Quality Control in Clinical Chemistry: The Two-sample Plot and Improvement of Laboratory Performance

Abstract Improved accuracy of clinical laboratory measurements requires a broad range concept of quality control. Items considered include standardization, precision, specificity, recovery studies, interlaboratory comparisons, and long-range stability of laboratory performance.

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Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i55a33837 ◽

2021 ◽

pp. 305-309

Author(s):

G. Anuradha ◽

S. Santhinigopalakrishnan ◽

S. Sumathy

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Laboratory ◽

Clinical Biochemistry ◽

Clinical Chemistry ◽

Control Data ◽

Laboratory Performance ◽

Clinical Biochemistry Laboratory ◽

Quality Control Data

Background: Physicians rely on laboratory results for treating patients. So it is the duty of laboratories to assure quality of the results released. So laboratory performance should be validated to maintain the quality. Six sigma has now gained recent interest in monitoring the laboratory quality.This study was designed to gauge the clinical chemistry parameters based on six sigma metrics. Materials and Methods: In this retrospective study, both the internal and external quality control data of 26 clinical chemistry parameters were collected for a period of 6 months from June 2020 to November 2020 and the six sigma analysis was done at the Central clinical biochemistry laboratory of Chettinad Hospital and research institute. Results: AST, amylase, lipase, triglyceride, HDL, iron, magnesium, creatine kinase showed sigma values more than 6.Uric acid, total protein, ALT, direct bilirubin, GGT,cholesterol, cholesterol, calcium, TIBC and phosphorus shows sigma values between 3.5 to 6. Glucose, BUN, creatinine, albumin, Na, K, Chloride, showed sigma values less than 3.5. Conclusion: Six sigma metrics can help in improving the quality of laboratory performance and also helps to standardisethe actual amount of QC that is required by the laboratory for maintaining quality.

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The Schemes of External Quality Control in Laboratory Medicine in the Balkans

Journal of Medical Biochemistry ◽

10.2478/v10011-007-0030-8 ◽

2007 ◽

Vol 26 (3) ◽

pp. 245-247

Author(s):

Petros Karkalousos

Keyword(s):

Quality Control ◽

Scientific Community ◽

Clinical Chemistry ◽

Laboratory Medicine ◽

External Quality Control ◽

External Quality ◽

The Balkans ◽

Control Schemes ◽

Balkan Countries ◽

Balkan Region

The Schemes of External Quality Control in Laboratory Medicine in the Balkans There are many differences between the national External Quality Control Schemes all around Europe, but the most important ones are certainly those between the countries of the Balkan region. These differences are due to these countries' different political and financial development, as well as to their tradition and the development of clinical chemistry science in each one. Therefore, there are Balkan countries with very developed EQAS and others where there is no such a scheme. Undoubtedly, the scientific community in these countries wants to develop EQAS despite of the financial and other difficulties.

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Computerized Preparation of Two-Way Analysis of Variance Control Charts for Clinical Chemistry

Clinical Chemistry ◽

10.1093/clinchem/18.3.250 ◽

1972 ◽

Vol 18 (3) ◽

pp. 250-257 ◽

Cited By ~ 4

Author(s):

J H Riddick ◽

Roger Flora ◽

Quentin L Van Meter

Keyword(s):

Quality Control ◽

Data Analysis ◽

Computer Program ◽

Analysis Of Variance ◽

Control Charts ◽

Clinical Chemistry ◽

Control Data ◽

Laboratory Error ◽

Quality Control Data

Abstract A system of quality-control data analysis by computer is described, in which two-way analysis of variance is used for partitioning sources of laboratory error into day-to-day, within-day, betweenpools and additivity variation. The partition for additivity is described in detail as to its advantages and applications. In addition, control charts based on two-way analysis of variance computations are prepared each month by computer. This computer program is designed to operate with the IBM 1800 or 1130 computers or any computer with a Fortran IV compiler. Examples are presented of use of the control charts and of tables of analysis of variance.

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Experience in the netherlands with an external quality control and scoring system for clinical chemistry laboratories

Clinica Chimica Acta ◽

10.1016/0009-8981(77)90285-6 ◽

1977 ◽

Vol 74 (3) ◽

pp. 191-201 ◽

Cited By ~ 24

Author(s):

A.P. Jansen ◽

E.J. Van Kampen ◽

B. Leijnse ◽

C.A.M. Meijers ◽

P.J.J. Van Munster

Keyword(s):

Quality Control ◽

The Netherlands ◽

Scoring System ◽

Clinical Chemistry ◽

External Quality Control ◽

External Quality ◽

Chemistry Laboratories

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Analytical Bias in a Quality Control Scheme

Clinical Chemistry ◽

10.1093/clinchem/15.11.1039 ◽

1969 ◽

Vol 15 (11) ◽

pp. 1039-1044 ◽

Cited By ~ 8

Author(s):

John R Allen ◽

Rachel Earp ◽

E Christis Farrell ◽

H D Grümer

Keyword(s):

Quality Control ◽

Clinical Chemistry ◽

Control Program ◽

Chemistry Laboratory ◽

Quality Control Program ◽

Control Scheme ◽

Clinical Chemistry Laboratory ◽

Random Times ◽

Quality Control Scheme ◽

Control Samples

Abstract A quality control program utilizing both "known" and "blind" control specimens was analyzed in the routine clinical chemistry laboratory. The results obtained with the control samples of 18 automated and nonautomated procedures demonstrated the presence of analytical bias. Only through the evaluation of blind control samples tested at random times can a reliable measure of the proficiency of the laboratory be achieved.

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Study of quality control results in Atellica CH 930 Clinical Chemistry System comparing with ADVIA Chemistry System XPT

Clinica Chimica Acta ◽

10.1016/j.cca.2019.03.052 ◽

2019 ◽

Vol 493 ◽

pp. S20-S21

Author(s):

N. Rico ◽

R. Wijngaard ◽

I. Falcón ◽

M. Perez ◽

J.L. Bedini Chesa

Keyword(s):

Quality Control ◽

Clinical Chemistry

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Evaluation of Discrepancies in Patients' Results— An Aspect of Computer-Assisted Quality Control

Clinical Chemistry ◽

10.1093/clinchem/21.1.87 ◽

1975 ◽

Vol 21 (1) ◽

pp. 87-92 ◽

Cited By ~ 20

Author(s):

Philip Whitehurst ◽

Thomas V Di Silvio ◽

Gaydzag Boyadjian

Keyword(s):

Quality Control ◽

Patient Safety ◽

Computer Program ◽

High Probability ◽

Clinical Chemistry ◽

Computer Assisted ◽

Probability Of Error ◽

On Demand

Abstract A computer program has been devised to select those clinical chemistry results that have a high probability of error for inclusion on a discrepancy report, which is printed on demand throughout the day. Each report entry is evaluated by a supervisor, who decides whether to accept the result or to re-assay. With this program, 8.4% of all results were included on the report, 1.9% were re-assayed, and 0.83% were judged to be in error and corrected. Checking results at the time of their release to the computer has led to earlier report delivery and more convenient timing of re-assays without compromise of patient safety.

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Chemical assays--new opportunities.

Clinical Chemistry ◽

10.1093/clinchem/33.9.1574 ◽

1987 ◽

Vol 33 (9) ◽

pp. 1574-1578 ◽

Cited By ~ 2

Author(s):

A E Burkhardt

Keyword(s):

Quality Control ◽

Solid Phase ◽

Clinical Chemistry ◽

Test Format ◽

Home Testing

Abstract The acceptance of the solid-phase format in various areas of clinical chemistry is the consequence of the advantages of this test format, which include stability of the reagents, unitized packaging, convenient and small instruments, and minimal preparations by users before testing. Overall, these advantages provide very convenient tests. Future successful uses of solid-phase reagents depend upon how well these features meet the needs of the users. Needs for systems to be used in the decentralized laboratory include even less cost, even more convenience, and improved quality control. Needs for home testing include convenient tests with clinically useful accuracy, improved quality control, and improved recording systems to overcome user bias in recording results. New solid-phase technologies being developed include noncolorimetric systems suitable for use with miniature probes, for in vitro or in vivo use, and spectrophotometric systems for determinations of analytes directly in capillaries of the skin without invasive sampling.

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A direct comparison of patient-based real-time quality control techniques: The importance of the analyte distribution

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/0004563220902174 ◽

2020 ◽

Vol 57 (3) ◽

pp. 206-214 ◽

Cited By ~ 1

Author(s):

Joel D Smith ◽

Tony Badrick ◽

Francis Bowling

Keyword(s):

Quality Control ◽

Real Time ◽

Error Detection ◽

Alanine Aminotransferase ◽

Clinical Chemistry ◽

Moving Average ◽

Moving Averages ◽

Skewed Distributions ◽

Real Patient ◽

Box Cox Transformation

Background Patient-based real-time quality control (PBRTQC) techniques have been described in clinical chemistry for over 50 years. PBRTQC has a number of advantages over traditional quality control including commutability, cost and the opportunity for real-time monitoring. However, there are few systematic investigations assessing how different PBRTQC techniques perform head-to-head. Methods In this study, we compare moving averages with and without truncation and moving medians. For analytes with skewed distributions such as alanine aminotransferase and creatinine, we also investigate the effect of Box–Cox transformation of the data. We assess the ability of each technique to detect simulated analytical bias in real patient data for multiple analytes and to retrospectively detect a real analytical shift in a creatinine and urea assay. Results For analytes with symmetrical distributions, we show that error detection is similar for a moving average with and without four standard deviation truncation limits and for a moving median. In contrast to analytes with symmetrically distributed results, moving averages perform poorly for right skewed distributions such as alanine aminotransferase and creatinine and function only with a tight upper truncation limit. Box–Cox transformation of the data both improves the performance of moving averages and allows all data points to be used. This was also confirmed for retrospective detection of a real analytical shift in creatinine and urea. Conclusions Our study highlights the importance of careful assessment of the distribution of patient results for each analyte in a PBRTQC program with the optimal approaches dependent on whether the patient result distribution is symmetrical or skewed.

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