A-401 Performance Evaluation of POCT Equipment for Blood Gas Measurement and Metabolic Parameters

Beyond the basics: Sigma scores in laboratory medicine with variable total allowable errors (TEa)

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.

Objective: To explore the consistency and relevance of the results of the bedside rapid blood gas analyzer GEM premier 3000, the Vitros5.1+5600 biochemical immunoassay analyzer and the SYSMEX XN-9000 automatic blood cell analyzer in the central laboratory detecting serum potassium (K+), serumsodium (Na+), blood glucose (Glu), hemoglobin (Hb) and red blood cell volume (Hct). And to provide a reference for the accurate interpretation of the bedside blood gas analysis report. Method: Usually, ICU patients will be taken arterial blood gas, biochemical and blood samples through the arterial indwelling needle; at the same time patients’ potassium, serum sodium, blood glucose, hemoglobin and red blood cell volume will be detected. This study implemented paired t-test and correlation regression analysis on each group of data, and used the analysis quality requirements (allowable total error) of CLIA’88 proficiency testing program as the criteria for clinical acceptance. Results: The paired t-test showed that the serum potassium, serum sodium and blood glucose detected by GEM premier 3000 and Vitros5.1+5600 were significantly different; and the hemoglobin and red blood cell volume detected by GEM premier 3000 and SYSMEX XN-9000 were significantly different (P +, Na+, Glu, Hb and Hct detected by the two sets of instruments is less than the allowable error of CLIA’88, and the SE of blood Na+ and Hb is less than half of the allowable error of CLIA’88. Conclusion: The test results of GEM premier 3000, the central laboratory Vitros5.1+5600 and SYSMEX XN-9000 have good correlation, but the consistency is not good. The test results of GEM premier 3000 cannot replace the central laboratory.

BackgroundLaboratories use quality control processes to monitor and evaluate analytical performance in terms of precision and bias. Sigma metrics provide an objective assessment of laboratory quality using the total allowable error as an additional parameter.ObjectiveThis study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines.MethodsA retrospective analysis was performed on 19 general chemistry analytes at Charlotte Maxeke Johannesburg Academic Hospital in South Africa between January 2017 and December 2017. Sigma metrics were calculated on two identical analysers, using internal quality control data and total allowable error guidelines from the Ricos biological variation database and three alternative sources (the Royal College of Pathologists of Australasia, the Clinical Laboratory Improvements Amendment, and the European Federation of Clinical Chemistry and Laboratory Medicine).ResultsThe sigma performance was similar on both analysers but varied based on the guideline used, with the Clinical Laboratory Improvements Amendment guidelines resulting in the best sigma metrics (53% of analytes on one analyser and 46% on the other had acceptable sigma metrics) and the Royal College of Pathologists of Australia guidelines being the most stringent (21% and 23%). Sodium and chloride performed poorly across all guidelines (sigma < 3). There were also month-to-month variations that may result in acceptable sigma despite poor performance during certain months.ConclusionThe sigma varies greatly depending on the total allowable error, but could be a valuable tool to save time and decrease costs in high-volume laboratories. Sigma metrics calculations need to be standardised.

Impact of blood collection devices and mode of transportation on peripheral venous blood gas parameters

To maintain the consistency of laboratory test results, between-reagent lot variation should be verified before using new reagent lots in clinical laboratory. Although the Clinical and Laboratory Standards Institute (CLSI) document EP26-A deals with this issue, evaluation of reagent lot-to-lot difference is challenging in reality. We aim to investigate a practical way for determining between-reagent lot variation using real-world data in clinical chemistry. The CLSI EP26-A protocol was applied to 83 chemistry tests in three clinical labs. Three criteria were used to define the critical difference (CD) of each test as follows: reference change value and total allowable error, which are based on biological variation, and acceptable limits by external quality assurance agencies. The sample size and rejection limits that could detect CD between-reagent lots were determined. For more than half of chemistry tests, reagent lot-to-lot differences could be evaluated using only one patient sample per decision level. In many cases, the rejection limit that could detect reagent lot-to-lot difference with≥90% probability was 0.6 times CD. However, the sample size and rejection limits vary depending on how the CD is defined. In some cases, impractical sample size or rejection limits were obtained. In some cases, information on sample size and rejection limit that met intended statistical power was not found in EP26-A. The CLSI EP26-A did not provide all necessary answers. Alternative practical approaches are suggested when CLSI EP26-A does not provide guidance.

<h3>ABSTRACT</h3> Choosing quality control (QC) rules for monitoring quantitative methods is compulsory, often frustrating, and not easy. As part of the protocol, there are many possible QC statistical “rules” (eg, rejecting a single value outside 2 SDs) to be selected. Each analyte should use the rule or rules that have the fewest accepted wrong results (for patients and controls). Selecting the best primary QC rule ensures the development of a simple, rapid system that calculates the rule best primary for each level used for an analyte. The algorithm uses 3 readily available data points for each QC level—the laboratory’s mean, SD, and the true (survey) mean. With these data and the total error allowable (TEa), the program calculates the values for the total error (TE) and Tea − TE. This algorithm generates the primary QC rule (eg, 12 SD, 2.5 SD, 13 SD rule). The rules, −12.5 or 13 SDs (and ones in between if wanted), will reduce wrong results without accepting false results. Additionally, QC rules such as 41 SD and 10 SD are no longer necessary. The 22-SD rule need not be rejected, but the user need only be aware. Using the algorithm by hand or laptop is easy and removes the guesswork of choosing the primary QC rules.

The aim of this study was to redefine the icterus index cutoff for the Roche Jaffé creatinine method using both conjugated and unconjugated bilirubin on 3 Roche cobas modules (c311, c501, and c701/c702) at laboratories across our hospital network. Interference was evaluated by adding conjugated bilirubin (as bilirubin conjugate, ditaurate) and unconjugated bilirubin to pooled remnant plasma. The effects of conjugated and unconjugated bilirubin were tested separately to assess the contribution of each species. The magnitude of interference was calculated as both absolute and percentage error with total allowable error limits set at 0.1 mg/dL (8 µmol/L or 8%). Analysis of interference data across the 3 Roche modules did not show bias exceeding our total allowable error limits for plasma creatinine up to a conjugated bilirubin icterus index of 16.2 (approximately 16.2 mg/dL or 277 μmol/L) or an unconjugated bilirubin icterus index of 18.5 (approximately 18.5 mg/dL or 316 μmol/L), the highest concentrations tested. Our results demonstrate that the Roche Jaffé method exhibits acceptable performance in the presence of icterus at icterus indexes above the manufacturer's current recommendations of 5 (approximately 5 mg/dL or 86 μmol/L) and 10 (approximately 10 mg/dL or 171 μmol/L) for conjugated and unconjugated bilirubin, respectively. We have updated the icterus index in our hospital system to 16 for conjugated bilirubin and 18 for unconjugated bilirubin.

Sigma methodology has become a valuable tool in clinical laboratories for assessing analytical performance and optimizing QC. However, the choice of total allowable error (TEa) sources significantly influences sigma calculation outcomes and can lead to inconsistent quality classifications. In this study, we aimed to evaluate how different TEa guidelines impact sigma metrics and internal quality control (IQC) strategies in order to highlight the need for harmonization in TEa selection. A prospective observational study was conducted over 3 months (April-June 2025) in the Clinical Biochemistry Laboratory at Bechir Hamza Children's Hospital. Sigma metrics were calculated for 14 routine analytes at 2 QC levels (level 1 and level 2) using internal and external QC data. Three TEa sources were used: CLIA 2025 (regulatory-based), Randox International Quality Assessment Scheme (RIQAS; peer group-based), and European Federation of Laboratory Medicine (EFLM; biological variation-based). IQC procedures were adapted based on Westgard sigma rules and flowcharts. Substantial variability in sigma metrics was observed across the 3 TEa guidelines. The same analyte could be classified as "world-class" under EFLM but "unacceptable" under RIQAS. Electrolytes (sodium, potassium, chloride) consistently exhibited poor performance across all guidelines. Visual tools, including radar and sigma charts, confirmed discrepancies. These differences significantly influenced the selection and complexity of IQC procedures. The choice of TEa guideline exerts a critical influence on sigma metrics and subsequent IQC planning. Current inconsistencies highlight the urgent need for standardized TEa criteria that are both clinically meaningful and practically achievable. Harmonization would improve comparability, optimize laboratory resources, and support evidence-based quality management in clinical laboratories.

In Reply.— With regard to Dr Tetrault's letter, the ±14.2% acceptable range that we used in our study is the correct, total allowable error for a single cholesterol measurement. We refer interested readers to the February 1990 Laboratory Standardization Panel report, which outlines these guidelines for cholesterol measurement. 1 Also, 100% of the cholesterol measurements need to fall within the ± 14.2% range. Dr Tetrault's interpretation of the range for total allowable error is incorrect. The laboratory cholesterol values that we used as a measure of the study participants' true cholesterol levels were adjusted to control for a 5% positive bias found in the measurement method used in our Centers for Disease Control reference laboratory. The precision estimate for the reference method was small (1.3%). We did not assume that our laboratory method was completely without error. Postural and temporal effects can be factors in blood measurements and are rarely

Introduction: With advance in technology, many instruments with wide variety of principle or technologies are available in market and in laboratory to conduct sample processing in time bound period. To fulfill the laboratory quality goal, it is essential that result matching be obtained in all used biochemical analysers for examinations. Documented evidence of method/instrument comparison can be used in future for quality improvement purposes. Aim: To assess and compare equivalence and harmonisation in glucose results produce by ERBA XL-640 vs ERBA XL-1000 in clinical biochemistry laboratory. Materials and Methods: A comparative study was carried out by results comparison of glucose measurements obtained in two automated systems ERBA XL-640 and ERBA XL-1000 in Clinical Biochemistry laboratory at Surat Municipal Institute of Medical Education and Research between June 2021 to October 2021, based on protocol EP09-3A of result harmonisation and review article-method comparison. The results value were compared according to the total allowable error in Clinical Laboratory Improvement Amendments of 1988 (CLIA’88). Results were analysed by visual and quantitative analysis method Regression analysis and Bland-Altman plot, Method evaluation chart (Normalised MEDx chart). Results: Total of 48 samples were run within the batches and between the batches and quantitative analysis of difference was done. It is acceptable at medical decision level 40 mg/dL, 120 mg/dL and 180 mg/dL with comparison to total allowable error. Conclusion: The present study concluded that the comparison of ERBA XL-640 and ERBA XL-1000 for glucose examination by end point method {Glucose Oxidase-Peroxidase (GOD-POD) method} is acceptable and can be used interchangeably without repeat at major clinical decision levels.

Why Analytical Performance Specifications are Important and How They Can Be Established

IntroductionMeasurements of blood ethanol concentrations must be accurate and reliable. The most important factors affecting blood ethanol stability are temperature and storage time. In this study, we aimed to compare ethanol stability in plasma samples at -20 °C for the different storage periods.Materials and methodsBlood samples were collected from intoxicated drivers (N = 80) and initial plasma ethanol concentrations were measured immediately. Plasma samples were then stored at -20 °C and re-assessed after 2, 3, 4, or 5 months of storage. Differences between the initial and stored ethanol concentrations in each group (N = 20) were analyzed using Wilcoxon matched-pairs test. The deviation from the initial concentration was calculated and compared with Clinical Laboratory Improvement Amendments (CLIA’88) Proficiency Testing Limits. Relationships between the initial concentrations and deviations from initial concentrations were analyzed by Spearman’s correlation analysis. For all statistical tests, differences with P values of less than 0.05 were considered statistically significant.ResultsStatistically significant differences were observed between the initial and poststorage ethanol concentrations in the overall sample group (P < 0.001). However, for the individual storage duration groups, analytically significant decreases were observed only for samples stored for 5 months, deviations from the initial concentrations exceeded the allowable total error (TEa). Ethanol decreases in the other groups did not exceed the TEa.ConclusionAccording to our results, plasma ethanol samples can be kept at -20 °C for up to 3-4 months until re-analysis. However, each laboratory should also establish its own work-flow rules and criterion for reliable ethanol measurement in forensic cases.

Age-specific reference intervals (RIs) have been developed for biochemistry analytes in children. However, the ability to interpret results from multiple laboratories for 1 individual is limited. This study reports a head-to-head comparison of reference values and age-specific RIs for 30 biochemistry analytes for children across 5 analyzer types. Blood was collected from healthy newborns and children 30 days to <18 years of age. Serum aliquots from the same individual were analyzed on 5 analyzer types. Differences in the mean reference values of the analytes by the analyzer types were investigated using mixed-effect regression analysis and by comparing maximum variation between analyzers with analyte-specific allowable total error reported in the Westgard QC database. Quantile regression was used to estimate age-specific RIs using power variables in age selected by fractional polynomial regression for the mean, with modification by sex when appropriate. The variations of age-specific mean reference values between analyzer types were within allowable total error (Westgard QC) for most analytes, and common age-specific reference limits were reported as functions of age and/or sex. Analyzer-specific reference limits for all analytes on 5 analyzer types are also reported as functions of age and/or sex. This study provides quantitative and qualitative measures of the extent to which results for individual children can or cannot be compared across analyzer types, and the feasibility of RI harmonization. The reported equations enable incorporation of age-specific RIs into laboratory information systems for improving evidence-based clinical decisions in children.

Background A laboratory procedure comprises a measurement procedure plus control procedure that validates the measurement. Numerous quality specifications for the measurement procedure are often set in terms of total allowable error (Tea); ie, that total measurement error (TEm) be less than or equal to Tea. Recommendations are usually based on the ratio of stable random measurement error Smeas) to Tea (sigma score), and implicitely assume that Smeas is stable. A method of evaluating the efficacy of QC procedures is to calculate the undetected lost medical utility (ULMU) function for a control procedure. ULMU functions are calculated from statistical power functions as: ULMU = (lost medical utility due to measurement error) X (1 – statistical power). This is an expected value of lost medical value not detected by QC. ULMU functions always have the shape of an inverted U: ie, at some level of TEm it reaches a maximum value. This maximum value is a way of specifying the medical efficacy of the QC procedure. Methods I have developed a QC simulation programnusing Microsoft Excel and its built-in programming language (VBA). Simulations were run on a Dell XP13 laptop computer. The simulator produces Guassian-distributed random numbers that may be scaled to simulate random measurement error (RE). It introduces analytical bias (SE), with both RE and SE ((and hence TEm) specified as a proportion of Tea. It allows definition of various QC rules, also with rejection parameters defined as a proportion of Tea, generating a 2-dimensions SPF that covers a user-specified range of SE and RE. It calculates an ULMU function based on the probability that TEm exceeds Tea, ie the probability of undetected non-conformity to Tea (UNC):UNC = (Probability of non-confomity) X (1-probabilty of rejection, Prej) A number of rules and procedures may be specified, including 1 point&amp;gt; limit, 2 points&amp;gt;limit, multimean, and root mean square deviation from the target value; rule violation may reject the run or repeat 1 or 2 controls. Rule parameters can be adjusted to achieve a desired maximum UNC. Loosening cotrol limite raises the entire UNC curve, while tightening the limits lowers the curve. At each level of maximum UNC, the simulator will also calculated the expected amount of rejection (Pfr) at stable operatoing conditions of SE and RE. Results The table below indicates the QC rule rejection limit for a maximum UNC of ∼10%, as well as the stable Smeas needed to achieve a Pfr of 1% and 0.1%, based on simulations of 10,000 or 30,000 runs. Conclusion Calculation oof the maximum UNC is a method of setting QC rules and procedures to assure a defined level of medically relevant maximum error, in essence a way of capping the probability of type II error. Use of more complex QC rules can lower the Pfr without sacrificing error detection. Rule parameters can be adjusted to yield any desired undetected non-conformity to error goals, but must be weighe against the cost in false rejection of stable operation.