therefore, the aim of the study was to assess the performance of hematology analyzers using sigma metrics. Method The study included all daily internal quality control data of hematology analyzer prospectively from August to October 2022. Data was collected by trained laboratory professionals using record formats. The sigma values of each CBC parameter were calculated using the formula: Sigma = (TEa – Bias) / CV. The data of TEa were adopted from five different guidelines. The bias of all complete blood count parameters was calculated from the laboratory mean of the daily IQC data and the target value of the manufacturer in the insert kit. A coefficient of variation was also calculated using IQC data. Results The current study found an inconsistent sigma value, based on sources TEa. Except HCT out 5 parameters included based on...
Using Six Sigma to Evaluate Analytical Performance of Hematology Analyzer
INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY
Background: Many medical decisions in hospital are based on hematology examination results. We must be aware about their method performance. Sigma-metric is an excellent way to evaluate analytical performance quality. We will display the performance of our laboratory hematology analyzer, Cell Dyne Ruby, by sigma-metric analysis.Method: sigma analysis was calculated by a formula, sigma = (TEa – CV)/ Bias. Total Error Allowable (TEa) was specified by the CLIA proficiency testing criteria. The coefficien of variant (CV) and bias data were supplied from analyzer running three levels of control low (L), normal (N), and high (H) include following analytes: hemoglobin (Hb), Red Blood Cell count (RBC), Hematocrit (HCT), White Blood Cell count (WBC), and Platelet count (PLT).Results : Sigma value as follows Hb(L:4,33 N:6,68 H:2,62), RBC(L:3,43 N:3,84 H:3,46), HCT(L:2,52 N:1,73 H:2,27), WBC (L:7,14 N:8,44 H:6,38),and PLT (L:2,46 N:8,75 H:7,84). Average sigma value for all parameters were 4...
Indian Journal of Clinical Biochemistry, 2016
Sigma is a metric that quantifies the performance of a process as a rate of Defects-Per-Million opportunities. In clinical laboratories, sigma metric analysis is used to assess the performance of laboratory process system. Sigma metric is also used as a quality management strategy for a laboratory process to improve the quality by addressing the errors after identification. The aim of this study is to evaluate the errors in quality control of analytical phase of laboratory system by sigma metric. For this purpose sigma metric analysis was done for analytes using the internal and external quality control as quality indicators. Results of sigma metric analysis were used to identify the gaps and need for modification in the strategy of laboratory quality control procedure. Sigma metric was calculated for quality control program of ten clinical chemistry analytes including glucose, chloride, cholesterol, triglyceride, HDL, albumin, direct bilirubin, total bilirubin, protein and creatinine, at two control levels. To calculate the sigma metric imprecision and bias was calculated with internal and external quality control data, respectively. The minimum acceptable performance was considered as 3 sigma. Westgard sigma rules were applied to customize the quality control procedure. Sigma level was found acceptable (C3) for glucose (L2), cholesterol, triglyceride, HDL, direct bilirubin and creatinine at both levels of control. For rest of the analytes sigma metric was found\3. The lowest value for sigma was found for chloride (1.1) at L2. The highest value of sigma was found for creatinine (10.1) at L3. HDL was found with the highest sigma values at both control levels (8.8 and 8.0 at L2 and L3, respectively). We conclude that analytes with the sigma value \3 are required strict monitoring and modification in quality control procedure. In this study application of sigma rules provided us the practical solution for improved and focused design of QC procedure.
Journal of drug delivery and therapeutics, 2024
In this study, analyzed HbA1C over a period of 6 months. Six Sigma improves the quality of process outputs by analyzing and eliminating the source of defects and reducing variability in manufacturing and business practices. In terms of clinical laboratory, the identification of test with low Sigma values (< 3σ) indicate that actions should be taken to improve analytic quality or the laboratory should use alternate methods and reagents. Our study showed methodologies for HbA1C is of world class performance achieving Sigma value ˃6, to maintain and improve this frequency of QC should be run as rule as per the westguard QC rule. Sigma metrics helps to assess analytical methodologies and augment laboratory performance. It acts as a guide for planning quality control strategy. It can be a self-assessment tool regarding the functioning of clinical laboratory. Using Six Sigma techniques, able to identify problem areas as well as recurring issues that affect the overall quality expectation of laboratory result.
Indian Journal of Medical Biochemistry
Introduction and aim: Internal and external quality control (IQC and EQC) is used to monitor and evaluate the analytical process. Six Sigma provides an objective assessment of performance. The Sigma metrics (σ) are calculated using the coefficient of variation (CV), bias, and total allowable error (TEa). One of the pitfalls of the Sigma metrics calculation is that it depends upon the source of the variables used in the formula and the measurand matrix. Hence, this study was conducted to calculate the Sigma metrics of urea, creatinine, Na, and K in serum and urine using Tea from biological variation (BV) (urine and serum) and Clinical Laboratory Improvement Amendments (CLIA) (serum) and subsequently comparing the Sigma metrics of all four analytes using TEa from BV between serum and urine control and using TEa from BV in the same matrix (serum). Materials and methods: A cross-sectional study was conducted in the Department of Clinical Biochemistry, St. John's Medical College for 1 year (January-December 2018). Bio-Rad IQC (serum and urine) data have been used to calculate σ of urea, creatinine, Na, and K. The cumulative CV and bias were obtained using unity real-time software from Bio-Rad Laboratories. Total allowable error values were obtained from BV and CLIA guidelines. Results: Urea, creatinine, Na, and K showed higher σ in the urine control than in serum controls indicating the better performance of these parameters in the urine matrix than in serum. In the same matrix (serum control), creatinine, Na, and K had higher σ using TEa from CLIA than TEa from BV. Na showed the highest difference in σ value between the two sources (p-value < 0.001). However, serum urea showed higher σ using TEa from BV compared to TEa from CLIA. Conclusion: Our study showed that σ varies with the matrix; henceforth, one should be careful in extrapolating the performance characteristics in terms of Sigma of an analyte from one matrix to another. In the same matrix, σ also varies depending on the source of TEa used in the calculation. It is, thus, essential to mention the source of the variables used to calculate σ for a better interpretation.
Indian Journal of Clinical Biochemistry, 2020
Variability in analytical performance of some analyte indicated the need of evaluation of quality plan of our laboratory. We tried to put the same degree of effort into our quality metrics as we put into the laboratory processes themselves. Application of six sigma methodologies improve the quality by focusing on the root causes of the problems in performance and analyzing by flowcharts, fishbone diagrams and other quality tools. Sigma metric was calculated for laboratory parameters for a period of 8 months during 2018-19. The analytes with poor sigma metric were free Thyroxine (FT3, FT4), Sodium, Calcium and Magnesium. Sigma metric of free Thyroxine (FT3, FT4), Sodium, Calcium and Magnesium were below 3. A road map for process improvement was designed with DMAIC (Define-Measure-Analyze-Improve-Control) model to solve the issue. Possible causes for low analytical performance of the particular analytes were depicted in Fishbone diagram. The Fishbone analysis identified the water quality issues with electrolyte analysis while high ambient temperature was culprit for poor assay performance of free Thyroxine. Sigma metric of the analytical performance was assessed once again after root cause analysis. Sigmametric showed marked improvement in control phase. Identification of problems led to reduction in non value added work leading to adequate resource utilization by addressing the priority issue. Therefore, DMAIC tool with Fish bone model analysis can be recommended as a well suited method for troubleshooting in poor performance of laboratory parameter.
Evaluation of Analytical Performance of Variant II Turbo Hba 1 C Analyzer According to Sigma
2018
Background: Hemoglobin A1c, (HbA1c) which is the major constituent of glycated hemoglobin, has been used in the follow-up of retrospective glycemia for years and in the diagnosis of diabetes mellitus nowadays. Since the analytical performance of HbA1c should be high likewise all laboratory tests, various quality control measures are used. Sigma metrics is one of these measures and it is the combination of bias, precision and total allowable error that ensures a general evaluation of analytical quality. The aim of our study was to evaluate the analytical performance of Bio-Rad’s Variant Turbo II HbA1c analyzer according to sigma metrics. Methods: Sigma levels were calculated using the data obtained from two levels of internal and 12 external quality control materials (Bio-Rad) of Variant II Turbo HbA1c analyzer according to s= (TEa% Bias%) / CV% formula. Results: The mean sigma levels for low and high quality control materials were found to be 3.0 and 4.1, respectively. Conclusions: ...
Background: To assess the analytical performance of quality trough external quality assesses and internal quality program data on sigma scale. Method and material: Imprecision was determined from the cumulative Levey-jenning SD over the 6 month, bias was calculated from the external quality records, Finally, analytical sigma metric estimates were calculated for each Analytes by the following equation: sigma metric: (TEa – Bias)/CV. All function and statistical analysis were done in our Private laboratories. Result: The sigma value >6 was observed for most analytes. Some of analytes have poor sigma metric <3 such as Creatinine and ALP in normal level and Calcium in pathologic level. Glucose, Urea, Uric Acid, Calcium, Phosphorous, total bilirubin, in normal levels and Urea, Creatinine, total and direct bilirubin in pathologic level have intermediate sigma metric 4-6. Conclusion: Chemistry tests are not commodities. Quality varies significantly from manufactures to manufactures and method to method. The sigma-assessment from multiple EQA/IQC programs provides more insight into the performance of methods and quality. Laboratory seeking optimal quality program would do well to consult this data as part of their decision-working process.
Evaluation of Analytical Performance of Variant II Turbo HbA1c Analyzer According to Sigma Metrics
Journal of Medical Biochemistry
Summary Background: Hemoglobin A1c, (HbA1c) which is the major constituent of glycated hemoglobin, has been used in the follow-up of retrospective glycemia for years and in the diagnosis of diabetes mellitus nowadays. Since the analytical performance of HbA1c should be high likewise all laboratory tests, various quality control measures are used. Sigma metrics is one of these measures and it is the combination of bias, precision and total allowable error that ensures a general evaluation of analytical quality. The aim of our study was to evaluate the analytical performance of Bio-Rad’s Variant Turbo II HbA1c analyzer according to sigma metrics. Methods: Sigma levels were calculated using the data obtained from two levels of internal and 12 external quality control materials (Bio-Rad) of Variant II Turbo HbA1c analyzer according to s= (TEa% - Bias%) / CV% formula. Results: The mean sigma levels for low and high quality control materials were found to be 3.0 and 4.1, respectively. Con...