Optimization on reagent-loading manner for modular clinical chemistry analyzer series: simulations and verifications

2020 ◽  
Author(s):  
Mingyang Wang ◽  
Liang Ming
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Turnaround Time ◽  
Internal Quality ◽  
Laboratory System ◽  
Control Cost ◽  
Related Factors ◽  
Health Care Intervention ◽  
Loading Mode ◽  
And Performance

Abstract Background: The analytical process, especially reagent-related factors, demonstrates a significant effect on clinical chemistry analyzer. Therefore, it should have our attention both in total laboratory system (TLA) and modular automation (MA). The reagent-loading mode of modular analyzer influences testing turnaround time (tTAT) and the cost.Methods: Parameters were simulated by cobas 8000 workflow simulator for reagent-loading manner with at least three positions (Pattern 1), the module-parallel reagent-loading manner (Pattern 2) and the single-position loading mode (Pattern 3). Then, we chose Pattern 2 for Tuesday effect-based optimization verification.Results: tTAT, reagent on-line time, quality control cost and performance verification times all declined by 43%. Tuesday effect solved the repetitive problem for verification. With comprehensive comparison between Pattern 1, Pattern 2 and Pattern 3, Pattern 2 shows optimal performance in subsequent verification.Conclusions: The optimization of reagent-loading manner saved much workforce, and reduced the quality control cost and internal quality assessment consumption.Trial registration: This article did not report the results of a health care intervention on human participants. Trial registration was not needed here.

scholarly journals Optimization on reagent-loading manner for modular clinical chemistry analyzer series: simulations and verifications

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mingyang Wang ◽  
Liang Ming
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Turnaround Time ◽  
Laboratory System ◽  
Related Factors ◽  
Loading Mode ◽  
Mode Pattern ◽  
On Line ◽  
And Performance ◽  
The Cost

AbstractObjectivesThe pre- and post-analytical processes have been discussed both in total laboratory system (TLA) and modular automation (MA). The analytical process, especially reagent-related factors influences on the integrated clinical chemistry analyzer, demonstrates a significant effect on clinical chemistry analyzer. Modular analyzer reagent-loading mode influences two mainly factors, testing turnaround time (tTAT) and the cost. Furthermore, how to definite the different reagent loading manners and verify the best reagent loading manner is big challenge.MethodsWe focus on tTAT, and study how the reagent-related factors effect TAT by simulations and verifications. Parameters were simulated by cobas 8000 workflow simulator for reagent-loading manner with at least three positions (Pattern 1), the module-parallel reagent-loading manner (Pattern 2) and the single-position loading mode (Pattern 3).ResultstTAT, reagent on-line time, quality control (QC) cost and performance verification times all declined by 43%. Tuesday effect solved the repetitive problem for verification. Pattern 2 shows optimal performance in Tuesday effect-based verification.ConclusionsThe optimization of reagent-loading manner saved much workforce, and reduced the QC cost.

Applying The Best Practice For Internal Quality Control (IQC) Management, Consolidation And Review Process In Sidra Medicine Clinical Chemistry Division

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S91-S91
Author(s):  
J M Asinas
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Best Practice ◽  
Clinical Chemistry ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Allowable Error ◽  
Chemistry Division ◽  
Set Up ◽  
High Level

Abstract Introduction/Objective The management of internal quality control (IQC) in Sidra Medicine Clinical Chemistry Division has been evaluated in order to promote a more consolidated and efficient process of IQC management. The statistical data produced from Cerner QC Module are transferred to IQC review templates consisting of formulas to auto- calculate parameters such as multiple of expected QC failure frequency and desirable comparison limit between analyzers. The IQC review and documentation process using the in-house excel template requires several hours to complete, hence a faster and more efficient IQC management module is required. The main objective of this study is to improve the initial IQC management set up, work flow and review procedures and to implement Biorad Unity Real Time (URT) program to develop a more efficient IQC management system. Methods The URT software has been recently configured and implemented to consolidate and streamline IQC management. URT is built through Sidra Medicine IT Enterprise level which allows multiple users to login. IQC data are downloaded using scripts from Cerner which are filtered through Biorad Unity Connect (UC) software. Additional quality tools are also explored such as various user defined statistical reports, IQC analysis using peer reviewed total allowable error (TeA) and assignment of the most appropriate Westgard rules. Determination of sigma metrics and uncertainty of measurement is also performed using the URT application. Results The generation of any IQC report is less cumbersome and time consuming as compared with the previous process. However, some user defined formulas in the IQC templates are not found on the URT reports. The URT Levey Jennings chart are also more user friendly and directly compares the daily IQC data with Unity inter-laboratory peers enabling the production of instant and monthly reports through QCNet site when assay investigation is required and for IQC report documentation. Conclusion The combination of Cerner IQC, Unity Real-time, QCNet Inter-laboratory reports and in house IQC templates produce a high level and very detailed IQC review which effectively evaluate assay performance to assist on IQC troubleshooting and root cause analysis to be able to apply the most appropriate corrective actions.

scholarly journals Quality Control Strategy for Automated CBC: A Laboratory Point of View Deducted from an Internal Study Organised in an Emergency Laboratory

2020 ◽  
Vol 28 (1) ◽  
pp. 19-27
Author(s):  
Oana Roxana Oprea ◽  
Adina Hutanu ◽  
Oana Pavelea ◽  
David Robert Kodori ◽  
Minodora Dobreanu
Keyword(s):  
Quality Control ◽  
Total Error ◽  
Laboratory Data ◽  
Turnaround Time ◽  
Statistical Quality Control ◽  
Internal Quality ◽  
Emergency Setting ◽  
Control Plan ◽  
Analytical Phase ◽  
Total Testing Process

AbstractIntroduction: The aim of this study was to determine the performance of the total testing process of complete blood count (CBC) on two different instruments in an emergency setting of a county hospital, and to design an appropriate internal quality control plan.Materials and method: Two models of Statistical Quality Control (SQC) were evaluated on Sysmex XT-1800i and Cell-Dyne Ruby: 3 levels of commercial blood every 8 hours (N=9) and an alternative model using 3 levels every 12 hours (N=6) as shift changes. Total Error (TE) was calculated using the formula: TE=Bias%+1.65xCV%; Sigma score was calculated using the formula: Sigma=[(TEa%–Bias%]/CV%. Values for coefficient of variation (CV%) and standard deviation (SD) were obtained from laboratory data and Bias% from proficiency testing. For the pre-analytical phase Sigma score was calculated, while for post-analytical phase the turnaround time (TAT) was assessed.Results: TE for all directly measured parameters, for both instruments, had lower values than Total Error allowable (TEa). CV% for almost all parameters had lower values than CV% derived from biological variation except for platelets (PLT) at low level on Sysmex XT-1800i and red blood cells (RBC) on Cell-Dyne Ruby. Sigma score ranged from as low as 2 to 10. Sigma score for pre-analytical phase was 4.2 and turnaround time was 36 minutes on average.Conclusions: Given the performances of the total testing process implemented for CBC in our laboratory, performing the internal control after every 50 samples/batch seems to fulfill both the Health Ministry Order (HMO) 1301/2007 and International Organization for Standardization ISO 15189:2013 recommendation. All quality instruments must work together to assure better patient results and every laboratory should design its own control plan that is appropriate for better quality achievement.

Time series modeling for quality control in clinical chemistry.

1988 ◽  
Vol 34 (7) ◽  
pp. 1396-1406 ◽  
Author(s):  
L C Alwan ◽  
M G Bissell
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Control Charts ◽  
Current Practice ◽  
Clinical Chemistry ◽  
Statistical Quality Control ◽  
New Approach ◽  
Basic Assumptions ◽  
Control Rules ◽  
And Performance

Abstract Autocorrelation of clinical chemistry quality-control (Q/C) measurements causes one of the basic assumptions underlying the use of Levey-Jennings control charts to be violated and performance to be degraded. This is the requirement that the observations be statistically independent. We present a proposal for a new approach to statistical quality control that removes this difficulty. We propose to replace the current single control chart of raw Q/C data with two charts: (a) a common cause chart, representing a Box-Jenkins ARIMA time-series model of any underlying persisting nonrandomness in the process, and (b) a special cause chart of the residuals from the above model, which, being free of such persisting nonrandomness, fulfills the criteria for use of the standard Levey-Jennings plotting format and standard control rules. We provide a comparison of the performance of our proposed approach with that of current practice.

Evaluation of Quality Control in Clinical Chemistry and Hematology Using Sigma Metrics

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S80-S80
Author(s):  
Carol Njeru
Keyword(s):  
Quality Control ◽  
Blood Cell ◽  
Cell Count ◽  
Clinical Chemistry ◽  
Hematological Parameters ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Total Serum ◽  
Blood Cell Count ◽  
Method Performance

Abstract Objectives The aim of this study was to evaluate clinical chemistry and hematology laboratory performance using six sigma metrics. Methods Clinical chemistry data and hematology data were analyzed from Bungoma Referral Hospital. Five parameters from renal and liver function tests were studied over a period of 6 months (December 2016 to May 2017). Data from IQC and EQA participation were used. The analytes were plasma creatinine, aspartate transaminase (AST), alanine transaminase (ALT), total serum protein, and total and direct bilirubin. Hematology parameters, namely white blood cell count (WBC), red blood cell count (RBC), and hemoglobin (Hb) levels, were studied. Data from IQC and EQA participation were used. Sigma metrics was calculated using total allowable error as per CLIA recommendations. Bias was calculated from HUQAS EQA participation while coefficient of variation was calculated from IQC data collected during the abovementioned months. Results Clinical chemistry had sigma metrics below 3; the highest sigma value was 2.01 while the lowest sigma value was 0.85. Hematological parameters had sigma levels above 3. The highest sigma value was 7.21 while the lowest sigma value was 3.87. Only one level of sigma was below 4. Conclusion Clinical chemistry analytes had sigma levels less than 3; method performance improvement with stringent internal quality control and correct setting of control limits need to be applied. Application of sigma metrics in addition to daily internal quality control can identify analytical deficits and improvement in clinical laboratories. Most hematological parameters had sigma levels above 3. The highest sigma value was 7.21 while the lowest sigma value was 3.87. Only one level of sigma was below 4.

scholarly journals The Evaluation of Analytical Performance of Immunoassay Tests by Using Six-Sigma Method

2017 ◽  
Vol 36 (4) ◽  
pp. 301-308 ◽  
Author(s):  
Rukiye Nar ◽  
Dilek Iren Emekli
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Chemistry ◽  
Quality Measurement ◽  
Analytical Performance ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Variation Database ◽  
The Mean ◽  
Six Sigma Methodology

SummaryBackground: The Six-Sigma Methodology is a quality measurement method in order to evaluate the performance of the laboratory. In the present study, it is aimed to evaluate the analytical performance of our laboratory by using the internal quality control data of immunoassay tests and by calculating process sigma values. Methods: Biological variation database (BVD) are used for Total Allowable Error (TEa). Sigma values were determined from coefficient of variation (CV) and bias resulting from Internal Quality Control (IQC) results for 3 subsequent months. If the sigma values are ≥6, between 3 and 6, and <3, they are classified as »world-class«, »good« or »un - acceptable«, respectively. Results: A sigma value >6 was found for TPSA and TSH for the both levels of IQC for 3 months. When the sigma values were analyzed by calculating the mean of 3 months, folate, LH, PRL, TPSA, TSH and vitamin B12 were found >6. The mean sigma values of CA125, CA15-3, CA19-9, CEA, cortisol, ferritin, FSH, FT3, PTH and testosteron were >3 for 3-months. However, AFP, CA125 and FT4 produced sigma values <3 for varied months. Conclusion: When the analytical performance was evaluated according to Six-Sigma levels, it was generally found as good. It is possible to determine the test with high error probability by evaluating the fine sigma levels and the tests that must be quarded by a stringent quality control regime. In clinical chemistry laboratories, an appropriate quality control scheduling should be done for each test by using Six-Sigma Methodology.

scholarly journals Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory

2018 ◽  
Vol 10 (02) ◽  
pp. 194-199 ◽  
Author(s):  
B. Vinodh Kumar ◽  
Thuthi Mohan
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Biochemical Parameters ◽  
Clinical Chemistry ◽  
Chemistry Laboratory ◽  
Internal Quality ◽  
Average Performance ◽  
Sigma Level ◽  
Clinical Chemistry Laboratory ◽  
Level 1

Abstract OBJECTIVE: Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics. MATERIALS AND METHODS: This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters. RESULTS: For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy. CONCLUSION: This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.

Combined Shewhart-cusum control chart for improved quality control in clinical chemistry.

1977 ◽  
Vol 23 (10) ◽  
pp. 1881-1887 ◽  
Author(s):  
J O Westgard ◽  
T Groth ◽  
T Aronsson ◽  
C H de Verdier
Keyword(s):  
Quality Control ◽  
Control Systems ◽  
Control Chart ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Internal Quality ◽  
Decision Limit ◽  
Cusum Control Chart ◽  
Computer Simulation Studies ◽  
Cusum Method

Abstract We describe the adaptation of the decision limit cumulative sum method (cusum) to internal quality control in clinical chemistry. With the decision limit method, the cusum is interpreted against a numerical limit, rather than by use of a V-mask. The method can be readily implemented in computerized quality-control systems or manually on controls charts. We emphasize the manual application here and demonstrate how the technique can be implemented on existing Shewhart or Levey-Jennings control charts. This permits both cusum and Shewhart control rules to be used simultaneously on a single control chart and also minimizes the data calculations necessary for the cusum method. Computer simulation studies are used to determine the performance characteristics of several different cusum rules, alone and in combination with a Shewhart rule. These studies indicate that improvements in existing quality-control systems should be possible by addition of this simple cusum method and by use of a combined Shewhart-cusum control chart. This should be particularly advantageous when introducing the cusum method in laboratories with manual quality-control systems.

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