Testing Internal Quality Control of Clinical Laboratory Data Using Paired t -Test under Uncertainty

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.

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Tests in the point of care – glucose meter as a POCT tool. Implement a quality system control

Diagnostyka Laboratoryjna ◽

10.5604/01.3001.0013.7986 ◽

2017 ◽

Vol 53 (4) ◽

pp. 211-216

Author(s):

Jolanta Stacherzak-Pawlik ◽

Alina Rak ◽

Joanna Smaciarz ◽

Krystyna Jasińska

Keyword(s):

Quality Control ◽

Glucose Concentration ◽

Point Of Care ◽

Laboratory Data ◽

University Hospital ◽

Internal Quality Control ◽

Internal Quality ◽

System Control ◽

Training Courses ◽

Glucose Meter

Introduction: Point of care testing (POCT) are tests carried out near to the patient, outside the laboratory. There is a continuing increase interest in POCT, however the issue of quality of those tests rises concerns. Glucose concentration measurements are carried out using glucometers, which makes those analyzers a POCT tool. All devices outside the laboratory should be included in internal quality control system according to ISO 22870, so it is necessary to control glucometers as well.<br>Aim: To perform the quality control of glucometers as a POCT tools in University Hospital in Wroclaw, evaluate the correct use of glucometers, conduct replacement of devices, and implement constant internal quality control.<br>Materials and methods: The approximate count of blood glucose measurements in the hospital was determined on the basis of laboratory data and glucometers strips orders. Quality control was performed using MediSense certified control materials at two concentrations levels. A total of 272 control measurements were used for interpretation, which were made on 25 FreeStyle Optium H glucometers. The results were registered in quality control sheet.<br>Results: Laboratory determinations of glucose concentration represented 24.6% of all glucose measurements, POCT tests 75,4%. Among control measurements 80.88% were within ± 15% error recommended by Polish Diabetology Association and 19.12% exceeded it, 7.35% exceeded ± 20%. However none measurement exceeded the error recommended by glucometer manufacturer.<br>Conclusions: Based on our results several glucometers were replaced and the systematic internal quality control was introduced. Numerous of glucometer training courses for medical staff were conducted.

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Internal Quality Control of PCR-based Genotyping Methods in Research Studies and Patient Diagnostics

Thrombosis and Haemostasis ◽

10.1055/s-0037-1613089 ◽

2002 ◽

Vol 87 (05) ◽

pp. 812-816 ◽

Cited By ~ 14

Author(s):

Jørgen Gram ◽

Jørgen Jespersen ◽

Moniek de Maat ◽

Else-Marie Bladbjerg

Keyword(s):

Quality Control ◽

Clinical Laboratory ◽

Dna Amplification ◽

Restriction Enzymes ◽

Control Programme ◽

Internal Quality Control ◽

Internal Quality ◽

Reagent Blank ◽

Thrombosis Research ◽

Control Programmes

SummaryGenetic analyses are increasingly integrated in the clinical laboratory, and internal quality control programmes are needed. We have focused on quality control aspects of selected polymorphism analyses used in thrombosis research. DNA was isolated from EDTA-blood (n = 500) by ammonium acetate precipitation and analysed for 18 polymorphisms by polymerase chain reaction (PCR), i. e. restriction fragment length polymorphisms, allele specific amplification, or amplification of insertion/deletion fragments. We evaluated the following aspects in the analytical procedures: sample handling and DNA-isolation (pre-analytical factors), DNA-amplification, digestion with restriction enzymes, electrophoresis (analytical factors), result reading and entry into a database (post-analytical factors). Furthermore, we evaluated a procedure for result confirmation. Isolated DNA was of good quality (42 µ.g/ml blood, A260/A280 ratio >1.75, negative DNAsis tests), and the reagent blank was contaminated in <1% of the results. Occasionally, results were re-analysed because of positive reagent blanks (<1%) or because of problems with the controls (< 5%). On confirmation, we observed 4 genotyping discrepancies. Control of data handling revealed 0.1% reading mistakes and 0.5% entry mistakes. Based on our experiences we propose an internal quality control programme for widely used PCR-based haemostasis polymorphism analyses.

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Appraisal of the domestic kit «MICMICRO » for antimicrobial susceptibility testing by serial microdilution method

Clinical Microbiology and Antimicrobial Chemotherapy ◽

10.36488/cmac.2020.3.231-236 ◽

2020 ◽

pp. 231-236

Author(s):

Anna A. Samoilova ◽

L.A. Kraeva ◽

I.V. Likhachev ◽

E.V. Rogacheva ◽

V.N. Verbov ◽

...

Keyword(s):

Escherichia Coli ◽

Quality Control ◽

Susceptibility Testing ◽

New Technologies ◽

Clinical Laboratory ◽

Clinical Isolates ◽

Internal Quality ◽

Diagnostic Efficiency ◽

Microdilution Method ◽

Reference Strains

Objective. To assess efficiency of the “MIC-MICRO” kit developed in the Department of New Technologies of the Saint-Petersburg Pasteur Institute, on reference strains and clinical bacterial isolates. Materials and Methods. In order to assess the “MIC-MICRO” kit, several options of its execution were used, including different groups of antibiotics: aztreonam, amikacin, gentamicin, colistin, meropenem, nitrofurantoin, chloramphenicol, cefotaxime, ceftriaxone, ciprofloxacin, erythromycin. In order to determine the range of antibiotic values, the EUCAST-2020 database was used. The quality control of adsorbed antibiotics was carried out using reference strains: Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213 and Escherichia coli NCTC 13846 (colistin-resistant). Acceptable and target ranges of MIC values for control strains are evaluated according to “Regular and extended internal quality control for determining MIC and disk diffusion according to EUCAST recommendations” (v10.0). A total of 28 clinical isolates of K. pneumoniae obtained from patients with nosocomial infections in St. Petersburg hospitals in 2018–2019 was used in the study. The coordination of test results was obtained in accordance with GOST R ISO 20776-1-2010. Susceptibility testing results were interpreted in accordance with EUCAST recommendations (v10.0). Results. The MIC values in relation to the reference strains obtained using the “MIC-MICRO” kit were determined in the range of recommended values of the EUCAST-2020 standard. The results obtained in relation to clinical isolates of K. pneumoniae showed that the sensitivity categories determined using the developed kit and the serial microdilution method were the same for all the studied strains. The percentage of colistin-resistant isolates (MIC > 2 mg/ml) in the serial microdilution method and determined using the “MIC-MICRO” kit was 35.7%. The percentage of susceptible strains was also similar for two types of methods (64.3%). Conclusions. Colistin susceptibility testing of K. pneumoniae strains using the “MIC-MICRO” diagnostic kit and the reference serial microdilution method in a tablet, showed comparable results. Diagnostic efficiency, ease to use and simple interpretation of results make it possible to use the developed “MIC-MICRO” kit in clinical laboratory practice.

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Implementing Self Sustained Quality Control Procedures in a Clinical Laboratory

Journal of Nepal Medical Association ◽

10.31729/jnma.560 ◽

2013 ◽

Vol 52 (189) ◽

pp. 233-237 ◽

Cited By ~ 2

Author(s):

Roshan Khatri ◽

Sanjay KC ◽

Prabodh Shrestha ◽

J N Sinha

Keyword(s):

Quality Control ◽

Clinical Laboratory ◽

Reliability And Validity ◽

Disease Diagnosis ◽

Mitigation Measures ◽

Internal Quality ◽

Control Procedure ◽

Random Errors ◽

Control Rules ◽

Control Serum

Introduction: Quality control is an essential component in every clinical laboratory which maintains the excellence of laboratory standards, supplementing to proper disease diagnosis, patient care and resulting in overall strengthening of health care system. Numerous quality control schemes are available, with combinations of procedures, most of which are tedious, time consuming and can be “too technical” whereas commercially available quality control materials can be expensive especially for laboratories in developing nations like Nepal. Here, we present a procedure performed at our centre with self prepared control serum and use of simple statistical tools for quality assurance. Methods: The pooled serum was prepared as per guidelines for preparation of stabilized liquid quality control serum from human sera. Internal Quality Assessment was performed on this sample, on a daily basis which included measurement of 12 routine biochemical parameters. The results were plotted on Levey-Jennings charts and analysed with quality control rules, for a period of one month. Results: The mean levels of biochemical analytes in self prepared control serum were within normal physiological range. This serum was evaluated every day along with patients’ samples. The results obtained were plotted on control charts and analysed using common quality control rules to identify possible systematic and random errors. Immediate mitigation measures were taken and the dispatch of erroneous reports was avoided. Conclusions: In this study we try to highlight on a simple internal quality control procedure which can be performed by laboratories, with minimum technology, expenditure, and expertise and improve reliability and validity of the test reports. Keywords: Levey-Jennings charts; pooled sera; quality control; Westgard Rule.

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

Revista Romana de Medicina de Laborator ◽

10.2478/rrlm-2020-0012 ◽

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.

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Improvement of Clinical Laboratory Services through Quality

International Journal of Reliable and Quality E-Healthcare ◽

10.4018/ijrqeh.2013040103 ◽

2013 ◽

Vol 2 (2) ◽

pp. 38-46 ◽

Cited By ~ 8

Author(s):

Antonia Mourtzikou ◽

Marilena Stamouli ◽

Elena Athanasiadi

Keyword(s):

Quality Control ◽

Health Care Providers ◽

Clinical Laboratory ◽

Internal Quality ◽

Care Providers ◽

Improvement Program ◽

Laboratory Performance ◽

Laboratory Services ◽

Poor Individual ◽

Eqa Schemes

Health care providers need test results that are relevant, accurate, and reliable for patient care. The term “quality control” is used to describe the set of procedures used to check that the results of laboratory tests are reliable for the intended clinical use. A laboratory might produce results that are considered unsatisfactory. While the cause for this might be immediately apparent, the identification of the underlying problem is neither always straightforward, nor easy because many factors can affect result quality. Internal quality control (IQC) and external quality assessment (EQA) are two distinct but complementary components of a laboratory quality improvement program. IQC ensures day-to-day laboratory consistency. EQA permits the identification of poor individual laboratory performance, as well as the detection of reagents, instruments and methods that produce unreliable or misleading results, by means of a retrospective analysis of data obtained by participating laboratories. Continuous participation in EQA schemes has been linked to improved laboratory performance.

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Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a Clinical Biochemistry Laboratory

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2021/47818.14722 ◽

2021 ◽

Author(s):

Trupti Diwan Ramteke ◽

Anita Shivaji Chalak ◽

Shalini Nitin Maksane

Keyword(s):

Quality Control ◽

Quality Management System ◽

Clinical Laboratory ◽

Clinical Biochemistry ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Internal Quality ◽

Method Performance ◽

Control Measurement ◽

Clinical Biochemistry Laboratory

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Quality goal index (QGI) was also calculated to identify the problems of inaccuracy and imprecision for parameters having lower sigma values. Materials and Methods: This retrospective, observational study was conducted at the Central Clinical Biochemistry Laboratory of Seth GS Medical College and KEM hospital in Mumbai for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of <4 to identify imprecision or inaccuracy. Statistical analysis was performed using Microsoft office excel 2010 software. Results: Total protein, Glucose, Urea, Triglyceride (TAG), High Density Lipoprotein (HDL), and Low Density Lipoprotein (LDL) for normal (L1) and pathological (L2) controls achieved excellent performance (>6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules-13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were <4 at one or more QC levels. For these analytes, strict QC procedures (Westgard rules-13s/22s/R4s/41s/6x, N4 and R45) were incorporated. QGI of these analytes was <0.8 which indicated the problem of imprecision. Staff training programs and review of standard operating procedures were done for these analytes to improve method performance. Conclusion: Sigma Metrics estimation helps in designing optimum QC protocols, minimising unnecessary QC runs and reducing the cost for analytes having high sigma metrics. Focused and effective QC strategy for analytes having low sigma helps in improving the performance of those analytes.

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Role and Responsibilities of Laboratory Medicine Specialists in the Verification of Metrological Traceability of in Vitro Medical Diagnostics / Uloga I Odgovornosti Specijalista Laboratorijske Medicine U Verifikaciji Metrološke Sledljivosti In Vitro Medicinske Dijagnostike

Journal of Medical Biochemistry ◽

10.1515/jomb-2015-0004 ◽

2015 ◽

Vol 34 (3) ◽

pp. 282-287 ◽

Cited By ~ 21

Author(s):

Federica Braga ◽

Ilenia Infusino ◽

Mauro Panteghini

Keyword(s):

Quality Control ◽

Measurement Uncertainty ◽

Clinical Laboratory ◽

Metrological Traceability ◽

Uncertainty Budget ◽

Medical Diagnostics ◽

Internal Quality ◽

Clinical Laboratories ◽

Laboratory Results

Summary To be accurate and equivalent, laboratory results should be traceable to higher-order references. Furthermore, their quality should fulfill acceptable measurement uncertainty as defined to fit the intended clinical use. With this aim, in vitro diagnostics (IVD) manufacturers should define a calibration hierarchy to assign traceable values to their system calibrators and to fulfill during this process uncertainty limits for calibrators, which should represent a proportion of the uncertainty budget allowed for clinical laboratory results. It is therefore important that, on one hand, the laboratory profession clearly defines the clinically acceptable uncertainty for relevant tests and, on the other hand, endusers may know and verify how manufacturers have implemented the traceability of their calibrators and estimated the corresponding uncertainty. Important tools for IVD traceability surveillance are quality control programmes through the daily verification by clinical laboratories that control materials of analytical systems are in the manufacturer’s declared validation range [Internal Quality Control (IQC) component I] and the organization of Exter nal Quality Assessment Schemes meeting metrological criteria. In a separate way, clinical laboratories should also monitor the reliability of employed commercial systems through the IQC component II, devoted to estimation of the measurement uncertainty due to random effects, which includes analytical system imprecision together with individual laboratory performance in terms of variability.

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