Performance Of Internal Quality Control Research Articles

Development and Implementation of an Internal Quality Control Sample to Standardize Oligomer-Based Diagnostics of Alzheimer's Disease.

Protein misfolding and aggregation are pathological hallmarks of various neurodegenerative diseases. In Alzheimer's disease (AD), soluble and toxic amyloid-β (Aβ) oligomers are biomarker candidates for diagnostics and drug development. However, accurate quantification of Aβ oligomers in bodily fluids is challenging because extreme sensitivity and specificity are required. We previously introduced surface-based fluorescence intensity distribution analysis (sFIDA) with single-particle sensitivity. In this report, a preparation protocol for a synthetic Aβ oligomer sample was developed. This sample was used for internal quality control (IQC) to improve standardization, quality assurance, and routine application of oligomer-based diagnostic methods. We established an aggregation protocol for Aβ1-42, characterized the oligomers by atomic force microscopy (AFM), and assessed their application in sFIDA. Globular-shaped oligomers with a median size of 2.67 nm were detected by AFM, and sFIDA analysis of the Aβ1-42 oligomers yielded a femtomolar detection limit with high assay selectivity and dilution linearity over 5 log units. Lastly, we implemented a Shewhart chart for monitoring IQC performance over time, which is another important step toward quality assurance of oligomer-based diagnostic methods.

Analytical method validation of pooled TSH reagent in clinical biochemistry laboratory

Introduction and Aim: In today's world, laboratories are a crucial aspect of health-care services, and the biochemistry section contributes the most testing parameters. To our knowledge, this is the first attempt to verify and validate a pooled TSH reagent kit. The present study is aimed to pool and validate the pooled TSH reagent kit for testing and compare its performance with the intact manufactured supplied reagent kit. Materials and Methods: Performed in a medium-sized biochemistry laboratory with a daily sample load of 1000-1200. Before disposal, the TSH reagent with the available dead-volume was pooled in a new kit. We gathered three used TSH reagent kits from the same lot series and pooled the dead-volume to generate one functioning reagent. Before assessing the analytical performance of pooled TSH reagent, this was placed onto the instrument, calibrated, and Quality control ran. The CLIA procedure for testing method specification was used to evaluate the reagent's performance, which included accuracy, precision, measurement range, reference range, recovery, interference, and method comparison. Results: With a high degree of analytical precision, the obtained reference range was within the manufacturer's range (r2=0.99). Precision was comparable with the manufacturer claim; inter-assay variation (1.90% CV), Intra-assay variation (1.50% CV) and overall (1.33% CV). AMR found to be as true as established by the manufacturer - 0.005-100 µIU/mL. Internal quality control performance; level 2 & level 3 variations of 1.58% and 1.20% respectively. Conclusion: A pooled TSH reagent was precise, accurate as compared to the manufacturer Reagent kit. This will reduce the number of the reagent procurement by the laboratory with documented high kit utility rate (Kit efficiency Index) ranging between 90-100%.

Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance

BackgroundLaboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases. MethodsWe evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis. ResultsOur study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs. ConclusionLaboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.

Sigma Metrics: A Valuable Tool for Evaluating the Performance of Internal Quality Control in Laboratory.

Background Six Sigma is a widely accepted quality management system that provides an objective assessment of analytical methods and instrumentation. Six Sigma scale typically runs from 0 to 6, with sigma value above 6 being considered adequate and 3 sigma being considered the minimal acceptable performance for a process. Methodology Sigma metrics of 10 biochemistry parameters, namely glucose, triglycerides, high-density lipoprotein (HDL), albumin, direct bilirubin, alanine transaminase, aspartate transaminase, urea nitrogen, creatinine and uric acid, and hematology parameters such as hemoglobin (Hb), total leucocyte count (TLC), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and platelet were calculated by analyzing internal quality control (IQC) data of 3 months (June–August 2019). Results Sigma value was found to be > 6 for triglyceride, HDL, Hb, TLC, and MCH, signifying excellent results and no further modification with respect to IQC. Sigma value was between 3 and 6 for glucose, albumin, creatinine, uric acid, PCV, and MCHC, implying the requirement of improvement in quality control (QC) processes. Sigma value of < 3 was seen in AST, ALT, direct bilirubin, urea nitrogen, platelet, and MCV, signifying suboptimal performance. Discussion Six Sigma provides a more quantitative framework for evaluating process performance with evidence for process improvement and describes how many sigmas fit within the tolerance limits. Thus, for parameters with sigma value < 3, duplicate testing of the sample along with three QCs three times a day may be used along with stringent Westgard rules for rejecting a run.Conclusion Sigma metrics help assess analytical methodologies and augment laboratory performance.

A retrospective study to evaluate the performance of internal quality control in a biochemistry laboratory using sigma metrics

To evaluate the performance of internal quality control in a biochemistry laboratory using Sigma metrics. Data were extracted from December 2018 to November 2019 from a Tertiary care hospital, Coimbatore. The data regarding internal quality control (IQC) and external quality assurance scheme (EQAS) were collected for 14 biochemical parameters. Sigma metrics were calculated using total allowable error (TEa), Coefficient of variance (CV) and percent bias for the above mentioned parameters. Quality goal index (QGI) ratio was used to analyze the reason for the lower sigma in analytes.Coefficient of variance (CV) from the calculated laboratory means and calculated standard deviation. Sigma metrics was calculated from CV%, TEa and Bias. Quality goal index ratio was calculated from CV% and Bias.In this study four parameters AST, ALP, Triglycerides and Amylase with sigma values &amp;#62;6; Six parameters Total Protein, ALT, HDL, Glucose, Calcium and Cholesterol with sigma values 3 to 6 and four parameters Albumin, Uric acid, Creatinine, and Urea with sigma values &amp;#60;3. For all analytes &amp;#60;6 sigma level, the quality goal index (QGI) was &amp;#60;0.8 indicating the parameters which needed improvement in imprecision, except for total protein and cholesterol whose QGI &amp;#62;1.2 indicated inaccuracy.In the present study Sigma value was highest for ALP (7.81) and lowest for Urea (1.27). Use of alternative methods can be done for the parameters whose sigma values less than 3 to improve their performance are recommended.

Laboratory Quality Management System and Quality Indicators Implementation Status as Perceived by Laboratory Professionals in Preparation for the Accreditation Process from Selected Government Hospitals of Ethiopia.

The aim was to produce quality results that clinical laboratories need to implement and maintain continuous quality improvement systems. In recent years, health organizations have increasingly prioritized the quality of laboratory services by implementing quality management systems (QMSs) and building quality improvement activities. Efforts to strengthen laboratory systems in the African region have received increased attention in recent years. Assessing the implementation levels of laboratory quality management system components is important to identify the gaps that need further improvements. A cross-sectional study design was used between March and May 2017 in selected government hospitals of Ethiopia, and sample size was determined using a finite population formula, and a proportional sampling technique was employed; a sample size of 184 (62%) was calculated from 295 laboratory professionals. All respondents were informed about the laboratory's experience in the quality management system implementation; of those, only 138 (79%) engaged in the implementation process. From the 18 selected laboratory quality management components, 5 were observed to have the list implementation status, which are: (1) Performance of internal quality control for all tests, (2) Development and communication of a quality manual for all tests, (3) Adequacy of storage space for the supplies, (4) Development of an action plan based on internal audit, and (5) monitoring of environmental conditions. Running quality controls for all types of tests became a headache in almost all laboratories. From 12 selected quality indicators studied in this research, the 5 indicators with either poor or very poor performance outcome were: control of documents 136 (77.7%), control of records 123 (70.3%), development of manuals and policies 122 (69.7%), development of process and procedures 120 (68.6%), and internal communication 114 (65.1%). This research indicated the top 3 LQMS components with either poor or very poor implementation status: (1) Performance of internal quality controls for all tests, (2) Development and communication of quality manuals for all tests, (3) Adequacy of storage space for the supplies. Of the quality indicators studied, control of documents was perceived to have poor performance outcome by 136 (77.7%) of study participants.

Sigma Metrics used to Evaluate the Performance of Internal Quality Control in a Clinical Biochemistry Laboratory.

The performance of 17 routine chemical detection methods was evaluated by the Sigma (σ) index, and separate quality control standards were established according to the sigma values of different detection methods. The internal quality control (IQC) and external quality assessment (EQA) data of 17 assays in the biochemical laboratory of our hospital were collected from January to June 2019. Referring to the total allowed error (TEa) standards established in the Health Industry Standards of the People's Republic of China (WS/T 403-2012), the sigma metric of each assay was calculated, the performance level for inspection was evaluated, the quality goal index (QGI) was calculated for items with analysis performance < 5 sigma, and the main causes of poor performance were determined to guide quality improvement. For level 1 internal quality control (IQC), five assays (AMY, Crea, UA, TP, and Na) showed a performance of ≥ 6 sigma levels. Five assays (GGT, LDH, ALP, K, and Ca) had a performance lower than 3 sigma. For level 2 IQC, nine assays (ALT, AST, CK, AMY, Crea, UA, TP, Na, and Mg) achieved 6 sigma, and four assays (GGT, LDH, ALP, and K) achieved less than 3 sigma. Among the 12 assays with a sigma value < 5, the precision of 1 assay should be improved first, the accuracy of 6 assays should be improved next, and both the precision and the accuracy of 5 assays should be improved. The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules.

Towards harmonization of external quality assessment/proficiency testing in hemostasis.

Quality in diagnostic testing represents a key target of laboratory medicine, for which an assurance around the quality of testing is expected from all involved in the process. Laboratories attempt to assure the quality of their testing by various processes, but especially by performance of internal quality control and external quality assessment (EQA). This is especially true for tests of hemostasis and coagulation. EQA in general provides information on test accuracy and on evaluation of long-term laboratory performance. EQA providers support laboratory performance by various means, including distribution of material for testing of analytes ("proficiency testing"), educational support through expert advice, distribution of publications or case series. Participation in EQA is often a laboratory accreditation requirement. This review aims to identify some of the strengths and weaknesses of EQA, and targets attempts towards harmonization of EQA practice, in order to achieve the best outcome for participant laboratories and, thus, for patients and their clinical care providers.

Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory.

OBJECTIVE:Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics.MATERIALS AND METHODS:This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters.RESULTS:For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy.CONCLUSION:This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.

Internal quality control in point-of-care testing: where's the evidence?

ISO 22870 standards require protocols for performance of internal quality control for all point-of-care testing devices and training of users in its theory and practice. However, the unique setting of point-of-care testing (i.e. processes conducted by non-scientific users) means that laboratory internal quality control programmes do not easily translate to point-of-care testing. In addition, while the evidence base for internal quality control within the laboratory has been increasing, the equivalent literature surrounding point-of-care testing is very limited. This has led to wide variation in what is considered acceptable practice for internal quality control at the point of care. Indeed, it has been demonstrated that internal quality control is an area of deficiency in point-of-care testing. Internal quality control protocols used at point-of-care testing should be defined based on risk management. The protocol will therefore be dependent on analyser complexity and availability of inbuilt system checks, the risk associated with release of an incorrect patient result as well as frequency of use. The emphasis should be on designing an effective internal quality control protocol as opposed to the inherent tendency of introducing high-frequency quality control. Typically a simple pass or fail criterion is used for internal quality control in point-of-care testing based on whether internal quality control results fall within assigned ranges. While simply taught, such criteria can require broad internal quality control ranges to decrease the probability of false rejection (also reducing the probability of error detection). Customized internal quality control ranges, two-tier acceptance systems and assay-specific internal quality control can be used to improve error detection rates.

Internal quality control of a turbidimetric immunoassay for canine serum C-reactive protein based on pooled patient samples.

Optimized internal quality control (IQC) procedures are important to ensure that only results without medically important errors are used for medical decision-making and to ensure that unnecessary rejection of valid analytical runs is avoided. Additionally, estimates of the analytical performance can be derived from IQC data. In the absence of available species-specific standards of a compound, the use of alternative control materials based on patient samples is a possibility, although investigations on the suitability of this approach are needed. The objective of the study was to plan and implement a simple IQC procedure with control material based on pooled canine serum samples for a turbidimetric immunoassay (TIA) for the determination of human C-reactive protein (CRP) that recently was validated for the determination of canine serum CRP, and to assess the clinical analytical performance of the assay. Proposed guidelines for the planning and implementation of IQC procedures were followed by using 2 control materials. Quality requirements of the assay were defined objectively by means of available data on biological variation, and goals for IQC performance were defined according to recommendations (probability of error detection [P(ed)] >.90 and of false rejection [P(fr)] <.05). Analytical performance was evaluated by means of medical decision charts. The control rule of 1(2.5s) (ie, rejection of the analytical run if at least 1 of 2 control materials deviates from the mean by more than 2.5 SD) fulfilled the criteria of predicted IQC performance (P(ed) =.94-1.00, P(fr) =.03). The IQC method was successfully implemented over a 14-week period. The observed coefficient of variation in the period of monitoring was 3.8% (low) and 2.9% (high), which equals excellent analytical performance. It was possible to plan and implement a simple IQC procedure for the CRP-TIA with control materials based on canine serum samples that fulfilled the criteria of high error detection and low false rejection of valid analytical runs. The assay showed excellent long-term analytical performance over a 14-week period.