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 Application of Sigma Metrics for Evaluating the Analytical Performance of Thyroid Profile and Cortisol in Clinical Biochemistry Laboratory

2020 ◽  
Author(s):  
Smita Natvarbhai Vasava ◽  
Roshni Gokaldas Sadaria
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Clinical Biochemistry ◽  
Poor Performance ◽  
Analytical Performance ◽  
Internal Quality ◽  
Six Sigma Methodology ◽  
Clinical Biochemistry Laboratory ◽  
Thyroid Profile

Introduction: Now-a-days quality is the key aspect of clinical laboratory services. The six sigma metrics is an important quality measurement method for evaluating the performance of the clinical laboratory. Aim: To assess the analytical performance of clinical biochemistry laboratory by utilising thyroid profile and cortisol parameters from Internal Quality Control (IQC) data and to calculate sigma values. Materials and Methods: Study was conducted at Clinical Biochemistry Laboratory, Dhiraj General Hospital, Piparia, Gujarat, India. Retrospectively, IQC data of thyroid profile and cortisol were utilised for six subsequent months (July to December 2019). Coefficient of Variation (CV%) and bias were calculated from IQC data, from that the sigma values were calculated. The sigma values <3, >3 and >6 were indicated by poor performance procedure, good performance and world class performance, respectively. Results: The sigma values were estimated by calculating mean of six months. The mean sigma value of Thyroid Stimulating Hormone (TSH) and Cortisol were >3 for six months which indicated the good performance. However, sigma value of Triiodothyronine (T3), Tetraiodothyronine (T4) were found to be <3 which indicated poor performance. Conclusion: Six sigma methodology applications for thyroid profile and cortisol was evaluated, it was generally found as good. While T3 and T4 parameters showed low sigma values which requires detailed root cause analysis of analytical process. With the help of six sigma methodology, in clinical biochemistry laboratories, an appropriate Quality Control (QC) programming should be done for each parameter. To maintain six sigma levels is challenging to quality management personnel of laboratory, but it will be helpful to improve quality level in the clinical laboratories.

scholarly journals Internal quality control in blood and component bank in a tertiary healthcare center in Northern India

2021 ◽  
Vol 6 (2) ◽  
pp. 115-118
Author(s):  
Kafil Akhtar ◽  
Radhika Arora ◽  
Umrah Malik ◽  
Ankita Parashar ◽  
Murad Ahmad ◽  
...  
Keyword(s):  
Quality Control ◽  
Factor Viii ◽  
Red Cells ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Blood Component ◽  
Minimal Risk ◽  
Blood Components ◽  
The Mean ◽  
Rbc Count

Quality control describes steps taken by blood and component bank to ensure that tests are performed correctly. Primary goal of quality control is transfusion of safe quality of blood. It is to ensure availability of efficient supply of blood and blood components. Internal quality control is the backbone of quality assurance program. To analyze the internal quality control of blood components in modern blood banking as an indicator of our blood bank performance. An observational cross sectional study conducted at the Blood and Component Bank, JN Medical College and Hospital from 2018 to 2020. Each blood component was arbitrarily chosen during the study on monthly basis. Selection criteria was 1.0% of total collection or minimum 4 bags per month. Packed red cells were evaluated for hemoglobin, hematocrit, RBC count; platelet concentrates for pH, yield and culture; fresh frozen plasma and cryoprecipitate were evaluated for unit volume, factor VIII and fibrinogen concentration. The mean HCT of packed red cells was 65.75+7.42%, volume was 238+26.25ml, Hb was 20.5+0.15g/dL and RBC count of 5.89x10+0.30x10. The mean platelet yield was 5.7x10, pH was ≥6.8+0.175 and volume was 82.5+13.75ml; cultures were negative and swirling was present in all the platelet units tested. Mean factor VIII and fibrinogen levels were found to be 95.25 +7.37and 307.5+41.37gm/l for FFP respectively. Mean volume, PT and APTT were 215+32.5ml, 14.15+0.325 sec and 29.50+1.5 sec respectively. The quality control of blood components ensures the timely availability of a blood component of high quality with maximum efficacy and minimal risk to potential recipients.

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 Sigma Metrics: A Valuable Tool for Evaluating the Performance of Internal Quality Control in Laboratory

2021 ◽  
Author(s):  
Akriti Kashyap ◽  
Sangeetha Sampath ◽  
Preeti Tripathi ◽  
Arijit Sen
Keyword(s):  
Quality Control ◽  
Uric Acid ◽  
Six Sigma ◽  
Quality Management System ◽  
Direct Bilirubin ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Mean Corpuscular Hemoglobin ◽  
Corpuscular Hemoglobin ◽  
Urea Nitrogen

Abstract Background Six Sigma is a widely accepted quality management system that provides an objective assessment of analytical methods and instrumentation. Six Sigma scale typically runs from 0 to 6, with sigma value above 6 being considered adequate and 3 sigma being considered the minimal acceptable performance for a process. Methodology Sigma metrics of 10 biochemistry parameters, namely glucose, triglycerides, high-density lipoprotein (HDL), albumin, direct bilirubin, alanine transaminase, aspartate transaminase, urea nitrogen, creatinine and uric acid, and hematology parameters such as hemoglobin (Hb), total leucocyte count (TLC), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and platelet were calculated by analyzing internal quality control (IQC) data of 3 months (June–August 2019). Results Sigma value was found to be > 6 for triglyceride, HDL, Hb, TLC, and MCH, signifying excellent results and no further modification with respect to IQC. Sigma value was between 3 and 6 for glucose, albumin, creatinine, uric acid, PCV, and MCHC, implying the requirement of improvement in quality control (QC) processes. Sigma value of < 3 was seen in AST, ALT, direct bilirubin, urea nitrogen, platelet, and MCV, signifying suboptimal performance. Discussion Six Sigma provides a more quantitative framework for evaluating process performance with evidence for process improvement and describes how many sigmas fit within the tolerance limits.Thus, for parameters with sigma value < 3, duplicate testing of the sample along with three QCs three times a day may be used along with stringent Westgard rules for rejecting a run. Conclusion Sigma metrics help assess analytical methodologies and augment laboratory performance.

The between-run component of variation in internal quality control.

1989 ◽  
Vol 35 (7) ◽  
pp. 1416-1422 ◽  
Author(s):  
K Linnet
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Systematic Errors ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Components Of Variance ◽  
Upper Limit ◽  
The Mean ◽  
Power Curves

Abstract Design of control charts for the mean, the within-run component of variance, and the ratio of between-run to within-run components of variance is outlined. The between-run component of variation is the main source of imprecision for analytes determined by an enzymo- or radioimmunoassay principle; accordingly, explicit control of this component is especially relevant for these types of analytes. Power curves for typical situations are presented. I also show that a between-run component of variation puts an upper limit on the achievable power towards systematic errors. Therefore, when the between-run component of variation exceeds the within-run component, use of no more than about four controls per run is reasonable at a given concentration.

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 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.

scholarly journals Internal quality control of blood products: An experience from a tertiary care hospital blood bank from Southern Pakistan

2018 ◽  
Vol 10 (01) ◽  
pp. 064-067 ◽  
Author(s):  
Sadia Sultan ◽  
Hasan Abbas Zaheer ◽  
Usman Waheed ◽  
Mohammad Amjad Baig ◽  
Asma Rehan ◽  
...  
Keyword(s):  
Quality Control ◽  
Factor Viii ◽  
Blood Bank ◽  
International Standards ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Minimal Risk ◽  
Care Hospital ◽  
Blood Products ◽  
The Mean

Abstract INTRODUCTION: Internal quality control (IQC) is the backbone of quality assurance program. In blood banking, the quality control of blood products ensures the timely availability of a blood component of high quality with maximum efficacy and minimal risk to potential recipients. The main objective of this study is to analyze the IQC of blood products as an indicator of our blood bank performance. METHODS: An observational cross-sectional study was conducted at the blood bank of Liaquat National Hospital and Medical College, from January 2014 to December 2015. A total of 100 units of each blood components were arbitrarily chosen during the study. Packed red cell units were evaluated for hematocrit (HCT); random platelet concentrates were evaluated for pH, yield, and culture; fresh frozen plasma (FFP) and cryoprecipitate (CP) were evaluated for unit volume, factor VIII, and fibrinogen concentrations. RESULTS: A total of 400 units were tested for IQC. The mean HCT of packed red cells was 69.5 ± 7.24, and in 98% units, it met the standard (<80% of HCT). The mean platelet yield was 8.8 ± 3.40 × 109/L and pH was ≥6.2 in 98% bags; cultures were negative in 97% of units tested. Mean factor VIII and fibrinogen levels were found to be 84.24 ± 15.01 and 247.17 ± 49.69 for FFP, respectively. For CP, mean factor VIII and fibrinogen level were found to be 178.75 ± 86.30 and 420.7 ± 75.32, respectively. CONCLUSION: The IQC of blood products at our blood bank is in overall compliance and met recommended international standards. Implementation of standard operating procedures, accomplishment of standard guidelines, proper documentation with regular audit, and staff competencies can improve the quality performance of the transfusion services.

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