Evaluation of Sigma Metrics and Westgard Rule Selection and Implementation of Internal Quality Control in Clinical Chemistry Reference Laboratory, Ethiopian Public Health Institute

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.

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Contribution to the History of Serbian Medicine: Ten Years from the Introduction of Clostridium difficile into Microbiological Diagnostics in Serbia

Acta Facultatis Medicae Naissensis ◽

10.1515/afmnai-2017-0025 ◽

2017 ◽

Vol 34 (3) ◽

pp. 235-240

Author(s):

Predrag Stojanović ◽

Branislava Kocić ◽

Gorana Dragovac ◽

Marina Randjelović ◽

Vukica Pantović ◽

...

Keyword(s):

Public Health ◽

Reference Laboratory ◽

Isolation And Identification ◽

The Public ◽

Health Institute ◽

Clinical Center ◽

Toxigenic Strains ◽

Scientific Meetings ◽

Anaerobic Infections ◽

Public Health Institute

SummaryIn Serbia, the first isolates ofC. difficilewere isolated in the Public Health Institute (PHI), Center for Microbiology in Niš, at the end of 2005.The National Reference Laboratory for Anaerobic Infections (NRLA) in PHI Niš confirmed the toxigenic strains that caused the first three registered hospital epidemics in Serbia, in 2006 at the Clinic of Neurology, Clinical Center Niš, in 2007 at the Clinical Center of Vojvodina in Novi Sad, and in 2009 in the General Hospital in Požarevac.In 2014,C. difficilespecies were isolated for the first time from 175 environment samples in the research studies which were conducted in NRLA of PHI Niš. In the samples of soil taken from the ground within the Clinical Center Niš, those taken from the parks at the territory of the Municipality of Niš, samples of mud and sand around the illegal sewage systems at the territory of the Municipality of Niška Banja, a small number of bacteriaC. difficileproducing the toxins (A+B+) as well as non-toxigenic isolates (A−B−) were found.Results of the first epidemiological investigations of cases of diarrhea associated with prior antibiotic treatment applied in hospitalized patients in a number of health centers in our country, microbiological investigations done in the Public Health Institute, valuable discussions at professional and scientific meetings influenced the general attitude that isolation and identification ofC. difficileand/or detection of toxin produced by this bacteria should be part of the routine work in the Serbian microbiological laboratories.

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

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqz116.001 ◽

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.

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

Journal of Medical Biochemistry ◽

10.1515/jomb-2017-0026 ◽

2017 ◽

Vol 36 (4) ◽

pp. 301-308 ◽

Cited By ~ 5

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.

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