Sex Hormones and Adrenal Steroids: Biological Variation Estimated Using Direct and Indirect Methods.

BACKGROUND: Biological variation (BV) data may be used to develop analytical performance specifications (APS), reference change values (RCV), and support the applicability of population reference intervals. This study estimates within-subject BV (CVI) for several endocrine biomarkers using 3 different methodological approaches. METHODS: For the direct method, 30 healthy volunteers were sampled weekly for 10 consecutive weeks. Samples were analyzed in duplicate for 17-hydroxyprogesterone (17-OHP), androstenedione, cortisol, cortisone, estradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex hormone-binding globulin (SHBG), and testosterone. A CV-ANOVA with outlier removal and a Bayesian model were applied to derive the CVI. For estradiol, FSH and LH, only the male subgroup was included. In the indirect method, using the same analytes and groups, pairs of sequential results were extracted from the laboratory information system. The total result variation for individual pairs was determined by identifying a central gaussian distribution in the ratios of the result pairs. The CVI was then estimated by removing the effect of analytical variation. RESULTS: The estimated CVI from the Bayesian model (µCVP(i)) in the total cohort was: 17-OHP, 23%; androstenedione, 20%; cortisol, 18%; cortisone, 11%; SHBG, 7.4%; testosterone, 16%; and for the sex hormones in men: estradiol, 14%; FSH, 8%; and LH, 26%. CVI-heterogeneity was present for most endocrine markers. Similar CVI data were estimated using the CV-ANOVA and the indirect method. CONCLUSIONS: Similar CVI data were obtained using 2 different direct and one indirect method. The indirect approach is a low-cost alternative ensuring implementation of CVI data applicable for local conditions.

Biological Variation Estimates Obtained from 91 Healthy Study Participants for 9 Enzymes in Serum.

BACKGROUND: We sought to develop estimates of biological variation (BV) for 9 enzymes in blood serum as part of the European Biological Variation Study. METHODS: Ninety-one healthy study participants (38 male and 53 female, 21-69 years old) were phlebotomized in each of 10 consecutive weeks at 6 European laboratories. The same preanalytical sample-handling protocol was followed at each center before transport to San Raffaele Hospital, Milan, Italy, for analysis. Sera were stored at -80 °C before analysis in duplicate within a single run on an ADVIA 2400 Clinical Chemistry System (Siemens Healthcare) following a protocol designed to minimize analytical imprecision. Assay traceability was established using frozen sera with target values assigned by reference methods. The results were subjected to outlier analysis before CV-ANOVA to deliver valid BV estimates. Results for 9 enzymes were subsequently partitioned for graphical display allowing visual assessment of the effects of country of origin, sex, and age on BV estimates. RESULTS: We found no effect of country upon the observed variation, but overall sex-related differences were evident for alanine amino transferase (ALT), γ-glutamyl transferase (GGT), and creatine kinase (CK). The following estimates for within-subject BV (CVI) and between-subject BV (CVG), respectively, were obtained: ALT: 9.3%, 28.2%; aspartate aminotransferase: 9.5%, 20.3%; GGT: 8.9%, 41.7%; alkaline phosphatase : 5.3%, 24.9%; lactate dehydrogenase: 5.2%, 12.6%; CK: 14.5%, 31.5%; amylase: 6.8%, 30.4%; pancreatic α-amylase: 6.3%, 24.9%; and lipase (LIP): 7.7%, 23.8%. CONCLUSIONS: All CVI and some CVG estimates were lower than those reported in the online BV 2014 updated database. Analytical performance specifications derived from BV can be applied internationally.

The EuBIVAS Project: Within- and Between-Subject Biological Variation Data for Serum Creatinine Using Enzymatic and Alkaline Picrate Methods and Implications for Monitoring.

BACKGROUND: The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) European Biological Variation Study (EuBIVAS) has been established to deliver rigorously determined biological variation (BV) indices. EuBIVAS determined BV for serum creatinine using the enzymatic and alkaline picrate measurement methods. METHOD: In total, 91 healthy individuals (38 males, 53 females; age range, 21-69 years) were bled for 10 consecutive weeks at 6 European laboratories. An equivalent protocol was followed at each center. Sera were stored at -80 °C before analysis. Analyses for each patient were performed in duplicate within a single run on an ADVIA 2400 system (San Raffaele Hospital, Milan). The data were subjected to outlier and homogeneity analysis before performing CV-ANOVA to determine BV and analytical variation (CVA) estimates with confidence intervals (CI). RESULTS: The within-subject BV estimates [CVI (95% CI)] were similar for enzymatic [4.4% (4.2-4.7)] and alkaline picrate [4.7% (4.4-4.9)] methods and lower than the estimate presently available online (CVI = 5.9%). No significant male/female BV differences were found. Significant differences were observed in mean creatinine values between men and women and between Turkish individuals and those of other nationalities. Between-subject BV (CVG) estimates, stratified accordingly, produced CVG values similar to historical BV data. CVA was 1.1% for the enzymatic and 4.4% for alkaline picrate methods, indicating that alkaline picrate methods fail to fulfill analytical performance specifications for imprecision (CVAPS). CONCLUSIONS: The serum creatinine CVI obtained by EuBIVAS specifies a more stringent CVAPS than previously identified. The alkaline picrate method failed to meet this CVAPS, raising questions regarding its future use.

Sample collections from healthy volunteers for biological variation estimates' update: a new project undertaken by the Working Group on Biological Variation established by the European Federation of Clinical Chemistry and Laboratory Medicine.

BACKGROUND: Biological variation (BV) data have many fundamental applications in laboratory medicine. At the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) the reliability and limitations of current BV data were discussed. The EFLM Working Group on Biological Variation is working to increase the quality of BV data by developing a European project to establish a biobank of samples from healthy subjects to be used to produce high quality BV data. METHODS: The project involved six European laboratories (Milan, Italy; Bergen, Norway; Madrid, Spain; Padua, Italy; Istanbul, Turkey; Assen, The Netherlands). Blood samples were collected from 97 volunteers (44 men, aged 20-60 years; 43 women, aged 20-50 years; 10 women, aged 55-69 years). Initial subject inclusion required that participants completed an enrolment questionnaire to verify their health status. The volunteers provided blood specimens once per week for 10 weeks. A short questionnaire was completed and some laboratory tests were performed at each sampling consisting of blood collected under controlled conditions to provide serum, K2EDTA-plasma and citrated-plasma samples. RESULTS: Samples from six out of the 97 enroled subjects were discarded as a consequence of abnormal laboratory measurements. A biobank of 18,000 aliquots was established consisting of 120 aliquots of serum, 40 of EDTA-plasma, and 40 of citrated-plasma from each subject. The samples were stored at -80 °C. CONCLUSIONS: A biobank of well-characterised samples collected under controlled conditions has been established delivering a European resource to enable production of contemporary BV data.

Estimation of the minimal preanalytical uncertainty for 15 clinical chemistry serum analytes.

BACKGROUND: We sought a model to estimate preanalytical uncertainty of blood samples collected and processed by using optimal procedures. METHODS: Optimal preanalytical handling of blood samples included use of a loosely fastened tourniquet, wide bore needles, recommended clotting time and centrifugation speed, and minimal storage before analysis. Blood was collected from each arm of 20 volunteers into 2 rapid-serum tubes and 2 serum-separation tubes. Linear mixed-effects models were used to estimate the between-venipuncture SD, the preanalytical SD (excluding venipuncture), the measurement repeatability SD, and systematic differences between the tubes and between venipunctures. RESULTS: No significant systematic differences were found between successive venipunctures. However, statistically significant mean differences were seen between serum-separation tubes and rapid-serum tubes for 7 of the 15 analytes. The preanalytical SD (excluding venipuncture) for lactate dehydrogenase (3.2 U/L, 95% CI 2.8-3.7) was significantly higher than the SD for measurement repeatability (1.9 U/L, 95% CI 1.7-2.1). For potassium both the preanalytical SD (excluding venipuncture) (0.092 mmol/L, 95% CI 0.080-0.11) and the between-venipuncture SD (0.075 mmol/L, 95% CI 0.048-0.12) were significantly higher than the measurement-repeatability SD (0.031 mmol/L, 95% CI 0.028-0.035). For glucose the between-venipuncture SD (0.20 mmol/L, 95% CI 0.14-0.27) was significantly higher than the preanalytical SD (excluding venipuncture) (0.07 mmol/L, 95% CI 0.06-0.08), and the measurement repeatability SD (0.057 mmol/L, 95% CI 0.051-0.064). CONCLUSIONS: By applying linear mixed-effects models we have estimated the minimal preanalytical uncertainty that will influence all patient results.

Random variation and systematic error caused by various preanalytical variables, estimated by linear mixed-effects models.

BACKGROUND: We wanted to determine whether specific, preanalytical sample handling increases preanalytical variation and bias test results compared with optimal handling. METHODS: Blood was collected into 4 serum-separation tubes from each arm of 60 outpatients. In 30 of the patients, half of the tubes were transported in the pneumatic tube system, while the other half were manually delivered. In the remaining patients, the blood samples were collected using 21-gauge straight needles (green needles) and 23-gauge butterfly needles. Half of the tubes were mixed by inverting 5-6 times, and the other half by one inversion. Linear mixed-effects models were used as statistical method. RESULTS: Transporting samples in the pneumatic tube system caused a significant bias to the results for LD (4.5 U/L, p<0.001) and magnesium (0.0021 mmol/L, p=0.003). For CK and glucose, the preanalytical variation was significantly higher for samples transported in the pneumatic tube system vs manual delivery. Using butterfly needles resulted in lower values (p<0.05) for calcium (-0.0072 mmol/L), CK (-0.75 U/L) and LD (-1.6 U/L) compared with 21-gauge needles. The preanalytical variation for ALP was significantly higher with butterfly needles. CONCLUSIONS: The specific sample handling had significant but small random and systematic effects on results for some analytes.