Application of the CLSI EP15-A3 Guideline as an Alternative Troubleshooting Tool for Verification of Assay Precision

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S88-S88
Author(s):  
Jose Jara Aguirre ◽  
Karl Ness ◽  
Alicia Algeciras-Schimnich
Keyword(s):  
Quality Control ◽  
Carcinoembryonic Antigen ◽  
Laboratory Investigation ◽  
Analysis Of Variance ◽  
Experimental Approach ◽  
Biological Variation ◽  
Analytical Performance ◽  
Microsoft Excel ◽  
Before And After ◽  
Assay Precision

Abstract Introduction The CLSI EP15-A3 guideline “User Verification of Precision and Estimation of Bias” provides a simple experimental approach to estimate a method’s imprecision and bias. The objective is to determine if the laboratory precision performance of repeatability (SR) and within-laboratory imprecision (SWL) are in accordance to the manufacturer specification claims (MSCs). Objectives Evaluate the utility of the EP15-A3 protocol to verify method precision during a troubleshooting investigation and after major instrument maintenance, using a carcinoembryonic antigen (CEA) immunoassay as an example. Methods CEA was performed on the Beckman Coulter DxI (Beckman Coulter, Brea, CA). Quality control (QC) levels (L1: 2.89; L2: 21.10; L3: 39.10 ng/mL) (Bio-Rad Laboratories, Irvine, CA) were used. Each QC level was measured before and after instrument maintenance as follows: five replicates per run, one run per day, and during 5 days. Imprecision estimates (IEs) for SR (%CVR) and SWL (%CVWL) were calculated by one-way analysis of variance using Microsoft Excel Analyse-it software. Estimated imprecision was compared to MSC and desirable imprecision specifications based on biological variation (BV). Results A change in the analytical performance of CEA was detected by a decreased sigma-metric indicator. After a bias problem was ruled out, the observed %CVR for L1, L2, and L3 were 7.2%, 3.7%, and 4.8%, respectively. The %CVWL were 8.3%, 5.0%, and 5.5%, which exceeded the MSC of %CVWL~4.0% to 4.5%. After a laboratory investigation, major instrument maintenance was performed by the manufacturer. The %CVR and %CVWL estimates for L1, L2, and L3 after maintenance were 3.2%, 3.8%, 3.5% and 3.9%, 4.2%, 4.0%, respectively. After maintenance, the CEA performance was consistent with the MSC for each of the levels analyzed and within the BV impression goal of %CV ≤6.4. Conclusion CLSI EP15-A3 guideline is an alternative troubleshooting tool that can be used to investigate and verify method precision performance before and after significant instrument maintenance.

EXPRESS: Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

2021 ◽  
pp. 000456322098803
Author(s):  
Qian Liu ◽  
Wenjun Zhu ◽  
Guangrong Bian ◽  
Wei Liang ◽  
Changxin Zhao ◽  
...  
Keyword(s):  
Quality Control ◽  
Cystatin C ◽  
Error Detection ◽  
Biological Variation ◽  
Analytical Performance ◽  
Batch Size ◽  
Internal Quality ◽  
Control Scheme ◽  
Improvement Measures ◽  
Quality Control Strategy

Background: Sigma metrics are commonly used to evaluate laboratory management. In this study, we aimed to evaluate the analytical performance of cystatin C using sigma metrics and to develop an individualized quality control scheme for cystatin C levels. Methods: Bias was calculated based on the samples used for the external quality assessment. The coefficient of variation was calculated using 6 months of internal quality control (IQC) measurements at two levels, and desirable specification derived from biological variation was used as the quality goal. The sigma value for cystatin C was calculated using the above data. The IQC scheme and improvement measures were formulated according to the Westgard sigma standards for batch size and quality goal index (QGI). Results: The sigma values for cystatin C, for quality control levels 1 and 2, were 3.04 and 4.95, respectively. The 13s/22s/R4s/41s/8x multi-rules (N=4 or 2 with R=2 or 4), with a batch size of 45 patient samples, were selected as the IQC schemes for cystatin C. With different levels of cystatin C, the power function graph showed a probability for error detection of 94% and 100% and a probability for false rejection of 4% and 2%, respectively. According to the QGI of cystatin C, its precision needs to be improved. Conclusions: With a “desirable” biological variation of 6.50%, the Westgard rule 13s/22s/R4s/41s/8x (N=4 or 2 with R=2 or 4, batch size of 45) with high efficacy for determining the detection error is recommended for individualized quality control schemes of cystatin C.

scholarly journals Performance Criteria for Testosterone Measurements Based on Biological Variation in Adult Males: Recommendations from the Partnership for the Accurate Testing of Hormones

2012 ◽  
Vol 58 (12) ◽  
pp. 1703-1710 ◽  
Author(s):  
Yeo-Min Yun ◽  
Julianne Cook Botelho ◽  
Donald W Chandler ◽  
Alex Katayev ◽  
William L Roberts ◽  
...  
Keyword(s):  
Total Error ◽  
Full Range ◽  
Biological Variation ◽  
Analytical Performance ◽  
Performance Criteria ◽  
Performance Goals ◽  
Adult Males ◽  
Large Variability ◽  
Wide Range ◽  
Variation Data

BACKGROUND Testosterone measurements that are accurate, reliable, and comparable across methodologies are crucial to improving public health. Current US Food and Drug Administration–cleared testosterone assays have important limitations. We sought to develop assay performance requirements on the basis of biological variation that allow physiologic changes to be distinguished from assay analytical errors. METHODS From literature review, the technical advisory subcommittee of the Partnership for the Accurate Testing of Hormones compiled a database of articles regarding analytical and biological variability of testosterone. These data, mostly from direct immunoassay-based methodologies, were used to specify analytical performance goals derived from within- and between-person variability of testosterone. RESULTS The allowable limits of desirable imprecision and bias on the basis of currently available biological variation data were 5.3% and 6.4%, respectively. The total error goal was 16.7%. From recent College of American Pathologists proficiency survey data, most currently available testosterone assays missed these analytical performance goals by wide margins. Data from the recently established CDC Hormone Standardization program showed that although the overall mean bias of selected certified assays was within 6.4%, individual sample measurements could show large variability in terms of precision, bias, and total error. CONCLUSIONS Because accurate measurement of testosterone across a wide range of concentrations [approximately 2–2000 ng/dL (0.069–69.4 nmol/L)] is important, we recommend using available data on biological variation to calculate performance criteria across the full range of expected values. Additional studies should be conducted to obtain biological variation data on testosterone from women and children, and revisions should be made to the analytical goals for these patient populations.

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

1972 ◽  
Vol 18 (3) ◽  
pp. 250-257 ◽  
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.

scholarly journals Statistical Analysis using traditional technique, Microsoft Excel and Predictive Analytic Software

10.17158/514 ◽  
2016 ◽  
Vol 19 (2) ◽  
Author(s):  
Jovelyn M. Durango ◽  
Carlito P. Yurango
Keyword(s):  
Statistical Analysis ◽  
Completion Time ◽  
Microsoft Excel ◽  
Instructional Tools ◽  
Traditional Technique ◽  
Significant Difference ◽  
Before And After ◽  
Computer Based ◽  
The One ◽  
Time Required

<p>The advent of technology has improved the way statistics is taught and learned. It is claimed that the use of computer-based instructional tools can actively explore the meaning of statistical concepts among the students, as well as enhance their learning experiences. This study aimed to compare three methods of statistical analysis namely, the traditional technique (use of the calculator), Microsoft Excel and Statistical Package for Social Sciences (SPSS) software. This investigation utilized the experimental design, specifically the One-Group Pretest – Posttest Design. There were six education students who self-assessed their attitude before and after the introduction of the use of various computation techniques and performed the statistical analysis considering also the completion time required for each process. Results of the study revealed an increase in the level of attitude among the respondents form the pretest to the posttest. Also, the cognitive level regardless of the approach was very high. However, the t-test failed to establish a significant difference in the attitude among the respondents. On the other hand, there were significant differences in both the test scores and completion time of the respondents in the three methods in favor of SPSS.</p><p> </p><p><strong>Keywords: </strong>Information technology, statistics, traditional technique, Microsoft excel, SPSS, comparative analysis, experimental research design, Davao City, Philippines. </p>

scholarly journals Analysis of Sensitometry in Radiographic Films using Optical Density Measurement

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Oladotun A. Ojo ◽  
Peter A. Oluwafisoye ◽  
Charles O. Chime
Keyword(s):  
Quality Control ◽  
Optical Density ◽  
Density Measurement ◽  
Absorbed Dose ◽  
Good Prediction ◽  
Radiographic Film ◽  
X Ray ◽  
Dose Curve ◽  
Optical Density Measurement ◽  
Before And After

The sensitivity of radiographic films is an important factor to the clarity and accuracy of X-ray exposure to patients during treatment or diagnostic periods. It is therefore important to do a thorough analysis of the sensitivity of the radiographic film before and after exposure to enhance the Quality Assurance (QA) and the Quality Control (QC), of the exposure procedures. The optical densities (OD) of each film was measured, with a densitometer model MA 5336, made by GAMMEX. These values were then converted to the absorbed dose (X mGy), which is the amount of dose absorbed by each patient. The optical density versus the dose curve, followed the expected pattern, showing a good prediction from the General model, that the films employed in the exposures were of good quality and standard. Hence the optical density versus dose sensitometric curves depicts the outcome of the various films sensitivity after an exposure to the X-ray radiation through the patients.

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.

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