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

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.

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

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.

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

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.

scholarly journals Assessment of six sigma metrics applications of quality control in clinical biochemistry laboratory

2019 ◽  
Vol 11 (3) ◽  
pp. 100-103
Author(s):  
Ramya KR ◽  
◽  
Vijetha Shenoy Belle ◽  
Pravesh Hegde ◽  
Sushma Jogi ◽  
...  
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Biochemistry ◽  
Clinical Biochemistry Laboratory
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