Management of Cytomegalovirus, Epstein-Barr Virus, and HIV Viral Load Quality Control Data Using Unity Real Time.

Quality control (QC) rules (Westgard rules) are applied to viral load testing to identify runs that should be reviewed or repeated, but this requires balancing the patient safety benefits of error detection with the cost and inefficiency of false rejection. In this study, we identified the total allowable errors (TEa) from the literature and utilized a commercially available software program (Unity Real Time; Bio-Rad Laboratories) to manage QC data, assess assay performance, and provide QC decision support for both FDA-approved/cleared (Abbott cytomegalovirus [CMV] and HIV viral load) as well as laboratory-developed (Epstein-Barr virus [EBV] viral load) assays. Unity Real Time was used to calculate means, standard deviations (SDs), and coefficient of variation (CV; in percent) of negative, low-positive, and high-positive control data from 73 to 83 days of testing. Sigma values were calculated to measure the test performance relative to a TEa of 0.5 log10. The sigma value of 5.06 for EBV predicts ∼230 erroneous results per million individual patient tests (0.02% frequency), whereas sigma values of >6 for CMV (11.32) and HIV (7.66) indicate <4 erroneous results per million individual patient tests. The Unity Real Time QC Design module utilized these sigma values to recommend QC rules and provided objective evidence for loosening the laboratory's existing QC rules for run acceptability, potentially reducing false rejection rates by 10-fold for the assay with the most variation (EBV viral load). This study provides a framework for laboratories, with Unity Real Time as a tool, to evaluate assay performance relative to clinical decision points and establish optimal rules for routine monitoring of molecular viral load assay performance.

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.

The Hemolyzed Sample: To Analyse Or Not To Analyse.

Preanalytical errors constitute about 40-65% of laboratory errors, of which 60% are due to hemolysis. This leads to imprecise reporting and misinterpretation of the actual concentration of analytes. Hence the aim of this study was to estimate the extent of different degrees of interference by visible hemolysis. 25 hemolysed samples along with their fresh unhemolysed sample were studied. Hemolyzed serum was mixed with unhemolyzed serum in predefined serial ratios from 100%, 70%, 50%, 30% and 10% to achieve different grades of hemolysis. Each dilution was analysed for BUN, creatinine, uric acid, phosphorus, Na, K, total protein, amylase, lipase, LDH, tacrolimus and methotrexate. Percentage difference of each dilution of the hemolyzed sample as compared to the unhemolyzed sample was calculated and considered acceptable only if less than TEa. It was observed that Percentage difference of BUN, creatinine, amylase and lipase in all dilutions of hemolyzed samples were within TEa while phosphorus, Na, K, total protein and LDH were beyond the acceptance criteria. Hence It was concluded that it may be safe to analyse a hemolysed sample for BUN, creatinine, amylase, lipase, tacrolimus and methotrexate while uric acid may be estimated in a moderately hemolysed sample. Phosphorus, sodium, potassium, total protein and LDH must never be analyzed in any hemolysed sample.

Evaluation of a novel quantitative canine species-specific point-of-care assay for C-reactive protein.

BACKGROUND: Species-specific point-of-care tests (POCT) permit a rapid analysis of canine C-reactive protein (CRP), enabling veterinarians to include CRP in clinical decisions. Aim of the study was to evaluate a novel POCT for canine CRP (Point Strip™ Canine CRP Assay) run on a small in-house-analyzer (Point Reader™ V) using lithium heparin plasma and to compare assay performance to an already established canine CRP assay (Gentian Canine CRP Immunoassay) run on two different bench top analyzers serving as reference methods (ABX Pentra 400, AU 5800). Linearity was assessed by stepwise dilution of plasma samples with high CRP concentrations. Limit of quantification (LoQ) was determined by repeated measurements of samples with low CRP concentrations. Coefficient of variation (CV) at low (10-50 mg/l), moderate (50-100 mg/l), and high (100-200 mg/l) CRP concentrations was investigated as well as possible interferences. Method comparison study was performed using 45 samples of healthy and diseased dogs. Quality criteria were fulfilled if the total observed error (TEobs = 2CV% + bias%) was below the minimal total allowable error of 44.4% (TE min). Additionally, a reference range (n = 60 healthy dogs) was established. RESULTS: Linearity was present at CRP concentrations of 10-132 mg/l (≙ 361 mg/l CRP with reference method) with a LoQ set at 10 mg/l. At moderate to high CRP concentrations, intra- and inter-assay CVs were ≤ 8% and ≤ 11% respectively, while CVs ≤ 22% and ≤ 28% were present at low concentrations. No interferences were observed at concentrations of 4 g/l hemoglobin, 800 mg/l bilirubin and 8 g/l triglycerides. Method comparison study demonstrated an excellent correlation with both reference methods (r = 0.98 for ABX Pentra 400; 0.99 for AU 5800), though revealing a proportional bias of 19.7% (ABX Pentra 400) and 10.7% (AU 5800) respectively. TEobs was 26.7-31.9% and 16.7-21.9% and thus < TEmin. Healthy dogs presented with CRP values ≤11.9 mg/l. CONCLUSIONS: The POCT precisely detects canine CRP at clinically relevant moderate and high CRP concentrations. The assay correlates well with both reference methods. Due to the bias, however, follow-up examinations should be performed with the same assay and analyzer.

Evaluation of a species-specific C-reactive protein assay for the dog on the ABX Pentra 400 clinical chemistry analyzer.

BACKGROUND: A canine-specific immunoturbidimetric CRP assay, Gentian Canine CRP Immunoassay) with species-specific controls and calibrators was introduced and recently evaluated on the clinical chemistry analyzer Abbott Architect c4000 as well as on the Olympus AU600. Aims of our study were 1) to independently evaluate the canine-specific CRP assay on the ABX Pentra 400 clinical chemistry analyzer in comparison to the previously validated human-based immunoturbidimetric assay (Randox Canine CRP assay) and 2) to assess the impact of different sample types (serum versus heparinized plasma) on the results. Imprecision, accuracy, interference and the prozone effect were determined using samples from healthy and diseased dogs (n = 278). The Randox Canine CRP assay calibrated with canine specific control calibration material served as a reference method. Additionally, the impact of the sample type (serum and lithium heparin) was evaluated based on samples of healthy and diseased dogs (n = 49) in a second part of the study. RESULTS: Linearity was present for CRP concentrations ranging from 4 to 281 mg/l. For clinically relevant CRP concentrations of 7-281 mg/l, recovery ranged between 90 and 105% and intra- and inter-assay CVs ranged between 0.68% - 12.12% and 0.88% - 7.84%, respectively. CV was thus lower than 12.16%, i.e. the desired CV% based on biological variation. Interference was not present up to a concentration of 5 g/l hemoglobin, 800 mg/l bilirubin and 10 g/l triglycerides. No prozone effect occurred up to 676 mg/l CRP. Method comparison study revealed a Spearman's rank correlation coefficient of rs = 0.98 and a mean constant bias of 5.2%. The sample type had a significant (P = 0.008) but clinically not relevant impact on the results (median CRP of 30.9 mg/l in lithium heparin plasma versus 31.4 mg/l in serum). CONCLUSIONS: The species-specific Gentian Canine CRP Immunoassay reliably detects canine CRP on the ABX Pentra 400 clinical chemistry analyzer whereby both serum and heparin plasma can be used. The quality criteria reached on the Abbott Architect c4000 and Olympus AU600 could be met.

Comparison of Methods Study between a Photonic Crystal Biosensor and Certified ELISA to Measure Biomarkers of Iron Deficiency in Chronic Kidney Disease Patients.

The total analytical error of a photonic crystal (PC) biosensor in the determination of ferritin and soluble transferrin receptor (sTfR) as biomarkers of iron deficiency anemia in chronic kidney disease (CKD) patients was evaluated against certified ELISAs. Antigens were extracted from sera of CKD patients using functionalized iron-oxide nanoparticles (fAb-IONs) followed by magnetic separation. Immuno-complexes were recognized by complementary detection Ab affixed to the PC biosensor surface, and their signals were followed using the BIND instrument. Quantification was conducted against actual protein standards. Total calculated error (TEcalc) was estimated based on systematic (SE) and random error (RE) and compared against total allowed error (TEa) based on established quality specifications. Both detection platforms showed adequate linearity, specificity, and sensitivity for biomarkers. Means, SD, and CV were similar between biomarkers for both detection platforms. Compared to ELISA, inherent imprecision was higher on the PC biosensor for ferritin, but not for sTfR. High SE or RE in the PC biosensor when measuring either biomarker resulted in TEcalc higher than the TEa. This did not influence the diagnostic ability of the PC biosensor to discriminate CKD patients with low iron stores. The performance of the PC biosensor is similar to certified ELISAs; however, optimization is required to reduce TEcalc.

The role of the uncertainty of measurement of serum creatinine concentrations in the diagnosis of acute kidney injury.

Uncertainty of measurement is the numeric expression of the errors associated with all measurements taken in clinical laboratories. Serum creatinine concentration is the most common diagnostic marker for acute kidney injury. The goal of this study was to determine the effect of the uncertainty of measurement of serum creatinine concentrations on the diagnosis of acute kidney injury. We calculated the uncertainty of measurement of serum creatinine according to the Nordtest Guide. Retrospectively, we identified 289 patients who were evaluated for acute kidney injury. Of the total patient pool, 233 were diagnosed with acute kidney injury using the AKIN classification scheme and then were compared using statistical analysis. We determined nine probabilities of the uncertainty of measurement of serum creatinine concentrations. There was a statistically significant difference in the number of patients diagnosed with acute kidney injury when uncertainty of measurement was taken into consideration (first probability compared to the fifth p = 0.023 and first probability compared to the ninth p = 0.012). We found that the uncertainty of measurement for serum creatinine concentrations was an important factor for correctly diagnosing acute kidney injury. In addition, based on the AKIN classification scheme, minimizing the total allowable error levels for serum creatinine concentrations is necessary for the accurate diagnosis of acute kidney injury by clinicians.

The importance of quality control validation and relationships with total error quality goals and bias in the interpretation of laboratory results.

The objective of a quality system is to provide accurate and reliable results for clinical decision-making. One part of this is Quality Control (QC) validation. QC validation is not routinely applied in veterinary laboratories. This leads to the inappropriate usage of random QC rules without knowing the Probability of error detection (Ped ) and Probability of false rejection (Pfr ) of a method. In this paper, we will discuss why QC validation is important, when it should be undertaken, why QC validation is done, and why it is not commonly done. We will present the role of total analytical error (TEa) in the QC validation process and the challenges when a consensus TEa has not been published. Finally, we will also discuss the possibilities of 'gray zone' determinations and mention the effects of bias on the quality of results. Reasons for the low prevalence of performing QC validation may include (a) lack of familiarity with the concept, (b) lack of time and resources needed to conduct QC validation, and (c) lack of TEa goal for some measurands. If no TEa is available, the user may elect to use a 'reverse approach' to QC validation. This uses the CV and bias generated from the evaluation of QC measurements, specifying Ped , Pfr , and N (number of QC measurements/run). This identifies the lowest total error that can be controlled under these defined conditions, thus enabling the laboratory to have an estimate of the 'gray zone' associated with results generated with a specific assay.

Are tube fill volumes below 90% a rejection criterion for all coagulation tests?

BACKGROUND: Rejected samples lead to prolonged turnaround time and delayed diagnosis and treatment of patients. This study was conducted to determine minimum acceptable sample volume in Sarstedt brand coagulation tubes to reduce high sample rejection rate. METHODS: Blood samples were drawn from 20 participants (10 healthy volunteers and 10 patients receiving oral anticoagulant) into coagulation tubes. Six samples were taken from each participant, with tube fill volumes of 100%, 90%, 80%, 70%, 60%, and 50%. Prothrombin time (PT), active partial thromboplastin time (aPTT), and fibrinogen tests were analyzed. RESULTS: According to quality performance specifications, the tube fill volume must be at least 70% for PT and aPTT and 50% for fibrinogen. There was no statistical difference in samples from healthy volunteers for PT, aPTT, and fibrinogen tests when the minimum tube fill volume was at least 80%, 90%, and 50%, respectively. These percentages were 50%, 70%, and 60%, respectively, in patients receiving oral anticoagulant. CONCLUSIONS: Sarstedt tubes meet quality standard specifications at a 70% fill rate for PT and aPTT and a 50% fill rate for fibrinogen. Comprehensive studies with larger populations are needed to accept these values as sample acceptance criteria for the laboratory.

Reducing Specimen Rejection Rates Using Concentration-Dependent Hemolysis Rejection Thresholds.

BACKGROUND: Using middleware solutions, it is possible to implement concentration-dependent analyte-specific hemolysis rejection limits. This makes day-to-day reporting of clinical specimens more efficient and potentially lowers sample rejection rates compared to a "one-size-fits-all" approach (i.e., solely based on a single cutoff provided in the package insert). METHODS: Hemolysis interference studies were performed at multiple analyte concentrations for three frequently ordered tests. For each assay, concentration-dependent hemolysis rejection limits were designed based on the total allowable error (TAE) for the analyte as well as the clinical significance of such incurred inaccuracy at the respective concentrations. In general, the following rationale was used: if the interference exceeds 10% (or package insert cutoffs), a comment is placed on the result. If the interference exceeds the TAE, the result will not be reported. Reduction in specimen rejection rates were estimated by comparing the incurred specimen rejection rates when package inserts' vs concentration-dependent hemolysis interference limits were applied to a data set in our institute during a three-month period. RESULTS: Concentration-dependent analyte-specific hemolysis rejection thresholds were designed for three commonly ordered assays that are especially susceptible to hemolysis interference. It is estimated that these novel thresholds for aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and direct bilirubin (DBIL) reduced specimen rejection rates from 9.3% to 1.3%, 31.4% to 4.8%, and 19.9% to 7.1%, respectively. CONCLUSIONS: Concentration-dependent analyte-specific hemolysis rejection thresholds for three commonly ordered assays can reduce rejection rates without significantly compromising the quality of test results.

Quality Assurance for Veterinary In-Clinic Laboratories.

Quality assurance and the implementation of a quality management system are as important for veterinary in-clinic laboratories as for reference laboratories. Elements of a quality management system include the formulation of a quality plan, establishment of quality goals, a health and safety policy, trained personnel, appropriate and well-maintained facilities and equipment, standard operating procedures, and participation in external quality assurance programs. Quality assurance principles should be applied to preanaltyic, analytic, and postanalytic phases of the in-clinic laboratory cycle to ensure that results are accurate and reliable and are released in a timely manner.

Total observed error, total allowable error, and QC rules for canine serum and urine cortisol achievable with the Immulite 2000 Xpi cortisol immunoassay.

Determining a simple quality control (QC) rule for daily performance monitoring depends on the desired total allowable error (TEa) for the measurand. When no consensus TEa exists, the classical approach of QC rule validation cannot be used. Using the results of previous canine serum and urine cortisol validation studies on the Immulite 2000 Xpi, we applied a reverse engineering approach to QC rule determination, arbitrarily imposing sigma = 5, and determining the resulting TEa for the QC material (QCM; TEaQCM) and the resulting probability of error detection (Ped) for each QC rule. For the simple QC rule 12.5S with Ped = 0.96 and probability of false rejection (Pfr) = 0.03, the associated TEaQCM were 20% and 35% for serum and 28% and 24% for urine QCM1 and QCM2. If these levels of TEaQCM are acceptable for interpretation of patient sample results, then users can internally validate the 12.5S QC rule, provided that their QCM CVs and biases are similar to ours. Otherwise, more stringent QC rules can be validated by using a lower sigma to lower the TEaQCM. With spiked samples (relevant cortisol concentrations in the veterinary patient matrix) at 38.6 and 552 nmol/L of cortisol, TEaQCM at sigma = 5 were much higher (54% and 40% for serum; 90.3% and 42.8% for urine). Spiked samples generate TEa that is probably too high to be suitable for daily QC monitoring; however, it is crucial to verify spiked sample observed total error (TEo; 26% and 18% for serum, 60% and 30% for urine) < TEaQCM, and to use spiked sample TEo for patient result interpretation. In the absence of consensus TEa for cortisol in dogs, we suggest the use of a 12.5S rule, provided that users accept the associated level of TEaQCM also as clinical TEa for results interpretation.

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

INTRODUCTION: The 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 METHODS: The 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. RESULTS: Considering 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. CONCLUSIONS: The 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 the scil vCell 5, a novel laser- and impedance-based point-of-care hematology analyzer, for use in dogs and cats.

A novel laser- and impedance-based point-of-care hematology analyzer (POCA), the vCell 5 (scil Animal Care), providing a complete blood count with 5-part leukocyte differential count has recently been introduced to veterinary laboratories. We evaluated the analyzer for use in dogs and cats including method comparison and assessment of linearity, carryover, and precision. Fresh blood samples from 192 healthy and diseased dogs and 159 cats were analyzed, and results were compared to reference methods (i.e., microhematocrit [PCV], Advia 2120 hematology analyzer). Total observed error (TEo) was calculated from CV, obtained at 3 concentrations, and bias%, and compared to total allowable error (TEa). For both species, excellent correlation (rs = 0.93-0.99) was seen between methods for WBC and RBC, hematocrit, hemoglobin, and platelet counts (PLT), except for feline PLT (rs = 0.79). Quality requirements (TEo < TEa) were fulfilled for WBC (TEo = 8.6-11.1%; TEa = 20%) and RBC (TEo = 3.5-7%; TEa = 10%), hematocrit (TEo = 5.7-9.4%; TEa = 10%), PCV (cat TEo = 7.8%; TEa = 10%), mean corpuscular volume (cat TEo = 5.1%; TEa = 7%), and PLT (TEo = 13.1-24.1%; TEa = 25%). Excellent linearity was demonstrated for WBC, RBC, and PLT, and hemoglobin. CVs of <2% for WBC, RBC, hematocrit, hemoglobin, and of <5% (dog) and 8% (cat) for PLT were demonstrated for values within the RI. Except for calculated variables and well-known species-specific deviations in feline PLT, scil POCA results were correlated favorably with reference method results and complied with quality requirements for cats and dogs.

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.

Analytical performance and method comparison of a quantitative point-of-care immunoassay for measurement of bile acids in cats and dogs.

Point-of-care analyzers (POCAs) for quantitative assessment of bile acids (BAs) are scarce in veterinary medicine. We evaluated the Fuji Dri-Chem Immuno AU10V analyzer and v-BA test kit (Fujifilm) for detection of feline and canine total serum BA concentration. Results were compared with a 5th-generation assay as reference method and a 3rd-generation assay, both run on a bench-top analyzer. Analytical performance was assessed at 3 different concentration ranges, and with interferences. For method comparison, samples of 60 healthy and diseased cats and 64 dogs were included. Linearity was demonstrated for a BA concentration up to 130 µmol/L in cats (r = 0.99) and 110 µmol/L in dogs (r = 0.99). The analyzer showed high precision near the lower limit of quantification of 2 µmol/L reported by the manufacturer. Intra- and inter-assay coefficients of variation were < 5% for both species and all concentrations. Interferences were observed for bilirubin (800 mg/L) and lipid (4 g/L). There was excellent correlation with the reference method for feline (rs = 0.98) and canine samples (rs = 0.97), with proportional biases of 6.7% and -1.3%, respectively. However, a large bias (44.1%) was noted when the POCA was compared to the 3rd-generation assay. Total observed error was less than total allowable error at the 3 concentrations. The POCA reliably detected feline and canine BA in clinically relevant concentrations.

Evaluation of Analytical Performance of Variant II Turbo HbA1c Analyzer According to Sigma Metrics.

BACKGROUND: Hemoglobin A1c, (HbA1c) which is the major constituent of glycated hemoglobin, has been used in the follow-up of retrospective glycemia for years and in the diagnosis of diabetes mellitus nowadays. Since the analytical performance of HbA1c should be high likewise all laboratory tests, various quality control measures are used. Sigma metrics is one of these measures and it is the combination of bias, precision and total allowable error that ensures a general evaluation of analytical quality. The aim of our study was to evaluate the analytical performance of Bio-Rad's Variant Turbo II HbA1c analyzer according to sigma metrics. METHODS: Sigma levels were calculated using the data obtained from two levels of internal and 12 external quality control materials (Bio-Rad) of Variant II Turbo HbA1c analyzer according to σ = (TEa% - Bias%) / CV% formula. RESULTS: The mean sigma levels for low and high quality control materials were found to be 3.0 and 4.1, respectively. CONCLUSIONS: The annual mean analytical performance of Variant II Turbo HbA1c analyzer was found to be acceptable according to sigma metrics. In order to be sure of the difference in HbA1c results indicating the success or failure in treatment but not arise from analytical variation, it is thought that more stringent quality control measures should be applied to reach higher sigma levels.

Evaluating the analytical quality control of urinary albumin measurements using sigma metrics.

BACKGROUND: There is no worldwide recognized reference system and standard for urinary albumin measurement until now, so the analytical quality from different laboratories has always varied. In this study, we aimed to evaluate the analytical performance of a urinary albumin assay system using Sigma-metric, and thereby choose a suitable control rule to guarantee the analytical quality of the assays. METHOD: Two levels of diluted reference material (ERM-DA47OK/IFCC) were used to calculate the biases, the coefficient of variation (CV) were calculated from six months of internal quality control measurements at two levels, and the external quality assessment standard of China for urinary albumin (30%) was used as the total allowable error(TEa). RESULTS: The Sigma values for quality control levels 1 and 2 were 4.28 and 6.14, leading to recommended Westgard rules of 13s/22s/R4s/41s (N = 2, R = 2) and 13s(N = 2, R = 1), respectively. Westgard rule 13s/22s/R4s/41s(N = 2, R = 2) was selected for the quality control of the urinary albumin measurements, and with it, the power function graph showed a high efficacy for determining the detection errors with a probability of false rejection of 1.004% and a probability of error detection of 98.80%. CONCLUSION: With a TEa of 30% recommended by the external quality assessment standard of China, Westgard rule 13s/22s/R4s/41s(N = 2, R = 2) with a high efficacy for determining the detection error is recommended for the quality control of urinary albumin measurements.

Analytical evaluation of the Radiometer AQT90 FLEX ßhCG assay.

OBJECTIVES: Many hospitals cannot afford an hCG assay on a central lab analyzer and turn to point of care testing (POCT) solutions. The Radiometer AQT90 FLEX is a small benchtop immunoareement between the AQT90 and comparator methods for samples with hCG ssay analyzer for use in the laboratory or at the patient bedside. This study evaluated the analytical performance of the AQT90's ßhCG assay. METHODS: Precision was assessed using whole blood patient samples and two levels of quality control. Linearity was assessed by dilution of a high hCG plasma sample. Carryover and hook effect were assessed using high and low hCG samples. Method comparisons were done against Abbott i-STAT Total ßhCG, Beckman Coulter Total ßhCG (5th IS), and Roche hCG+ß. Sample concentrations ranged from<2 IU/L to 4,973 IU/L. RESULTS: Repeatability and within-laboratory precision passed most manufacturer's claims and allowable error criteria. Linearity was validated from<2 IU/L to 4,741 IU/L. Hook effect was not observed up to 2,446,448 IU/L. Carryover was<4.0 ppm. A linear relationship was observed with i-STAT, Beckman and Roche methods. At>20 IU/L, biases were apparent against all three comparator assays (i-STAT: +20%, Roche: +30%, Beckman: +5 to 15%). At ≤20 IU/L, the acceptability of agreement varied according to TAE specifications. Concordance between AQT90 and comparator assays using 5 IU/L as the medical decision level ranged from 69% to 81%. CONCLUSIONS: Overall, the AQT90 hCG assay performed well and would be suitable for smaller suburban or rural hospitals. Some limitations have been noted and should be kept in mind during clinical testing.

Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma.

INTRODUCTION: In recent years, Six Sigma metrics has became the hotspot in all trades and professions, which contributes a general procedure to explain the performance on sigma scale. Nowadays, many large companies, such as General Healthcare, Siemens, etc., have applied Six Sigma to clinical medicine and achieved satisfactory results. In this paper, we aim to evaluate the process performance of our laboratory by using Sigma metrics, thereby choosing the correct analytical quality control approach for each parameter. MATERIALS AND METHODS: This study was conducted in the clinical chemistry laboratory of Shandong Provincial Hospital. The five-months data of internal quality control were harvested for the parameters: amylase (AMY), lactate dehydrogenase (LD), potassium, total bilirubin (TBIL), triglyceride, aspartate aminotransferase (AST), uric acid, high density lipoprotein-cholesterol (HDL-C), alanine aminotransferase (ALT), urea, sodium, chlorine, magnesium, alkaline phosphatase (ALP), creatinine (CRE), total protein, creatine kinase (CK), total cholesterol, glucose (GLU), albumin (ALB). Sigma metrics were calculated using total allowable error, precision and percent bias for the above-mentioned parameters. RESULTS: Sigma values of urea and sodium were below 3. Sigma values of total protein, CK, total cholesterol, GLU and ALB were in the range of 3 to 6. Sigma values of AMY, uric acid, HDL-C, TBIL, ALT, triglyceride, AST, ALP and CRE were more than 6. CONCLUSION: Amylase was the best performer with a Sigma metrics value of 19.93, while sodium had the least average sigma values of 2.23. Actions should be taken to improve method performance for these parameters with sigma below 3.