Search

When traditional statistical quality control protocols, represented by the Westgard protocol were applied to infectious disease serology, the rejection limits were questioned because of the high rejection probability. We first define the probability of false rejection (Pfr) and error detection (Ped) for infectious disease serology. QC data in 6 months were collected and the Pfr of each rule in the Westgard protocol and Rilibak protocol was evaluated. Then, as improvements, we chose different rules for negative and positive QC data to constitute an asymmetric protocol, furthermore, while reagent lot changes, the mean value of QC protocol is reset with the first 15 QC results of new lot reagent. QC materials and Standard Reference Materials were tested synchronously in the next 6 months, to verify whether the Pfr and Ped of the asymmetric protocol could meet the requirement. Protocol 1 exhibited the higher level of rejection rate among the two protocols, especially after reagent lot changes; Pfr below the lower control limit (LCL) was 1.39–21.78 times higher than the upper control limit (UCL); false rejections were more likely to occur in negative QC data, with Pfr-total of 27–65%. The asymmetric protocol can significantly reduce the proportion of analytes with Pfr by over 20%. Systematic error due to reagent lot changes and random error due to routine QC data variation were considered potential factors for excessive Pfr. Asymmetric QC protocol that can reduce Pfr by different control limits for negative and positive QC data. [ABSTRACT FROM AUTHOR]

Abstract When traditional statistical quality control protocols, represented by the Westgard protocol were applied to infectious disease serology, the rejection limits were questioned because of the high rejection probability. We first define the probability of false rejection (Pfr) and error detection (Ped) for infectious disease serology. QC data in 6 months were collected and the Pfr of each rule in the Westgard protocol and Rilibak protocol was evaluated. Then, as improvements, we chose different rules for negative and positive QC data to constitute an asymmetric protocol, furthermore, while reagent lot changes, the mean value of QC protocol is reset with the first 15 QC results of new lot reagent. QC materials and Standard Reference Materials were tested synchronously in the next 6 months, to verify whether the Pfr and Ped of the asymmetric protocol could meet the requirement. Protocol 1 exhibited the higher level of rejection rate among the two protocols, especially after reagent lot changes; Pfr below the lower control limit (LCL) was 1.39–21.78 times higher than the upper control limit (UCL); false rejections were more likely to occur in negative QC data, with Pfr-total of 27–65%. The asymmetric protocol can significantly reduce the proportion of analytes with Pfr by over 20%. Systematic error due to reagent lot changes and random error due to routine QC data variation were considered potential factors for excessive Pfr. Asymmetric QC protocol that can reduce Pfr by different control limits for negative and positive QC data.

Introduction: Internal Quality Control (IQC) is run to assess the day-to-day performance of an analytical process, ensuring the reliability of patient results. However, commercial internal quality materials can be expensive and inaccessible for many laboratories. Therefore, there is a need for an effective and economical alternative to commercial IQC. Aim: To evaluate the effectiveness and stability of pooled sera as an IQC material. Materials and Methods: A laboratory study was conducted over a period of 50 days, from January 10 to February 28, 2023, in the Department of Biochemistry at Dr. Rajendra Prasad Government Medical College (DRPGMC), Kangra, Himachal Pradesh, India. Serum samples (150 μL) were collected from 100 leftover samples over a span of 10 days. The serum was thoroughly mixed and distributed into 75 aliquots. Commercial IQC was also reconstituted and distributed into 75 aliquots. Both the commercial and pooled sera IQC were stored at -20°C. One aliquot of pooled sera was analysed daily for 16 routine biochemistry parameters, along with the commercial IQC, for the entire 50-day period. The results from both materials were interpreted using L-J chart and Westgard multirule. The stability of pooled sera for these routine parameters was assessed by comparing the mean values of the biochemical parameters on day 1 and day 50. Data analysis was performed using Statistical Package for the Social Sciences (SPSS) version 20.0 software, and a paired t-test with a p-value

Introduction: Internal Quality Control (IQC) is run to assess the day-to-day performance of an analytical process, ensuring the reliability of patient results. However, commercial internal quality materials can be expensive and inaccessible for many laboratories. Therefore, there is a need for an effective and economical alternative to commercial IQC. Aim: To evaluate the effectiveness and stability of pooled sera as an IQC material. Materials and Methods: A laboratory study was conducted over a period of 50 days, from January 10 to February 28, 2023, in the Department of Biochemistry at Dr. Rajendra Prasad Government Medical College (DRPGMC), Kangra, Himachal Pradesh, India. Serum samples (150 µL) were collected from 100 leftover samples over a span of 10 days. The serum was thoroughly mixed and distributed into 75 aliquots. Commercial IQC was also reconstituted and distributed into 75 aliquots. Both the commercial and pooled sera IQC were stored at -20°C. One aliquot of pooled sera was analysed daily for 16 routine biochemistry parameters, along with the commercial IQC, for the entire 50-day period. The results from both materials were interpreted using L-J chart and Westgard multirule. The stability of pooled sera for these routine parameters was assessed by comparing the mean values of the biochemical parameters on day 1 and day 50. Data analysis was performed using Statistical Package for the Social Sciences (SPSS) version 20.0 software, and a paired t-test with a p-value <0.05 was considered statistically significant. The bias percentage for each pooled sera parameter was calculated and compared with the desirable bias for clinical significance. Results: Analytical errors were observed during the study period for Alkaline Phosphatase (ALP), urea, sodium, potassium, and chloride. The nature and number of analytical errors observed with both materials were the same, with Westgard rule 13s detecting random errors for ALP and rule 22s detecting systematic errors for urea and serum electrolytes (sodium, potassium, and chloride). Difference in the mean value of alanine transaminase, aspartate transaminase, and bilirubin, on day 1 and day 50 was statistically significant with p-values 0.035, 0.04, and 0.024, respectively. When these parameters were assessed for clinical bias, they were found to be clinically insignificant. Conclusion: Pooled serum is an effective and stable alternative IQC material for daily performance assessment of analytical processes. In-house IQC or pooled sera not only reduces the cost of quality control but also helps maintain continuity in the absence of commercial IQC, preventing any disruptions in the control system over an extended period. [ABSTRACT FROM AUTHOR]

Background: Quality control (QC) validation is an important step in the laboratory harmonization process. This includes the application of statistical QC requirements, procedures, and control rules to identify and maintain ongoing stable analytical performance. This provides confidence in the production of patient results that are suitable for clinical interpretation across a network of veterinary laboratories. Objectives: To determine that a higher probability of error detection (Ped) and lower probability of false rejection (Pfr) using a simple control rule and one level of quality control material (QCM) could be achieved using observed analytical performance than by using the manufacturer's acceptable ranges for QCM on the Sysmex XT‐2000iV hematology analyzers for veterinary use. We also determined whether Westgard Sigma Rules could be sufficient to monitor and maintain a sufficiently high level of analytical performance to support harmonization. Methods: EZRules3 was used to investigate candidate QC rules and determine the Ped and Pfr of manufacturer's acceptable limits and also analyzer‐specific observed analytical performance for each of the six Sysmex analyzers within our laboratory system using the American Society of Veterinary Clinical Pathology (ASVCP)‐recommended or internal expert opinion quality goals (expressed as total allowable error, TEa) as the quality requirement. The internal expert quality goals were generated by consensus of the Quality, Education, Planning, and Implementation (QEPI) group comprised of five clinical pathologists and seven laboratory technicians and managers. Sigma metrics, which are a useful monitoring tool and can be used in conjunction with Westgard Sigma Rules, were also calculated. Results: The QC validation using the manufacturer's acceptable limits for analyzer 1 showed only 3/10 measurands reached acceptable Ped for veterinary laboratories (>0.85). For QC validation based on observed analyzer performance, the Ped was >0.94 using a 1‐2.5s QC rule for the majority of observations (57/60) across the group of analyzers at the recommended TEa. We found little variation in Pfr between manufacturer acceptable limits and individual analyzer observed performance as this is a characteristic of the rule used, not the analyzer performance. Conclusions: An improved probability of error detection and probability of false rejection using a 1‐2.5s QC rule for individual analyzer QC was achieved compared with the use of the manufacturers' acceptable limits for hematology in veterinary laboratories. A validated QC rule (1‐2.5s) in conjunction with sigma metrics (>5.5), desirable bias, and desirable CV based on biologic variation was successful to evaluate stable analytical performance supporting continued harmonization across the network of analyzers. [ABSTRACT FROM AUTHOR]

Shui Fu,1 Liang Zhang,1 Qi-Lei Hu,1,2 Zuo-Jie Li3 1Department of Clinical Laboratory, Linping Campus, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 311000, People’s Republic of China; 2Quality Management Section, Linping Campus, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 311100, People’s Republic of China; 3Department of Clinical Laboratory, The People’s Hospital of Cangnan Zhejiang, Cangnan County, Zhejiang, 325800, People’s Republic of ChinaCorrespondence: Zuo-Jie Li, Department of Clinical Laboratory, The People’s Hospital of Cangnan Zhejiang, No. 2288 Yucang Road, Cangnan County, Zhejiang, 325800, People’s Republic of China, Tel +86 577-64810162, Email acdemicarticle@126.comObjective: This study establish and evaluate an internal quality control system for erythrocyte sedimentation rate (ESR) by a “relay” mode based on samples from relevant patients.Methods: The method for establishing a new internal quality control system for ESR by a “relay” mode based on patient’s samples was executed from February 2021 to July 2021. In this paper, a total of 219 outpatients were recruited for ESR determination, and their blood samples were stored at 4 °C or room temperature for 24 h. Subsequently, the samples were re-measured for ESR, and the re-measured values were compared with the initial values. The patient samples (15± 1mm/h and 50± 3mm/h) were selected after the TEST1 ESR analyzer was calibrated, and were stored overnight at 4 °C and measured again the following day. The percentage deviation was determined and entered into the quality control management module for internal quality control. Next, we analyzed the median distribution trend of the patients’ ESR values measured by our laboratory every day over five months, as well as the external quality assessment (EQA) results for ESR obtained from the National Center for Clinical Laboratories (NCCL).Results: The ESR of the room temperature samples after 24 h of storage had significantly decreased (P=0.001), while there was no noticeable difference for those stored at 4 °C (P=0.197). Results of the internal quality control in March were satisfactory, and there was no significant deviation in the median ESR relay results within five months. Besides, the EQA results for the ESR data obtained from NCCL were excellent.Conclusion: As a precise and practical new method, the ESR relay internal quality control method can be used to scientifically determine the stability and accuracy of the TEST1 ESR analyzer.Keywords: erythrocyte sedimentation rate, ESR, fully automatic ESR analyzer, quality control, fresh blood sample, Westgard rules

Clinical laboratories are mandated to deliver accurate, reliable, timely and correctly reported result which, used in decision making for disease screening, diagnosis and monitoring. With aid of six sigma principles and metrics it is possible to assess the quality laboratory process and the quality control that is needed to ensure that the desired quality is achieved. Thus, this study was undertaken to evaluate the performance of biochemical parameters by calculating the sigma metrics of individual parameters using internal quality control (IQC) and Proficiency Testing (PT) results. The sigma metrics of 21 clinical chemistry parameters were calculated from COBAS 6000 analyzer with internal quality control (IQC) materials and external quality assurance scheme (EQAS) performance in national clinical chemistry laboratory for the period of six months. We obtained an excellent performance (≥ 6 sigma) for test parameters amylase pancreatic, amylase total, HDL, magnesium, AST, triglyceride, total bilirubin and ALT in both levels of quality control. Urea, creatinine and chloride were failed to meet the minimal sigma performance for both level 1 and 2. Sigma values of 3–6 were observed for ALP, Direct bilirubin, total protein, albumin, glucose, potassium, and phosphate with both levels of quality control. Though, stringent IQC strategy is not mandatory for analytes that scored sigma value ≥ 6. However, continuous monitoring quality control is required for renal function tests and process improvement will be designed for those with poor sigma values. [ABSTRACT FROM AUTHOR]

Introduction: Quality Control Management (QCM) in clinical laboratories is crucial for ensuring reliable results in analytical measurements, with biological variation being a key factor. The study focuses on assessing the analytical performance of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) system for Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), and Hepatitis C (HCV). Five models proposed between 1999 and 2014 offer different approaches to evaluating analytical quality, with Model 2 based on biological variation and Model 5 considering the current state of the art. The study evaluates the RT-PCR system's analytical performance through Internal Quality Control (IQC) and External Quality Control (EQC)., Materials and Methods: The Laboratório Central de Saúde Pública do Estado do Ceará (LACEN-CE) conducted daily IQC using commercial kits, and EQC was performed through proficiency testing rounds. Random error, systematic error, and total error were determined for each analyte., Results: Analytical performance, assessed through CV and random error, met specifications, with HIV and HBV classified as "desirable" and "optimal." EQC results indicated low systematic error, contributing to total errors considered clinically insignificant., Conclusion: The study highlights the challenge of defining analytical specifications without sufficient biological variability data. Model 5 is deemed the most suitable. The analytical performance of the RT-PCR system for HIV, HBV, and HCV at LACEN-CE demonstrated satisfactory, emphasizing the importance of continuous quality control in molecular biology methodologies., (Copyright © 2024 International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). All rights reserved.)

Background Multirules are often employed to monitor quality control (QC). The performance of multirules is usually determined by simulation and is difficult to predict. Previous studies have not provided computer code that would enable one to experiment with multirules. It would be helpful for analysts to have computer code to analyze rule performance. Objective To provide code to calculate power curves and to investigate certain properties of multirule QC. Methods We developed computer code in the R language to simulate multirule performance. Using simulation, we studied the incremental performance of each rule and determined the average run length and time to signal. Results We provide R code for simulating multirule performance. We also provide a Microsoft Excel spreadsheet with a tabulation of results that can be used to create power curves. We found that the R4S and 10x rules add very little power to a multirule set designed to detect shifts in the mean. Conclusion QC analysts should consider using a limited-rule set. [ABSTRACT FROM AUTHOR]

Introduction: The diagnosis of diabetes mellitus is based on suitable cut-off values of specific biomarkers, such as the concentration of glucose in plasma. The German Diabetes Association has very recently published a clinical practice guideline on the definition, classification and diagnosis of diabetes mellitus that recommends measurements of plasma glucose concentration have an imprecision defined as a minimal difference (MD) of at a fasting plasma glucose concentration of 7.0 mmol/L. To obtain reliable values for the MD, we investigated long-term and short-term measurement uncertainty. Methods: The imprecision was determined by two approaches: (1) a long-term dataset with imprecision based on the Guideline of the German Medical Association on Quality Assurance in Medical Laboratory Examinations (Rili-BAEK), in a medical laboratory operating 24/7, using internal quality control (IQC) data for four concentrations during a 10-year period; and (2) a detailed short-term dataset with imprecision assessed by hourly measurements of control materials. These datasets were used to calculate the MD cut-off (MDcut-off) as: MD cut-off = k × SD = 2 × SD , where SD is the standard deviation and k = 2 represents a confidence level of 95%. Results: The MDcut-off of ≤ 0.7 mmol/L at a fasting plasma glucose concentration of 7.0 mmol/L (MDcut-off 7.0) for the long-term and the short-term approaches were 0.44 and 0.40 mmol/L, respectively. The MDcut-off 7.0 from both approaches was therefore below the recommended value of 0.7 mmol/L. It was noted that the variability in performance within and between instruments can be covered by reporting the long-term MDcut-off 7.0 across all connected instruments. In this study, stable results for the MDcut-off 7.0 were obtained after 1 year. Conclusion: Imprecision as measured by IQC data is remarkably stable over many years of operation. Current imprecision assessment usually focuses on only single instruments, whereas clinicians perceive the measurement as the result of the combined analytical performance of all instruments used for a certain assay. In the clinical setting, the MD may be a more useful measure of imprecision, and we suggest deriving the MDcut-off combined from all instruments and control cycles that are used in the patient care setting for a given analyte. [ABSTRACT FROM AUTHOR]

A zero defects goal was implemented in the clinical laboratory settings using a six-sigma model. Daily Internal Quality Control (IQC) and external quality control data from April-September 2023 was extracted to calculate the sigma metrics of 21 biochemical analytes based on Total Error Allowable (TEa), % bias and co-efficient of variation percent (CV%). A retrospective comparative study was conducted in the department of Clinical Biochemistry at Kanva Diagnostic Services Pvt. Ltd, Bengaluru, India. The analytical performance of the 21 biochemical analytes was tested on Cobas 6000 and C311 analyzers. Quality Goal Index (QGI) and root cause analysis was calculated to infer the reason for the deviation of six sigma. Method decision charts were plotted to show the comparison of the problem analytes on both the analyzers. On Cobas 6000 at level 1 IQC, out of 21 analytes, 10 analytes showed σ>6 and 10 analytes showed σ 3-6 and on C311, 15 analytes which showed σ>6 and 6 analytes that showed σ 3-6. On Cobas 6000 at level 2 IQC, out of 21 analytes, 12 analytes showed σ>6 and 8 analytes showed σ 3-6 and on C311 17 analytes showed σ>6 and 4 analytes showed σ 3-6. Creatinine failed to meet minimal sigma performance at both levels of IQC on Cobas 6000., Competing Interests: There is no conflict of interest between the authors., (© 2023 Biomedical Informatics.)

Background: A general trend towards conducting infectious disease serology testing in centralized laboratories means that quality control (QC) principles used for clinical chemistry testing are applied to infectious disease testing. However, no systematic assessment of methods used to establish QC limits has been applied to infectious disease serology testing. Methods: A total of 103 QC data sets, obtained from six different infectious disease serology analytes, were parsed through standard methods for establishing statistical control limits, including guidelines from Public Health England, USA Clinical and Laboratory Standards Institute (CLSI), German Richtlinien der Bundesärztekammer (RiliBÄK) and Australian QConnect. The percentage of QC results failing each method was compared. Results: The percentage of data sets having more than 20% of QC results failing Westgard rules when the first 20 results were used to calculate the mean±2 standard deviation (SD) ranged from 3 (2.9%) for R4S to 66 (64.1%) for 10X rule, whereas the percentage ranged from 0 (0%) for R4S to 32 (40.5%) for 10X when the first 100 results were used to calculate the mean±2 SD. By contrast, the percentage of data sets with >20% failing the RiliBÄK control limits was 25 (24.3%). Only two data sets (1.9%) had more than 20% of results outside the QConnect Limits. Conclusions: The rate of failure of QCs using QConnect Limits was more applicable for monitoring infectious disease serology testing compared with UK Public Health, CLSI and RiliBÄK, as the alternatives to QConnect Limits reported an unacceptably high percentage of failures across the 103 data sets. [ABSTRACT FROM AUTHOR]

Objectives: In the United States, minimum standards for quality control (QC) are specified in federal law under the Clinical Laboratory Improvement Amendment and its revisions. Beyond meeting this required standard, laboratories have flexibility to determine their overall QC program.Methods: We surveyed chemistry and immunochemistry QC procedures at 21 clinical laboratories within leading academic medical centers to assess if standardized QC practices exist for chemistry and immunochemistry testing.Results: We observed significant variation and unexpected similarities in practice across laboratories, including QC frequency, cutoffs, number of levels analyzed, and other features.Conclusions: This variation in practice indicates an opportunity exists to establish an evidence-based approach to QC that can be generalized across institutions. [ABSTRACT FROM AUTHOR]

Sigma metrics have become a useful tool for all parts of the quality control (QC) design process. Through the allowable total error model of laboratory testing, analytical assay performance can be judged on the Six Sigma scale. This not only allows benchmarking the performance of methods and instruments on a universal scale, it allows laboratories to easily visualize performance, optimize the QC rules and numbers of control measurements they implement, and now even schedule the frequency of running those controls. [ABSTRACT FROM AUTHOR]

Background: Many labs have not yet selected the most appropriate Westgard Quality Control (QC) rule for each test. This is mainly due to the apparent complexity of the matter. Methods: From the Westgard OPSpecs Charts QC planning tool and the Sigma Metrics formula's it was deduced that every Westgard rule has its own Sigma value. This was converted to an easy three-step road map to optimal Westgard QC rules. Results: The road map provided is based on Sigma Metrics that hold a definition of 'world class quality', at which no further effort to increase quality needs to be taken. Furthermore, it is shown that clinical chemical tests can be classified as 'good': quality at or above world class, 'bad': quality below world class but controllable with Westgard QC rules and 'ugly': quality not controllable with Westgard QC rules alone. Finally, practical tips of how to deal with this and related aspects are given. Conclusions: The use of the road map based on Sigma Metrics leads to fast and easy implementation of optimal Westgard QC rules. [ABSTRACT FROM AUTHOR]

Background. The commonly used Westgard control rules to detect systematic errors in one analytical run are far from perfect. Mean rules are more effective, but are in little use. We propose using a variant of mean rules, where we compare the confidence interval of the mean directly with the allowable systematic error. Methods. Probabilities of false rejection and error detection were calculated using standard Normal distribution functions. Results and conclusion. Confidence interval of the mean rules are more flexible and are more easy to practise than the traditional mean rules. They outperform the commonly used Westgard rules if more than one control specimen is analysed in each run. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Practical application of Sigma Metrics QC procedures in clinical chemistry," by C. H. Schoenmakers and colleagues in the 2011 issue.