No abstract available.
Chemistry, Clinical ; Clinical Chemistry Tests ; Methods

In 2016, the clinical chemistry proficiency-testing program consisted of 21 programs, including the general chemistry program of the Korean Association of External Quality Assessment Service. The general chemistry program consisted of 28 test items and was conducted using two level control materials four times per year. Based on the information and results for each test item entered by each institution, statistical analysis data according to test method, instrument, and reagent were reported. The report comprised a general statistics report showing the characteristics of all participating institutions and a separate institutional report showing the evaluation data of individual institutions. The statistics included the number of participating institutions and the mean, standard deviation, coefficient of variation, median, minimum, and maximum values for each group. Each report was composed of a table, histogram, and Levey-Jennings chart showing the statistics for each test item. The results of each institution and the statistics for each classification are presented in the table showing the statistics, and a standard deviation index is presented together with a method classification and a classification by reagent companies. A total of 14 items, including albumin, were evaluated by more than 1,000 institutions. There was no significant difference in the distribution of the measurement methods compared with those used in the previous year. The coefficient of variation showed a tendency to increase as the concentration of the level control material decreased and as the number of participating institutions decreased for each test item. Most of them showed a coefficient of variation within 10%. These statistical data will be useful when interpreting the survey results from the institutions and selecting a test method.
Chemistry ; Chemistry, Clinical* ; Classification ; Korea* ; Methods

BACKGROUND: The conventional indocyanine green retention rate at 15 minutes (ICG R15) test is inefficient and inconvenient because it requires the use of a manual spectrophotometer and several samples per patient. This study aimed to establish the automation of the ICG R15 test using an automated clinical chemistry analyzer, and to evaluate the calculation of R15 with a small number of samples. METHODS: The performance of the AU5832 (Beckman Coulter, USA) for determining ICG concentration was evaluated in accordance with the Clinical Laboratory Standards Institute (CLSI) guidelines. The R15 results for 77 patients determined by spectrophotometry and AU5832 were compared. We evaluated the calculation of R15 with three samples, except for one sample in which the results had been obtained previously, at 5, 10, and 15 minutes after injection of ICG into the patients, and compared the results with those obtained with four samples. RESULTS: The automated ICG test using the AU5832 system showed proper performances according to CLSI. Although the difference in the R15 results between the two methods was within the 95% confidence interval, the R15 was adjusted by the regression equation because it was slightly lower according to the automated method compared with the manual method. The R15 with three samples (0, 5, and 15 minutes) showed the best correlation with conventional R15 with four samples (r2=0.996). Compared with the manual method, the R15 result using the AU5832 showed excellent agreement with four samples (kappa value 0.904) and with three samples (kappa value 0.880). CONCLUSIONS: The ICG R15 test using the AU5832 system is comparable with the conventional method in clinical use.
Automation ; Chemistry, Clinical* ; Humans ; Indocyanine Green* ; Methods ; Spectrophotometry

BACKGROUND: JEOL BioMajesty JCA-BM6010/C (JCA-BM6010/C, JEOL Ltd., Japan) is a recently developed ultra-compact automated clinical chemistry analyzer with a throughput of 1,200 tests per hour. Here, we present the first performance evaluation of JCA-BM6010/C. METHODS: We evaluated the precision, linearity, correlation, accuracy, and carryover of 11 analytes (ALP, ALT, AST, calcium, creatinine, GGT, glucose, LDH, total bilirubin, total protein, and uric acid) using the JEOL closed reagent (JEOL Ltd.) according to the guidelines of the Clinical Laboratory Standards Institute. Linearity was further evaluated for ALT, AST, and GGT using open reagents by Sekisui (Japan). The performance of JCA-BM6010/C was compared to that of the Roche-Hitachi Cobas 8000 c702 chemistry autoanalyzer (Cobas 8000, Roche Diagnostics, Switzerland). Its performance using open reagents from Denka Seiken (Japan), Roche, and Sekisui was also evaluated. RESULTS: The total coefficients of variation (CV) for all analytes were 1.0–2.7%. Linearity was observed for all analytes over the entire tested analytical range (R²≥0.99). The results of JCA-BM6010/C strongly correlated (r≥0.988) with those of Cobas 8000 for all evaluated analytes except LDH (r=0.963), as well as for all open reagents. Recovery rates for creatinine, glucose, calcium, and uric acid were 96.6–101.5% and 98.7–109.3% with the JEOL exclusive and open reagents, respectively. Sample carryover was less than 0.34%. CONCLUSIONS: JCA-BM6010/C showed acceptable performance in the precision, linearity, correlation, accuracy, and sample carryover analyses and in the method comparison. Therefore, it could be a useful routine laboratory medical analyzer.
Bilirubin ; Calcium ; Chemistry ; Chemistry, Clinical* ; Creatinine ; Glucose ; Indicators and Reagents ; Methods ; Uric Acid

BACKGROUND: For convenience, multiple instruments can be used to measure the same laboratory results within one health care system. However, the laboratory must verify the comparability of the results. In this study, we evaluated a method for verifying the comparability of patient results obtained from two instruments within one health care system, EP31-A-IR, proposed by the Clinical and Laboratory Standards Institute. METHODS: Using the range test proposed by the EP31-A-IR, we evaluated the comparability of 17 clinical chemistry test results from the HITACHII/MODULAR system (Roche Diagnostics, Switzerland) and the TOSHIBA/200FR system (Toshiba Medical Systems Co., Japan). The 0.33× biological variability, allowable total error, and standards of the Clinical Laboratory Improvement Amendments were used to determine the acceptance criteria. RESULTS: Among 16 test parameters, the differences of means between the two instruments were less than their range rejection limit in 15 tests, and so the comparability between the two instruments was considered acceptable. Creatinine was not evaluated using this protocol because its range rejection limit was not deducible from the EP31-A-IR statistics table. CONCLUSIONS: The EP31-A-IR guideline is useful for verifying the comparability of results between two instruments. However, not all parameters are covered by the guideline. With consideration of the characteristics of each test parameter, each laboratory should devise its own method for evaluating comparability.
Clinical Chemistry Tests ; Creatinine ; Delivery of Health Care* ; Humans ; Methods ; Quality Control

High oxygen-affinity hemoglobin (Hb) variants and a 2,3-diphosphoglycerate (2,3-DPG) deficiency could cause congenital (familial) erythrocytosis. High oxygen-affinity Hb variants and a 2,3-DPG deficiency might result in low tissue oxygen tension left-shifted oxygen dissociation curves and reduction in the standard P₅₀ value (P(50,std), oxygen tension at which haemoglobin is 50% saturated). Hence, the P(50,std) value is considered while formulating diagnostic strategies for erythrocytosis. In this study, we established a reference range for P(50,std) using an International Federation of Clinical Chemistry and Laboratory Medicine-approved equation (Hill's equation) for individual single venous/arterial blood samples. Blood gas analysis results of 243 samples with oxygen saturation ranging from 40%–90% (Hb < 16 mg/dL) were selected. The reference range of P(50,std) was in the 2.5th–97.5th percentile, and was 25.9–27.3 mm Hg. Hill's equation is a simple approved method for evaluating the P(50,std) values. Only a single sample of venous or arterial blood and a blood gas analyser are required to obtain the P(50,std). Our study provides a useful tool for the diagnostic work-up of patients with erythrocytosis.
2,3-Diphosphoglycerate ; Blood Gas Analysis ; Chemistry, Clinical ; Humans ; Methods ; Oxygen ; Polycythemia* ; Reference Values

Albumin is the most abundant protein in human serum. A dye-binding method is commonly used in clinical laboratories for its estimation using different types of dyes. However, all these dye methods were interfered by a variety of compounds. Here we present a method for the detection of albumin in human serum and other biological fluids. The principle is based on the fact that lactate dehydrogenase isoenzyme-5 (LDH-5) binds specifically to Dextran-Blue (DB). Albumin inhibits the binding of LDH-5 with DB. Absence of LDH activity in DB fraction after gel filtration indicates the presence of albumin in sample and vice versa.
Chemistry, Clinical/*methods ; Chromatography, Gel ; Human ; Isoenzymes/metabolism ; Lactate Dehydrogenase/metabolism ; Protein Binding ; Sepharose/chemistry ; Serum Albumin/*analysis

In 2017, the clinical chemistry proficiency testing program consisted of 24 programs with the addition of the urine chemistry program in the Korean Association of External Quality Assessment Service. The routine chemistry program consisted of 32 test items, including osmolality, total CO2, and estimated glomerular filtration rate tests, and the urine chemistry program consisted of 12 test items, including the albumin test. Based on the information and results of each test item entered by each institution, statistical analysis data according to test method, instrument, and reagent were reported. The statistics included the number of participating institutions, mean, standard deviation, coefficient of variation, median, minimum, and maximum values for each group. Each report was composed of a table, histogram, Levy-Jennings chart, and standard deviation index showing statistics by each test item. A total of 14 items, including albumin, were evaluated by more than 1,000 institutions, and the number of participating institutions is continuously increasing. The coefficient of variation tended to increase, as the concentration of the control material was lower for each test item. Most of them showed a coefficient of variation within 10%. Alkaline phosphatase and lactate dehydrogenase were found to have high coefficients of variation due to differences in measurement values between measurement methods. The distribution of measurement methods in general chemistry test items was not significantly different from that of previous years, and the distribution of measurement methods for albumin, glucose, phosphorus, and protein among the urine chemistry program was different from that of the routine chemistry program.
Alkaline Phosphatase ; Chemistry* ; Chemistry, Clinical ; Glomerular Filtration Rate ; Glucose ; Korea* ; L-Lactate Dehydrogenase ; Methods ; Osmolar Concentration ; Phosphorus

BACKGROUND: Measurement uncertainty characterizes the dispersion of the quantity values attributed to a measurand. Although this concept was introduced to medical laboratories some years ago, not all medical researchers are familiar with it. Therefore, the evaluation and expression of measurement uncertainty must be highlighted using a practical example. METHODS: In accordance with the procedure for evaluating and expressing uncertainty, provided by the Joint Committee for Guides in Metrology (JCGM), we used plasma glucose (Glu) as an example and defined it as the measurand. We then analyzed the main sources of uncertainty, evaluated each component of uncertainty, and calculated the combined uncertainty and expanded uncertainty with 2 budgets for single measurements and continuous monitoring, respectively. RESULTS: During the measurement of Glu, the main sources of uncertainty included imprecision, within-subject biological variance (BVw), calibrator uncertainty, and systematic bias. We evaluated the uncertainty of each component to be 1.26%, 1.91%, 5.70%, 0.42%, and -2.87% for within-run imprecision, between-day imprecision, BVw, calibrator uncertainty, and systematic bias, respectively. For a single specimen, the expanded uncertainty was 7.38% or 6.1+/-0.45 mmol/L (kappa=2); in continuous monitoring of Glu, the expanded uncertainty was 13.58% or 6.1+/-0.83 mmol/L (kappa=2). CONCLUSIONS: We have demonstrated the overall procedure for evaluating and reporting uncertainty with 2 different budgets. The uncertainty is not only related to the medical laboratory in which the measurement is undertaken, but is also associated with the calibrator uncertainty and the biological variation of the subject. Therefore, it is helpful in explaining the accuracy of test results.
Blood Chemical Analysis/methods/standards ; Clinical Chemistry Tests/*methods/standards ; Glucose/*analysis/standards ; Humans ; Models, Statistical ; Quality Control ; *Uncertainty

BACKGROUND: Measurement uncertainty characterizes the dispersion of the quantity values attributed to a measurand. Although this concept was introduced to medical laboratories some years ago, not all medical researchers are familiar with it. Therefore, the evaluation and expression of measurement uncertainty must be highlighted using a practical example. METHODS: In accordance with the procedure for evaluating and expressing uncertainty, provided by the Joint Committee for Guides in Metrology (JCGM), we used plasma glucose (Glu) as an example and defined it as the measurand. We then analyzed the main sources of uncertainty, evaluated each component of uncertainty, and calculated the combined uncertainty and expanded uncertainty with 2 budgets for single measurements and continuous monitoring, respectively. RESULTS: During the measurement of Glu, the main sources of uncertainty included imprecision, within-subject biological variance (BVw), calibrator uncertainty, and systematic bias. We evaluated the uncertainty of each component to be 1.26%, 1.91%, 5.70%, 0.42%, and -2.87% for within-run imprecision, between-day imprecision, BVw, calibrator uncertainty, and systematic bias, respectively. For a single specimen, the expanded uncertainty was 7.38% or 6.1+/-0.45 mmol/L (kappa=2); in continuous monitoring of Glu, the expanded uncertainty was 13.58% or 6.1+/-0.83 mmol/L (kappa=2). CONCLUSIONS: We have demonstrated the overall procedure for evaluating and reporting uncertainty with 2 different budgets. The uncertainty is not only related to the medical laboratory in which the measurement is undertaken, but is also associated with the calibrator uncertainty and the biological variation of the subject. Therefore, it is helpful in explaining the accuracy of test results.
Blood Chemical Analysis/methods/standards ; Clinical Chemistry Tests/*methods/standards ; Glucose/*analysis/standards ; Humans ; Models, Statistical ; Quality Control ; *Uncertainty