Sigma Metrics used to Evaluate the Performance of Internal Quality Control in a Clinical Biochemistry Laboratory

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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Internal quality control: best practice

Journal of Clinical Pathology ◽

10.1136/jclinpath-2013-201661 ◽

2013 ◽

Vol 66 (12) ◽

pp. 1027-1032 ◽

Cited By ~ 23

Author(s):

Helen Kinns ◽

Sarah Pitkin ◽

David Housley ◽

Danielle B Freedman

Keyword(s):

Quality Control ◽

Error Detection ◽

Best Practice ◽

Internal Quality Control ◽

Internal Quality ◽

Assay Performance ◽

Clinically Significant ◽

Clinical Biochemistry Laboratory ◽

Definition Of ◽

Selection Of

There is a wide variation in laboratory practice with regard to implementation and review of internal quality control (IQC). A poor approach can lead to a spectrum of scenarios from validation of incorrect patient results to over investigation of falsely rejected analytical runs. This article will provide a practical approach for the routine clinical biochemistry laboratory to introduce an efficient quality control system that will optimise error detection and reduce the rate of false rejection. Each stage of the IQC system is considered, from selection of IQC material to selection of IQC rules, and finally the appropriate action to follow when a rejection signal has been obtained. The main objective of IQC is to ensure day-to-day consistency of an analytical process and thus help to determine whether patient results are reliable enough to be released. The required quality and assay performance varies between analytes as does the definition of a clinically significant error. Unfortunately many laboratories currently decide what is clinically significant at the troubleshooting stage. Assay-specific IQC systems will reduce the number of inappropriate sample-run rejections compared with the blanket use of one IQC rule. In practice, only three or four different IQC rules are required for the whole of the routine biochemistry repertoire as assays are assigned into groups based on performance. The tools to categorise performance and assign IQC rules based on that performance are presented. Although significant investment of time and education is required prior to implementation, laboratories have shown that such systems achieve considerable reductions in cost and labour.

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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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Efficacy of Pooled Serum Internal Quality Control in Comparison with Commercial Internal Quality Control in Clinical Biochemistry Laboratory

Journal of Laboratory Physicians ◽

10.1055/s-0040-1721151 ◽

2020 ◽

Vol 12 (03) ◽

pp. 191-195

Author(s):

Sweta Kulkarni ◽

Shema Alain Pierre ◽

Ramachandran Kaliaperumal

Keyword(s):

Quality Control ◽

Density Lipoprotein ◽

Cost Effective ◽

Internal Quality Control ◽

Internal Quality ◽

Serum Samples ◽

Commercial Sample ◽

Effective Measure ◽

Clinical Biochemistry Laboratory ◽

Better Than

Abstract Introduction With increasing automation in clinical laboratories, the requirements for quality control (QC) material have greatly increased in order to monitor performance. The constant use of commercial control material is not economically feasible for many countries because of nonavailability or the high-cost of those materials. Therefore, preparation and use of in-house QC serum will be a very cost-effective measure with respect to laboratory needs. Materials and Methods In-house internal quality control from leftover serum samples of master health checkup subjects, which have been screened negative for HIV, HCV and HBsAg antibodies was pooled in a glass jar with ethanediol as preservative and kept in deep freezer at − 20°C. From the pooled serum, 100 microliter thirty aliquots were prepared. Every day along with commercial internal QC (IQC), one aliquot of pooled serum was analyzed for 30 days for the following parameters: plasma glucose, blood urea, serum creatinine, total cholesterol, triglycerides (TGL), high-density lipoprotein, calcium, total protein, albumin, total bilirubin, AST, ALT, ALP, amylase. After getting 30 values for each parameter, mean, standard deviation (SD) and CV% were calculated for both IQC commercial sample and pooled serum sample. Results The mean, SD, and CV% of glucose, cholesterol, TGL, calcium, alanine aminotransaminase (ALT), aspartate aminotransferase (AST), amylase, and alkaline phosphatase (ALP) were statistically significant between pooled serum and commercial QC. Conclusion In-house QC prepared from pooled serum is better than commercial internal QC. The biochemical parameters were stable in pooled serum due to less matrix effect; also, variation was less in pooled serum IQC.

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Quality improvement in clinical biochemistry laboratory using six sigma metrics and quality goal index

10.17727/jmsr.2021/9-16 ◽

2021 ◽

Vol 9 (2) ◽

pp. 101-107

Author(s):

Maheshwari A ◽

Sadariya B ◽

Javia HN ◽

Sharma D

Keyword(s):

Quality Control ◽

Quality Assurance ◽

Six Sigma ◽

Quality Management System ◽

Clinical Biochemistry ◽

Process Efficiency ◽

Internal Quality ◽

Level Ii ◽

Level I ◽

Clinical Biochemistry Laboratory

Introduction: One of the most popular quality management system tackle employed for process perfection is six sigma. When the process outcome is measurable, six sigma can be used to assess the quality. Aim: Present study was conducted with the objective to apply six sigma matrices and quality goal index for the assessment of quality assurance in a clinical biochemistry laboratory. Materials and methods: Present study is a retrospective study. Internal and external quality control data were analyzed retrospectively during July 2020 to December 2020. Descriptive statistics like laboratory mean ± standard deviation; bias and coefficient of variation (CV) were calculated for the parameters glucose, urea, creatinine, ALT (SGPT), AST (SGOT), cholesterol, triglyceride and HDL. Sigma value was calculated for both level I & level II of internal quality control (IQC). Results: Satisfactory sigma values (≥3) were elicited for blood glucose, cholesterol, triglyceride, HDL, urea and creatinine, while ALT (SGPT) and AST (SGOT) performed poorly (<3) on the sigma scale. The quality goal index (QGI) ratio was found (> 1.2) for only 2 parameters SGPT and SGOT (with sigma value <3) for both levels 1 and 2, indicating inaccuracy. Conclusion: Results of present study focuses on meticulous appraisal and execution of quality measures to improve sigma standards of all the analytical processes. Even though six sigma provides benefits over former approaches to quality assurance, it also opens newer challenges for laboratory practitioners. Therefore, sigma metric analysis provides a point of reference to design a protocol for IQC for the laboratory; address poor assess performance, and assess the existing laboratory process efficiency.

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