Implementation of a multirule, multistage quality control program in a clinical laboratory computer system

Eggert, Arthur A.; Westgard, James O.; Barry, Patricia L.; Emmerich, Kenneth A.

doi:10.1007/bf00993007

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…The frequency would be required to change as 2 (12 hourly) or 4 (6 hourly) runs per day (instead of 3). In addition, 4 or 2 control measurements of each level (L2 and L3) are required in each run respectively, if runs are 2 or 4 in numbers per day [13][14][15][16].…”

Section: Resultsmentioning

confidence: 99%

Application of Sigma Metrics Analysis for the Assessment and Modification of Quality Control Program in the Clinical Chemistry Laboratory of a Tertiary Care Hospital

Iqbal

Mustansar

2016

Ind J Clin Biochem

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Sigma is a metric that quantifies the performance of a process as a rate of Defects-Per-Million opportunities. In clinical laboratories, sigma metric analysis is used to assess the performance of laboratory process system. Sigma metric is also used as a quality management strategy for a laboratory process to improve the quality by addressing the errors after identification. The aim of this study is to evaluate the errors in quality control of analytical phase of laboratory system by sigma metric. For this purpose sigma metric analysis was done for analytes using the internal and external quality control as quality indicators. Results of sigma metric analysis were used to identify the gaps and need for modification in the strategy of laboratory quality control procedure. Sigma metric was calculated for quality control program of ten clinical chemistry analytes including glucose, chloride, cholesterol, triglyceride, HDL, albumin, direct bilirubin, total bilirubin, protein and creatinine, at two control levels. To calculate the sigma metric imprecision and bias was calculated with internal and external quality control data, respectively. The minimum acceptable performance was considered as 3 sigma. Westgard sigma rules were applied to customize the quality control procedure. Sigma level was found acceptable (C3) for glucose (L2), cholesterol, triglyceride, HDL, direct bilirubin and creatinine at both levels of control. For rest of the analytes sigma metric was found\3. The lowest value for sigma was found for chloride (1.1) at L2. The highest value of sigma was found for creatinine (10.1) at L3. HDL was found with the highest sigma values at both control levels (8.8 and 8.0 at L2 and L3, respectively). We conclude that analytes with the sigma value \3 are required strict monitoring and modification in quality control procedure. In this study application of sigma rules provided us the practical solution for improved and focused design of QC procedure.

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Section: Resultsmentioning

confidence: 99%

Application of Sigma Metrics Analysis for the Assessment and Modification of Quality Control Program in the Clinical Chemistry Laboratory of a Tertiary Care Hospital

Iqbal

Mustansar

2016

Ind J Clin Biochem

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“…The tool uses freely available software and can be implemented in any lab that has access to a windows computer running Skyline and R. Future studies will focus on the addition of more identification-free metrics – for example, variations in total ion current could be used to monitor spray stability. Also we would like to implement a set of multi-rules, analogous to the Westgard rules [21,33,34] used in the clinical regime, to determine when an experiment has statistically failed. These endeavors will be met with key challenges.…”

Section: Discussionmentioning

confidence: 99%

Implementation of Statistical Process Control for Proteomic Experiments Via LC MS/MS

Bereman

Johnson

Bollinger

et al. 2014

J. Am. Soc. Mass Spectrom.

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Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution); and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies.

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“…Westgard's rules include additional control limits at ±1 and ±2 SD that enable the laboratory to monitor control values for both dispersion and trends in the data that may develop over time. These control rules can be either applied to data plotted on an L-J chart and observed by visual inspection or programmed into a microcomputer for assessment (Eggert et al 1987).…”

Section: Figurementioning

confidence: 99%

Development, Application, and Quality Control of Serology Assays Used for Diagnostic Monitoring of Laboratory Nonhuman Primates

Simmons

2008

ILAR Journal

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The careful development, validation, and implementation of serodiagnostic assays can provide reliable results that make them a valuable tool in microbial quality control for nonhuman primates. This article includes identification and description of the components of assay development, including formulas for calculating the number of positive serum samples needed for assay validation and methods for calculating their diagnostic sensitivity and specificity. To ensure that assays are performing within predetermined specifications, there must be a quality control system that includes appropriate system and sample suitability controls as well as mechanisms to track assay performance over time. The section on quality assurance includes definitions of precision and accuracy in assay performance, and how to interpret these two factors using the Levey-Jennings chart, Westgard's rules, and other monitoring methods. Because all serologic assays are prone to false positive and false negative results, it is essential to interpret all diagnostic test results using both the expected prevalence of disease in the population and the population-specific assay performance characteristics that are determined during assay validation. The discussion on interpreting diagnostic test results also includes guidelines for calculating the positive and negative predictive values of an assay and for interpreting results based on the disease prevalence of the test population. A glossary provides definitions of commonly used terms.

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Implementation of a multirule, multistage quality control program in a clinical laboratory computer system

Cited by 13 publications

References 8 publications

Application of Sigma Metrics Analysis for the Assessment and Modification of Quality Control Program in the Clinical Chemistry Laboratory of a Tertiary Care Hospital

Application of Sigma Metrics Analysis for the Assessment and Modification of Quality Control Program in the Clinical Chemistry Laboratory of a Tertiary Care Hospital

Implementation of Statistical Process Control for Proteomic Experiments Via LC MS/MS

Development, Application, and Quality Control of Serology Assays Used for Diagnostic Monitoring of Laboratory Nonhuman Primates

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