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

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.

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 Sigma metrics in quality control- An innovative tool

2022 ◽  
Vol 8 (4) ◽  
pp. 253-259
Author(s):  
Juby Sara Koshy ◽  
Afsheen Raza
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Assessment Tool ◽  
Clinical Laboratory ◽  
Density Lipoprotein ◽  
Internal Quality ◽  
Control Procedure ◽  
Gamma Glutamyl Transferase ◽  
Level 1 ◽  
Glutamyl Transferase

The clinical laboratory in today’s world is a rapidly evolving field which faces a constant pressure to produce quick and reliable results. Sigma metric is a new tool which helps to reduce process variability, quantitate the approximate number of analytical errors, and evaluate and guide for better quality control (QC) practices.To analyze sigma metrics of 16 biochemistry analytes using ERBA XL 200 Biochemistry analyzer, interpret parameter performance, compare analyzer performance with other Middle East studies and modify existing QC practices.This study was undertaken at a clinical laboratory for a period of 12 months from January to December 2020 for the following analytes: albumin (ALB), alanine amino transferase (SGPT), aspartate amino transferase (SGOT), alkaline phosphatase (ALKP), bilirubin total (BIL T), bilirubin direct (BIL D), calcium (CAL), cholesterol (CHOL), creatinine (CREAT), gamma glutamyl transferase (GGT), glucose (GLUC), high density lipoprotein (HDL), triglyceride (TG), total protein (PROT), uric acid (UA) and urea. The Coefficient of variance (CV%) and Bias % were calculated from internal quality control (IQC) and external quality assurance scheme (EQAS) records respectively. Total allowable error (TEa) was obtained using guidelines Clinical Laboratories Improvement Act guidelines (CLIA). Sigma metrics was calculated using CV%, Bias% and TEa for the above parameters. It was found that 5 analytes in level 1 and 8 analytes in level 2 had greater than 6 sigma performance indicating world class quality. Cholesterol, glucose (level 1 and 2) and creatinine level 1 showed &#62;4 sigma performance i.e acceptable performance. Urea (both levels) and GGT (level 1) showed &#60;3 sigma and were therefore identified as the problem analytes. Sigma metrics helps to assess analytic methodologies and can serve as an important self assessment tool for quality assurance in the clinical laboratory. Sigma metric evaluation in this study helped to evaluate the quality of several analytes and also categorize them from high performing to problematic analytes, indicating the utility of this tool. In conclusion, parameters showing lesser than 3 sigma need strict monitoring and modification of quality control procedure with change in method if necessary.

scholarly journals Correlation between glycated hemoglobin HbA1c and serum lipid profile in patients with type 2 diabetes

2021 ◽  
pp. 14-18
Author(s):  
Asmaa Alboueishi
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Blood Glucose ◽  
Serum Lipid ◽  
Clinical Laboratory ◽  
Low Density Lipoprotein ◽  
Fasting Blood Glucose ◽  
Density Lipoprotein ◽  
Diabetic Patients

Background: Hyperlipidemia is a common risk factor for diabetes that leads to cardiovascular disease, one of the causes of death of diabetic patients. Theaimof this study was to investigate the association between HbA1c levels and serum lipids in Libyan patients withtype 2 diabetes. Material and methods: The study was conducted in 2019 on 325 patients (174 males, 151 females) with type 2 diabetes referred to a private clinical laboratory in Tripoli, Libya. Blood samples were collected for measurement of HbA1c, fasting blood glucose and serum lipid concentrations. Diabetes was defined according to the American Diabetes Association criteria.The data were analyzed using an independent t-test and Pearson’scorrelation test.Results: The ages of the patients ranged from 40 to 83 years, with a mean of 51.52 ± 14.32 years SD. No significant correlation between HbA1c and age was noted (r=0.011, p=0.063). There was a significant positive correlation betweenHbA1c level and fasting blood glucose (r =0.641, p=0.000), low-density lipoprotein (r = 0.240, p = 0.000), total cholesterol (r = 0.223, p = 0.000) and triglycerides(r=0.140,p 0.067). The correlation between HbA1c and high-density lipoprotein-C was negative but not significant (r= -0.088, p = 0.123). Conclusion: HbA1c could be used as a predictor of dyslipidemia and thus it may serve as anindicator of the development of cardiovascular disease in patients with type-2 diabetes mellitus.

Improved phenotyping of apolipoprotein E: application to population frequency distribution

1990 ◽  
Vol 36 (11) ◽  
pp. 1871-1874 ◽  
Author(s):  
J S Hill ◽  
P H Pritchard
Keyword(s):  
Population Studies ◽  
Clinical Laboratory ◽  
Simple Procedure ◽  
Low Density Lipoprotein ◽  
Large Population ◽  
Density Lipoprotein ◽  
Lipid Clinic ◽  
Apo E ◽  
Slab Gels ◽  
Peroxidase Conjugate

Abstract A simple procedure for phenotyping apolipoprotein (apo) E directly from plasma has been developed for use in the lipid clinic laboratory. In this new method, 10 microL of serum or plasma is pretreated with neuraminidase (EC 3.2.1.18), which removes the sialic acid residues from apo E and eliminates additional bands, thereby ensuring correct phenotype assignment. After a rapid delipidation step, the samples are focused in vertical polyacrylamide mini-slab gels and immunoblotted with a polyclonal goat anti-apo E antibody, followed by a Protein G-peroxidase conjugate. The accuracy of this method was confirmed by comparison with the established procedure of phenotyping by isoelectric focusing of delipidated very-low-density lipoprotein. In addition, sera from 203 subjects from Vancouver, selected without conscious bias, were used to determine the local distribution of the apo E alleles. We estimate that the relative frequencies of apo E alleles epsilon 2, epsilon 3, and epsilon 4 in this population are 0.086, 0.761, and 0.153, respectively. The speed and convenience of using minigels make this procedure ideal for clinical laboratory applications and large population studies.

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