External Quality Control Data Research Articles

Analysis of biochemical analytes using six sigma metrics with two analyzers at an Indian lab setting.

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.

A Significant Increase in the Incidence of Neonatal Hyperbilirubinemia and Phototherapy Treatment Due to a Routine Change in Laboratory Equipment.

Total serum bilirubin (TSB) analysis is pivotal for diagnosing neonatal hyperbilirubinemia. Because of a routine change in laboratory equipment, our TSB assay changed from a diazo to a vanadate oxidase method. Upon implementation, TSB results were substantially higher in newborns than expected based on the validation. To investigate the application of TSB and intermethod differences in neonates and their impact on phototherapy treatment. The diazo and vanadate methods were compared directly using neonatal and adult samples. Anonymized external quality control data were analyzed to explore interlaboratory differences among 8 commercial TSB assays. Clinical patient data were extracted from the medical records to investigate the number of newborns receiving phototherapy. The mean bias of the vanadate versus the diazo TSB method was +17.4% and +3.7% in neonatal and adult samples, respectively. External quality control data showed that the bias of commercial TSB methods compared with the reference method varied from -3.6% to +20.2%. Within-method variation ranged from 5.2% to 16.0%. After implementation of the vanadate TSB method, the number of neonates treated with phototherapy increased approximately threefold. Currently available TSB assays lack harmonization for the diagnosis of neonatal hyperbilirubinemia. Between-methods differences are substantially higher in neonatal compared with adult samples, highlighting the importance of including neonatal samples during assay validation. Close collaboration between laboratory specialists and clinicians is essential to prevent overtreatment or undertreatment upon the implementation of novel analyzers or assays. Also, harmonization of TSB assays, with an emphasis on neonatal application, is warranted.

A practical approach example to measurement uncertainty: Evaluation of 26 immunoassay parameters.

IntroductionMeasurement uncertainty is a non-negative parameter that characterizes the distribution of all values appropriate to the measured size and is associated with the measured result. In this study, we aimed to compare the results with various suggestions and produce more qualified results by calculating the measurement uncertainties of the immunoassays like fertility hormones, drug concentration tests, cardiac markers, thyroid function tests and tumour markers.Materials and methodsUncertainty calculation was made in accordance with the top-down approach according to Nordtest guide. The 12-month study of internal and external quality assessment results were used. The parameters of drug concentration tests were performed on the Abbott Architect c8000, other hormones/markers on the i2000 of the same brand.ResultsFactors that increased the measurement uncertainty of a test were due to external quality control data. The calculations showed that 13 of 26 parameters satisfied quality requirements. The highest uncertainty value, with 28% belonged to cancer antigen 19-9 test. The lowest value was calculated for prolactin with 8.3%. Dehydroepiandrosterone sulfate and phenytoin performed poorly in terms of measurement uncertainty, although internal and external quality control assessment results were considered favourable for both.ConclusionIt is recommended that the concept of measurement uncertainty, which plays an important role in the total quality performance of the laboratory, should be followed up by the clinical laboratory experts at certain time intervals and should be increased the awareness of clinicians about the subject.

Evaluation of clinical biochemical laboratory analytical process performance using sigma metrics and quality goal index

Background: Application of sigma matrics across clinical laboratory diagnostic process enables identification of errors in methods together with quality goal index can help point out the cause of unacceptable process performance. We believe the analytical performance of the clinical biochemistry laboratory standards do not meet the required quality according to six sigma standards. Aim and Objective: The study aimed at performance evaluation of clinical biochemical laboratory analytical process using sigma metrics and quality goal index. Methods: Sigma (σ) for the 20 analytes performed on HITACHI 7600 - 110 was calculated based on absolute bias, total allowable error (TEa) and coefficient of variation (CV). Two level internal quality control data obtained retrospectively from December 2020 to May 2021 was used to calculate the coefficient of variation. Bias was obtained from six month external quality control data. Total allowable error was obtained from three different sources. Quality goal index (QGI) was calculated to identify the cause of unwanted analyte performance. Results: Different sigma scores were obtained for analytes on different total allowable error standard, only triglyceride showed world class performance along all the three total allowable error standards. However, creatine kinase & total bilirubin showed world class on clinical laboratory improvement amendment (CLIA) and biological variable database (BVD). On the other hand, glucose, and aspertate amino transferase had poor or unacceptable performance on all the total allowable error standards. Conclusion: Six sigma is an excellent tool in evaluating the quality performance of clinical biochemistry laboratory analytical process, quality goal index can help identify the problem and westgard rules can help design individual quality improvement strategy.

Evaluation of the clinical chemistry tests analytical performance with Sigma Metric by using different quality specifications - Comparison of analyser actual performance with manufacturer data.

IntroductionThe interest in quality management tools/methodologies is gradually increasing to ensure quality and accurate results in line with international standards in clinical laboratories. Six Sigma stands apart from other methodologies with its total quality management system approach. However, the lack of standardization in tolerance limits restricts the advantages for the process. Our study aimed both to evaluate the applicability of analytical quality goals with Roche Cobas c 702 analyser and to determine achievable goals specific to the analyser used.Materials and methodsThe study examined under two main headings as Sigmalaboratory and Sigmaanalyser. Sigmalaboratory was calculated using internal and external quality control data by using Roche Cobas c 702 analyser for 21 routine biochemistry parameters and, Sigmaanalyser calculation was based on the manufacturer data presented in the package inserts of the reagents used in our laboratory during the study. Sigma values were calculated with the six sigma formula.ResultsConsidering the total number of targets achieved, Sigmaanalyser performed best by meeting all CLIA goals, while Sigmalaboratory showed the lowest performance relative to biological variation (BV) desirable goals.ConclusionsThe balance between the applicability and analytical assurance of “goal-setting models” should be well established. Even if the package insert data provided by the manufacturer were used in our study, it was observed that almost a quarter of the evaluated analytes failed to achieve even “acceptable” level performance according to BV-based goals. Therefore, “state-of-the-art” goals for the Six Sigma methodology are considered to be more reasonable, achievable, and compatible with today’s technologies.

Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

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.

Evaluation of Quality Control Data of Hormones Using Six Sigma Metrics Tool in Clinical Laboratory

Background: In health care system it is necessary to provide high quality and reliable test results to the patients. Many clinical laboratories are using six sigma as a tool to improve the quality control in health care system. Keeping this in mind, the present study was conducted using the quality control data of hormones under NABL(National Accreditation Board for Testing and Calibration Laboratories) which were assayed in our clinical laboratory.
 Materials and Methods: In this retrospective study, both the internal and external quality control data of 11 hormones were collected for a period of 6 months from April 2020 to September 2020 and the six sigma analysis was done.
 Results: Testosterone level 1(6.8), level 2(6.5) and Folate level1(6.9), level 2(6.6) showed sigma level more than 6 and hence excellent performance. The hormones, FT3 level 1(3.7), level 2(4.8), HCG level 2(3.6), TSH level 1(4.8), level 2(4.7) and Vitamin B12 level 1(4.4), level 2(4.5) showed average performance with sigma level between 3.5 and 6. The hormones, FT4 level 1(1.7), level 2(2), HCG level 1(2.2), Prolactin level 1(3), level 2(3.3), FSH level 1(1.9), level 2(2.0), LH level 1(2), level 2(1.9) and Progesterone level 1(3.4), level 2(3.3) showed poor performance with sigma level less than 3.5.
 Conclusion: Stringent rules need not be applied for hormones with sigma>6. Moreover, control limits can be relaxed to 3S so that false rejections can be minimized. For hormones with sigma< 6, internal QC rules have to be strictly applied and the root cause analysis has to be done. To conclude, six sigma metrics is a powerful quality control tool which helps to improve the performance of the clinical laboratory and hence the efficiency of the health cares system.

Use of Uncertainty of Measurement for Traceability of Test Results and Setting up of own Quality Goal for Methods having Lower Stability- A Tertiary Care Hospital study.

Uncertainty of measurement (UM) provides a quantitative estimate for traceability of test results. The Nordtest guide was applied for calculating UM of 26 analytes. For this, internal and external quality control data from July 2019 to April 2020 was used. UM of test results were compared to %TEa values of CLIA '2019, RiliBÄK, and Ricos. It was observed that UM for all analytes were below %TEa values of RiliBÄK. UM value of Albumin, Calcium and Sodium could not meet CLIA '2019 and Ricos guidelines. For results of Albumin, Calcium and Sodium to be traceable, more frequent quality control protocols resulted in decrease in bias. Quality goals were set for these three parameters. This helped in reduction of quality control cycles and optimum utilization of resources.

Evaluation of 20 clinical chemistry and 12 immunoassay analytes in terms of total analytical error and measurement uncertainty

In analytical quality management, target setting models that are selected by the purpose together with the error models that are applied correctly have critical importance. In our study, we aimed to compare the analytical performance characteristics of routine clinical chemistry and immunoassay tests with different target-setting models proposed by various organizations. Our study was performed with internal and external quality control data obtained using Beckman Coulter AU680 for clinical chemistry analytes and Roche Cobas 8000 autoanalyzer for immunoassay analytes. The total analytical error (TAE) was calculated by the formula TAH%=1.65×(CV%)+Bias%. Measurement uncertainty (MU) has been calculated adhering to the Nordtest guideline. Results were compared with BVEFLM, CLIA, RCPA, PRDEQA%, pUEQAS%, and permissible MU (pU%) data to investigate analytical performance qualities. When we compare the results of TAE and MU, MU was found to be higher than TAE for all analytes. ALT, AST, glucose, K, and triglycerides met all target values, showing the best performance. Taken together, our results show that CLIA for total analytical error and PRDEQA% and pUEQAS% for measurement uncertainty can match better than BVEFLM, RCPA and pU%. These test results should be evaluated with measurement uncertainty to avoid misdiagnosis. Appropriate specification limits should be defined for the examination of test methods that meet the objectives for fitness for clinical purposes.

Quality improvement in clinical biochemistry laboratory using six sigma metrics and quality goal index

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.

Sigma Metric Evaluation of Drugs in a Clinical Laboratory: Importance of Choosing Appropriate Total Allowable Error and a Troubleshooting Roadmap.

Objectives Stringent quality control is an essential requisite of diagnostic laboratories to deliver consistent results. Measures used to assess the performance of a clinical chemistry laboratory are internal quality control and external quality assurance scheme (EQAS). However, the number of errors cannot be measured by the above but can be quantified by sigma metrics. The sigma scale varies from 0 to 6 with “6” being the ideal goal, which is calculated by using total allowable error (TEa), bias, and precision. However, there is no proper consensus for setting a TEa goal, and influence of this limiting factor during routine laboratory practice and sigma calculation has not been adequately determined. The study evaluates the impact of the choice of TEa value on sigma score derivation and also describes a detailed structured approach (followed by the study laboratory) to determine the potential causes of errors causing poor sigma score. Materials and Methods The study was conducted at a clinical biochemistry laboratory of a central government tertiary care hospital. Internal and external quality control data were evaluated for a period of 5 months from October 2019 to February 2020. Three drugs (carbamazepine, phenytoin, and valproate) were evaluated on the sigma scale using two different TEa values to determine significant difference, if any. Statistical Analysis Bias was calculated using the following formula: Bias% = (laboratory EQAS result − peer group mean) × 100 / peer group mean Peer group mean sigma metric was calculated using the standard equation: Sigma value = TEa − bias / coefficient of variation (CV)%. Results Impressive sigma scores (> 3 sigma) for two out of three drugs were obtained with TEa value 25, while with TEa value 15, sigma score was distinctly dissimilar and warranted root cause analysis and corrective action plans to be implemented for both valproate and carbamazepine. Conclusions The current study evidently recognizes that distinctly different sigma values can be obtained, depending on the TEa values selected, and using the same bias and precision values in the sigma equation. The laboratories should thereby choose appropriate TEa goals and make judicious use of sigma metric as a quality improvement tool.

Klinik Laboratuvarımızın Toplam Kalite Performansının Altı-Sigma Yöntemiyle Değerlendirilmesi

Aim: In order to evaluate the performance of the clinical laboratories, a quality measurement method, The Six-Sigma Methodology, is used. We aimed to evaluate the analytical performance of our clinical laboratory by using the internal and external quality control data of 28 analytes, and by calculating their process sigma values. Methods: Sigma values of 28 analytes of our laboratory were determined from coefficient of variation (CV %) and bias for 3 subsequent months from December 2018 to January 2019. If the sigma values are >6, between 3 and 6, and <3, they are classified as »world-class«, »good« or »un-acceptable«, respectively. Results: 6 (21%) analytes; T Protein, Creatinin, Ca, Na, Cl, HbA1c; provided <3 sigma value, 1 (04%) analyte; lipase; provided between 3-6 value and 11 (39%) analytes; amylase, GGT, ALT, LDH, CRP, Lactate, BUN, Direct Bil., T Bil., CK and CK-MB; provided > 6 sigma value for both IQC1 and IQC2 levels for three consecutive months, respectively. Conclusions: In terms of Six-Sigma Metrics; our results were generally found as world-class or good. An appropriate quality control scheduling should be carried out for each test by using Six-Sigma Methodology in all clinical chemistry laboratories.

Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a 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.

The importance of measuring the uncertainty of HbA1c analysis

Aim: HbA1c, which reflects blood glucose levels in the last two to three months, has a significant role in the diagnosis, treatment and follow-up of diabetic patients. Measurement uncertainty is defined as the range of possible values, which comprises the measured level. This study aims at showing how to calculate the measurement uncertainty of HbA1c and informing clinicians on its significance for the efficient diagnosis, treatment and follow-up.Materials and Methods: The measurement uncertainty calculating model was used to determine the measurement uncertainty of HbA1c. This model comprises six steps and is constructed depending on European Accreditation Guidelines, described in Nordest Guidelines, Eurolab Technic Report, and ISO/DTS 21748 Guidebook. The HbA1c analysis was chromatographically studied with Trinity Biotech Premier Hb9210 analyzer. Besides internal and external quality control data of HbA1c tests done in Elazig City Hospital in the months between January 2019 and September 2019, data analyses were done to calculate the measurement uncertainty by using the HbA1c test results.Results: The measurement uncertainty of HbA1c was calculated as HbA1c±4.27% using a confidence interval of 95%. These results, were found to be lower than the total allowable error, determined by international organizations. Based on our results, the cut-off value of HbA1c of 6.5% had a measurement uncertainty between 6.2% and 6.8%.Conclusion: We consider that this study will guide the laboratory specialists, concerned with the suggestion that “medical laboratories are required to calculate the measurement uncertainty for quantitative results.” Furthermore, reporting the measurement uncertainty with test results will supply clinicians with information about measurement quality and contribute to creating awareness on this issue.

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

Background: Six sigma is a powerful tool which can be used by laboratories for assessing the method quality, optimizing Quality Control (QC) procedure, change the number of rules applied, and frequency of controls run .The aim of this study was to quantify the defects or errors in the analytical phase of laboratory testing by sigma metrics and then represent the sigma value in Method Decision Chart.Methods: A retrospective study was conducted in a tertiary care hospital in Bhubaneswar, India. The clinical chemistry laboratory has been NABL accredited for the past 5 years and strictly quality checked. Internal and external quality control data was collected for a period of six months from January - June 2018 for 20 biochemical analytes. Sigma metrics for each parameter was calculated and plotted on method decision chart.Results: The sigma metrics for level 2 indicated that 6 out of the 20 analytes qualified Six Sigma quality performance. Of these seven analytes failed to meet minimum sigma quality performance with metrics less than three and another seven analytes performance with sigma metrics was between three and six. For level 3, the data collected indicated that seven out of 20 analytes qualified Six Sigma quality performance, six analytes had sigma metrics less than 3 and seven analytes had sigma metrics between 3 and 6.Conclusion: In our study Sigma value was highest for amylase and lowest for potassium. Use of alternative methods and/ or change of reagents can be done for potassium to bring the sigma value within an acceptable range.

State-of-the-art for the measurement of seventeen haemostasis and thrombosis variables from external quality control data

State-of-the-art for the measurement of seventeen haemostasis and thrombosis variables from external quality control data

Uncertainty of measurement for 14 immunoassay analytes: application to laboratory result interpretation

Laboratory tests are an integral part of clinical decision making. Therefore, measurement uncertainty comes into prominence in the context of the accuracy of the laboratory result. This study aims to investigate measurement uncertainty of 14 immunoassay analytes, to compare them with different quality goals and to utilize them in the result interpretation. Measurement uncertainties of 14 immunoassay analytes were estimated by using internal and external quality control data by using Nordtest approach. Expanded uncertainties (U) were compared with allowable total error (TEa%), permissible relative deviation in the external quality assessment (PRDEQA%) and permissible expanded uncertainty for external quality assessment (pUEQAS%). Uncertainties were incorporated into the calculation of reference change values (RCV) and uncertainty adjusted reference intervals. RCVs of 14 analytes were calculated by three different methods reported by Harris, Clinical Laboratory Standards Institute (CLSI), and National Pathology Accreditation Advisory Council (NPAAC). Measurement uncertainties of TSH, estradiol, LH, progesterone, prolactin, and vitamin B12 were within defined allowable limits. Uone-sided FT3 and Uone-sided ferritin exceeded defined TEa% but UFT3 and Uferritin were found below the limits of pUEQAS%. Measurement uncertainties of FT4, cortisol, DHEAS, FSH, testosterone, and folate did not meet the specification limits. Recently defined permissible expanded uncertainty promises new targets to compare estimated measurement uncertainty. Measurement uncertainty should be applied to the laboratory result interpretation within the scope of RCV and reference interval to obviate misdiagnosis. Furthermore, we suggest that laboratories should inform clinicians about the tests with high uncertainties to assist them making the right clinical diagnosis. Abbreviations CLSI: Clinical Laboratory Standards Institute; CV: coefficient of variation; CVA: analytic coefficient of variation; CVG: inter-individual coefficient of variation; CVI: intra-individual coefficient of variation; DHEAS: dehydroepiandrosterone sulfate; FSH: follicle-stimulating hormone; FT3: free triiodothyronine; FT4: free thyroxine; k: coverage factor; LH: luteinizing hormone; LRL: lower reference limit; MD: minimal difference; NPAAC: National Pathology Accreditation Advisory Council; PRDEQA%: permissible relative deviation in the external quality assessment; pUEQAS%: permissible expanded uncertainty for external quality assessment; RCV: reference change value; RCV': uncertainty-adjusted reference change value; TSH: thyroid-stimulating hormone; Rw: within-laboratory reproducibility; RMSbias: root mean square of biases; u(Cref): the uncertainty of nominal values; u(bias): uncertainty component for bias; uc: combined standard uncertainty; TEa%: allowable total error; U: expanded uncertainty; Uone-sided%: one sided estimation of expanded measurement uncertainty using coverage factor “1.65”; URL: upper reference limit

Use of External Quality Control Material for HIV-1 RNA Testing To Assess the Comparability of Data Generated in Separate Laboratories and the Stability of HIV-1 RNA in Samples after Prolonged Storage

The National Institute of Allergy and Infectious Diseases (NIAID) AIDS Clinical Trials Group (ACTG) stores specimens from its clinical trials in a biorepository and permits the use of these specimens for nonprotocol exploratory studies, once the studies for the original protocol are concluded. We sought to assess the comparability of the data generated from real-time HIV-1 RNA testing during two clinical trials with the data generated from the retesting of different aliquots of the same samples after years of storage at -80°C. Overall, there was 92% agreement in the data generated for 1,570 paired samples (kappa statistic = 0.757; 95% confidence interval [CI], 0.716 to 0.797), where samples were tested in one laboratory using the microwell plate (MWP) version of the Roche HIV-1 Monitor test within 1 to 37 days of collection and retested in another laboratory using the Cobas version of the assay after a median of 6.7 years of storage (range, 5.7 to 8.6 years). Historical external quality control data submitted to the NIAID Virology Quality Assurance program (VQA) by client laboratories using the same two versions of the Monitor assay were used to differentiate between systematic differences in the assays to evaluate the stability of HIV-1 RNA in the stored samples. No significant loss of RNA was noted in samples containing either a low concentration (<50 copies/ml) or a high concentration (≥50 copies/ml) of HIV-1 RNA (P = 0.10 and P = 0.90, respectively) regardless of the time in storage. These data confirm the quality of the plasma samples in the ACTG biorepository following long-term storage.

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

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 (≥3) 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.

Uncertainty estimation to evaluate mass balances on a combustion system

Mass balances of ash and potassium for a fluidized bed combustor were performed incorporating measurement uncertainties. The total output mass of ash or a chemical element should be equal to the mass in the input fuel; however, this is not often achieved. A realistic estimation of recovery uncertainty can support the reliability of a mass balance. Estimation of uncertainty helps to establish a reliable evaluation of the recovery ratio of ash mass and elemental mass. This may clarify whether any apparent lack in closing the mass balance can be attributed to uncertainties. The evaluation of measurement uncertainty for different matrices, namely coal, biomass, sand and ashes from different streams was based on internal quality control data and external quality control data, namely analysis of samples from proficiency tests or use of a certified reference material. The evaluation of intermediate precision and trueness allowed the estimation of measurement uncertainty. Due to the different physic and chemical characteristics of the studied matrices, the uncertainty of precision was evaluated using R-charts of data obtained from the analysis of duplicates for the majority of samples. This allowed evaluating sample heterogeneity effects. The instrumental acceptance criterion was also considered and included in the combined uncertainty. The trueness was evaluated using data from several proficiency tests and from analysis of a certified reference material or sample spiking. Statistically significant bias was included.