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EQA-based evaluation of metrological traceability of clinical chemistry test results in Argentina
Abstract and Figures
Equivalence of results among laboratories is a major mission for medical laboratories. In the Netherlands,
medical laboratories only use homogenous, commercial for general chemistry analytes,
whereas in Argentina heterogenous, home brew test applications are common. The effect of this
practice difference on test accuracy is studied using key features of the accuracy-based EQA
program of the Netherlands. Six frozen, human-based, commutable poolsera, covering the (patho)
physiological measuring range for 17 general chemistry analytes, were assayed by 75 Argentinian
labs and 200 Dutch laboratories in 2014. After removal of outliers, harmonization status among
laboratories was evaluated by calculating overall mean interlaboratory coef cients of variation
(CVs, %) per analyte and per country for all 6 levels. Evenso, standardization status was evaluated
after removal of outliers by calculating overall mean recoveries (%) as compared to the assigned
target values per analyte per country for all 6 levels. Absolute median biases were compared to
(minimal/desirable) biases derived from biological variation criteria. For serum enzymes interlaboratory
CVs in the Argentinian laboratories ranged between 10 and 22%, as compared to 3-6%
in the Netherlands. For serum uric acid, creatinine, glucose and total protein, interlaboratory CVs
varied between 4.3 and 13.1% in Argentinian labs, as compared to <3.5% in the Netherlands. For
serum electrolytes, interlaboratory CVs ranged between 1.8 and 3.8% for Na+; 2.9-5.8% for Cl-;
3.8-7.5% for K+; 9.4-10.4% for Ca2+ and 16.2-22.3% for Mg2+ as compared to 2% (Na+, K+,
Cl-, Ca2+) and 3% (Mg2+) in the Netherlands. Mean recoveries in Argentinian laboratories for e.g.
serum creatinine, glucose, CK, Ca2+ and Na+ were 95-119%; 95-104%; 98-102%; 98-102% and
96-100% respectively, whereas min-max recovery ranges were 65-155%; 58-126%; 47-132%;
66-132% and 85-115%. In the Netherlands, absolute mean recoveries were overall 98.9% with a
SD of 2.0%. Median biases in Argentinian laboratories ranged from -2.9 to 18.2%; -3.1 - 2.6%;
-3.3 - 0.5%; -1.1 - 3.8% and -4.3-0% for serum creatinine, glucose, CK, Ca2+ and Na+. In the
Netherlands overall mean/median biases were 1.1% (SD=2.0%). Exchange of commutable, value-
assigned EQA-materials was helpful for studying the harmonization and standardization status
of medical tests in Argentina, and for revealing the future harmonization and standardization
potential. The results clearly demonstrate that metrological traceability of test results in Argentina
is on average in line with what is expected; yet, the spreading among laboratories is far too high
and should be improved.
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Las pruebas de laboratorio tienen un impacto crítico en la toma de decisiones médicas. Sin embargo, en el ciclo, que inicia con la solicitud del médico y termina con la interpretación final, pueden ocurrir errores en cualquier etapa. La evaluación de las causas que generan estos errores y la toma de medidas para detectarlos y prevenirlos, antes de que causen daño, es crítico en el proceso analítico. Esto se logra a través de la gestión de riesgos (GR). La norma EP23A, Control de calidad en el laboratorio basado en la gestión de riesgos, del Clinical Laboratory Standards Institute (CLSI), introduce los principios de la GR para los laboratorios clínicos. Esta directriz permite desarrollar un plan de GR, individualizado. Este artículo resume los principios de la GR en los laboratorios clínicos.
Introduction
Two approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory.
Materials and methods
Sigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa − bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis.
Results
For the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values.
Conclusions
Both methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.
Background:
The European Atherosclerosis Society-European Federation of Clinical Chemistry and Laboratory Medicine Consensus Panel aims to provide recommendations to optimize atherogenic lipoprotein quantification for cardiovascular risk management.
Content:
We critically examined LDL cholesterol, non-HDL cholesterol, apolipoprotein B (apoB), and LDL particle number assays based on key criteria for medical application of biomarkers. (a) Analytical performance: Discordant LDL cholesterol quantification occurs when LDL cholesterol is measured or calculated with different assays, especially in patients with hypertriglyceridemia >175 mg/dL (2 mmol/L) and low LDL cholesterol concentrations <70 mg/dL (1.8 mmol/L). Increased lipoprotein(a) should be excluded in patients not achieving LDL cholesterol goals with treatment. Non-HDL cholesterol includes the atherogenic risk component of remnant cholesterol and can be calculated in a standard nonfasting lipid panel without additional expense. ApoB more accurately reflects LDL particle number. (b) Clinical performance: LDL cholesterol, non-HDL cholesterol, and apoB are comparable predictors of cardiovascular events in prospective population studies and clinical trials; however, discordance analysis of the markers improves risk prediction by adding remnant cholesterol (included in non-HDL cholesterol) and LDL particle number (with apoB) risk components to LDL cholesterol testing. (c) Clinical and cost-effectiveness: There is no consistent evidence yet that non-HDL cholesterol-, apoB-, or LDL particle-targeted treatment reduces the number of cardiovascular events and healthcare-related costs than treatment targeted to LDL cholesterol.
Summary:
Follow-up of pre- and on-treatment (measured or calculated) LDL cholesterol concentration in a patient should ideally be performed with the same documented test method. Non-HDL cholesterol (or apoB) should be the secondary treatment target in patients with mild to moderate hypertriglyceridemia, in whom LDL cholesterol measurement or calculation is less accurate and often less predictive of cardiovascular risk. Laboratories should report non-HDL cholesterol in all standard lipid panels.
OBJECTIVE
Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics.
MATERIALS AND METHODS
This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters.
RESULTS
For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy.
CONCLUSION
This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.
Low density lipoprotein (LDL) is the predominant atherogenic lipoprotein particle in the circulation. Conventionally, a fasting lipid profile has been used for atherosclerotic cardiovascular disease (ASCVD) risk assessment. A non-fasting sample is now regarded as a suitable alternative to a fasting sample. In routine clinical practice, the Friedewald equation is used to estimate LDL-cholesterol, but it has limitations. Commercially available direct measures of LDL-cholesterol are not standardised. LDL-cholesterol is a well-established risk factor for ASCVD, being the primary therapeutic target in both primary and secondary prevention. Non-high-density lipoprotein (HDL)-cholesterol is a measure of the cholesterol content in the atherogenic lipoproteins, but it does not reflect the particle number. Non-HDL-cholesterol has the advantage over LDL-cholesterol of including remnant cholesterol and being independent of triglyceride variability, but it is compromised by the non-specificity bias of direct HDL-cholesterol methods used in the calculation. Apolipoprotein (apo) B, the major structural protein in very low-density lipoprotein, intermediate density lipoprotein, LDL and lipoprotein (a), is a measure of the number of atherogenic lipoproteins. ApoB methods are standardised, but the assay comes at an additional, albeit relatively low cost. Non-HDL-cholesterol and apoB are more accurate measures than LDL-cholesterol in hypertriglyceridaemic individuals, non-fasting samples, and in those with very-low LDL-cholesterol concentrations. Accumulating evidence suggests that non-HDL-cholesterol and apoB are superior to LDL-cholesterol in predicting ASCVD risk, and both have been designated as secondary targets in some treatment guidelines. We review the measurement, potential role, utility and current status of non-HDL-cholesterol and apoB when compared with LDL-cholesterol in ASCVD risk assessment.
Before 2009 essentially all societies, guidelines, and statements required fasting before measuring a lipid profile for cardiovascular risk prediction. This was mainly due to the increase seen in triglycerides during a fat tolerance test. However, individuals eat much less fat during a normal day and nonfasting triglycerides have been shown to be superior to fasting in predicting cardiovascular risk. Lipids and lipoproteins only change minimally in response to normal food intake: in four large prospective studies, maximal mean changes were +0.3 mmol/L (26 mg/dL) for triglycerides, –0.2 mmol/L (8 mg/dL) for total cholesterol, –0.2 mmol/L (8 mg/dL) for LDL cholesterol, and –0.1 mmol/L (4 mg/dL) for HDL cholesterol. Further, in 108,602 individuals from the Copenhagen General Population Study in random nonfasting samples, the highest versus the lowest quartile of triglycerides, total cholesterol, LDL cholesterol, remnant cholesterol, non-HDL cholesterol, lipoprotein(a), and apolipoprotein B were all associated with higher risk of both ischaemic heart disease and myocardial infarction. Finally, lipid-lowering trials using nonfasting blood samples for assessment of lipid levels found that reducing levels of nonfasting lipids reduced the risk of cardiovascular disease. To date there is no sound scientific evidence as to why fasting should be superior to nonfasting when evaluating a lipid profile for cardiovascular risk prediction. Indeed, nonfasting samples rather than fasting samples have many obvious advantages. First, it would simplify blood sampling in the laboratory. Second, it would benefit the patient, avoiding the inconvenience of fasting and therefore needing to have blood drawn early in the day. Third, for individuals with diabetes, the risk of hypoglycaemia due to fasting would be minimised. Many countries are currently changing their guidelines towards a consensus on measuring a lipid profile for cardiovascular risk prediction in the nonfasting state, simplifying blood sampling for patients, laboratories, and clinicians worldwide.
Background:
Lipoprotein(a) [Lp(a)] is reported as Lp(a) particle mass (mg/dL) or molar concentration of apolipoprotein(a) [apo(a)] (nmol/L), which is considered the gold standard. Values are often converted from one measurement to the other but the validity of this is unknown.
Objectives:
To quantify the relationship between Lp(a) molar concentration and Lp(a) mass in the context of various Lp(a) level thresholds and apo(a) isoform size.
Methods:
In all samples, Lp(a) levels in molar concentration and apo(a) isoform size were determined at the Northwest Lipid Metabolism and Diabetes Research Laboratories (NLMDRL). Lp(a) mass levels were determined at the University of California, San Diego (UCSD) (1635 samples), by 5 commercially available assays: Denka 1 and Denka 2 (each 80 samples), 2 turbidimetric assays (2545 and 2673 samples, respectively), and an enzyme-linked immunosorbent assay (2605 samples). The ratios between Lp(a) molar concentration and mass (eg, nmol/L/mg/dL) were calculated and related to apo(a) isoform size.
Results:
The mean (SD) ratios for NLMDRL/UCSD, NLMDRL/Denka1, and NLMDRL/Denka2 were 2.42 (1.25), 1.64 (0.18), and 2.02 (0.22), respectively. The ratios for NLMDRL/UCSD, NLMDRL/Denka1, and NLMDRL/Denka2 increased by Lp(a) cutoffs, with ratios of 1.82, 1.52, and 1.87, respectively, for Lp(a) < 75 nmol/L and 2.80, 1.89, and 2.24, respectively, for Lp(a) > 125 nmol/L. For the commercial turbidimetric assays and enzyme-linked immunosorbent assay, the ratios ranged from <1 to >5.
Conclusions:
Lp(a) molar/mass ratios are threshold, method, and isoform dependent. A single factor between assays is not appropriate. These data support the transition of Lp(a) mass assays to molar concentration to improve diagnostic and clinical interpretation of Lp(a)-mediated risk.
Background:
The European Federation of Clinical Chemistry and Laboratory Medicine European Biological Variation Study (EuBIVAS) has been established to deliver rigorously determined data describing biological variation (BV) of clinically important measurands. Here, EuBIVAS-based BV estimates of serum electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose, as well as their associated analytical performance specifications (APSs), are presented.
Method:
Samples were drawn from 91 healthy individuals (38 male, 53 female; age range, 21-69 years) for 10 consecutive weeks at 6 European laboratories. Samples were stored at -80 °C before duplicate analysis of all samples on an ADVIA 2400 (Siemens Healthineers). Outlier and homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data, when relevant, to determine BV estimates with CIs.
Results:
The within-subject BV (CVI) estimates of all measurands, except for urea and LDL cholesterol, were lower than estimates available in an online BV database, with differences being most pronounced for HDL cholesterol, glucose, and direct bilirubin. Significant differences in CVI for men and women/women <50 years of age were evident for uric acid, triglycerides, and urea. The CVA obtained for sodium and magnesium exceeded the EuBIVAS-based APS for imprecision.
Conclusions:
The EuBIVAS, which is fully compliant with the recently published Biological Variation Data Critical Appraisal Checklist, has produced well-characterized, high-quality BV estimates utilizing a stringent experimental protocol. These new reference data deliver revised and more exacting APS and reference change values for commonly used clinically important measurands, thus having direct relevance to diagnostics manufacturers, service providers, clinical users, and ultimately patients.
We developed a laboratory incident report classification system that can guide reduction of actual and potential adverse events. The system was applied retrospectively to 129 incident reports occurring during a 16-month period. Incidents were classified by type of adverse event (actual or potential), specific and potential patient impact, nature of laboratory involvement, testing phase, and preventability. Of 129 incidents, 95% were potential adverse events. The most common specific impact was delay in receiving test results (85%). The average potential impact was 2.9 (SD, 1.0; median, 3; scale, 1-5). The laboratory alone was responsible for 60% of the incidents; 21% were due solely to problems outside the laboratory's authority. The laboratory function most frequently implicated in incidents was specimen processing (31%). The preanalytic testing phase was involved in 71% of incidents, the analytic in 18%, and the postanalytic in 11%. The most common preanalytic problem was specimen transportation (16%). The average preventability score was 4.0 (range, 1-5; median, 4; scale, 1-5), and 94 incidents (73%) were preventable (score, 3 or more). Of the 94 preventable incidents, 30% involved cognitive errors, defined as incorrect choices caused by insufficient knowledge, and 73% involved noncognitive errors, defined as inadvertent or unconscious lapses in expected automatic behavior.
The commentary tries to reply to the following three questions: Are External Quality Assessment Schemes (EQAS) really fit for purpose? Are all schemes equivalent and sufficiently harmonized? Is the role of EQAS similar and necessary in all branches of laboratory medicine? Although the reply to the first two questions is, unfortunately, negative for several reasons (lack of commutable material with reference method values, EQAS with different scopes, etc.), the reply to the third one is positive: EQAS are a necessary source of information on trueness and accuracy and must be fully developed for all the branches of the clinical laboratory.