Plasma creatinine medians from patients partitioned by gender and age used as a tool for assessment of analytical stability at different concentrations.

Background Monthly medians of patient results are useful in assessment of analytical quality in medical laboratories. Separate medians by gender makes it possible to generate two independent estimates of contemporaneous errors. However, for plasma creatinine, reference intervals (RIs) are different by gender and also higher over 70 years of age. Methods Daily, weekly and monthly patient medians were calculated from the raw data of plasma creatinine concentrations for males between 18 and 70 years, males >70 years, females between 18 and 70 years and females >70 years. Results The medians of the four groups were all closely associated, with similar patterns. The mean of percentage bias from each group defined the best estimate of bias. The maximum half-range (%) of the bias evaluations provided an estimate of the uncertainty comparable to the analytical performance specifications: thus, bias estimates could be classified as optimum, desirable or minimum quality. Conclusions Medians by gender and age are useful in assessment of analytical stability for plasma creatinine concentration ranging from 60 to 90 µmol/L. The daily medians are valuable in rapid detection of large systematic errors, the weekly medians in detecting minor systematic errors and monthly medians in assessment of long-term analytical stability.

Within-subject biological variation of activated partial thromboplastin time, prothrombin time, fibrinogen, factor VIII and von Willebrand factor in pregnant women.

BACKGROUND: During pregnancy, interpretation of results from coagulation parameters can be difficult as the physiological changes that occur may affect the biochemical parameters. The aim of this study was to describe the normal course of five coagulation parameters in healthy pregnancies, and to estimate the within-subject biological variation (CVI). METHODS: Blood samples were obtained every 4th week during pregnancy and three samples after delivery in 20 healthy women and every 4th week during a 40-week period in 19 healthy non-pregnant women. Activated partial thromboplastin time (APTT), prothrombin time (PT), PT International Normalized Ratio (INR), fibrinogen, factor VIII clot (FVIII:C) and von Willebrand factor antigen (vWF:Ag) were analyzed. The physiological changes during pregnancy were compensated by transformation into multiples of the median (MoM) and it is natural logarithm (lnMoM) in order to establish a kind of steady state, and CVI was calculated from the standard deviation. RESULTS: During pregnancy, APTT, PT and INR remained unchanged or decreased, depending upon the reagent used, while fibrinogen, FVIII:C and vWF:Ag increased gradually until delivery. The CVI in pregnancy were 2.2 and 3.0% for APTT, 2.3 and 2.6% for PT, 2.2 and 2.3% for INR, 7.2% for fibrinogen, 12.2% for FVIII:C and 11.3% for vWF:Ag, and corresponded with the CVI in non-pregnant women. CONCLUSIONS: Transformation of coagulation parameters in healthy pregnancies to MoM is a tool to establish a kind of steady state. Although there is a physiological change in these coagulation parameters during pregnancy, the CVI after lnMoM transformation was comparable with the CVI of non-pregnant women.

Gender-partitioned patient medians of serum albumin requested by general practitioners for the assessment of analytical stability.

BACKGROUND: Recently, the use of separate gender-partitioned patient medians of serum sodium has revealed potential for monitoring analytical stability within the optimum analytical performance specifications for laboratory medicine. The serum albumin concentration depends on whether a patient is sitting or recumbent during phlebotomy. We therefore investigated only examinations requested by general practitioners (GPs) to provide data from sitting patients. METHODS: Weekly and monthly patient medians of serum albumin requested by GP for both male and female patients were calculated from the raw data obtained from three analysers in the hospital laboratory on examination of samples from those >18 years. The half-range of medians were applied as an estimate of the maximum bias. Further, the ratios between the two medians were calculated (females/males). RESULTS: The medians for male and female patients were closely related despite considerable variation due to the current analytical variation. This relationship was confirmed by the calculated half-range for the monthly ratio between the genders of 0.44%, which surpasses the optimum analytical performance specification for bias of serum albumin (0.72%). The weekly ratio had a half-range of 1.83%, which surpasses the minimum analytical performance specifications of 2.15%. CONCLUSIONS: Monthly gender-partitioned patient medians of serum albumin are useful for monitoring of long-term analytical stability, where the gender medians are two independent estimates of changes in (delta) bias: only results requested by GP are of value in this application to ensure that all patients are sitting during phlebotomy.

Separate patient serum sodium medians from males and females provide independent information on analytical bias.

BACKGROUND: During monitoring of monthly medians of results from patients undertaken to assess analytical stability in routine laboratory performance, the medians for serum sodium for male and female patients were found to be significantly related. METHODS: Daily, weekly and monthly patient medians of serum sodium for both male and female patients were calculated from results obtained on samples from the population >18 years on three analysers in the hospital laboratory. The half-range of medians was applied as an estimate of the maximum bias. Further, the ratios between the two medians were calculated. RESULTS: The medians of both genders demonstrated dispersions over time, but they were closely connected in like patterns, which were confirmed by the half-range of the ratios of medians for males and females that varied from 0.36% for daily, 0.14% for weekly and 0.036% for monthly ratios over all instruments. CONCLUSIONS: The tight relationship between the gender medians for serum sodium is only possible when raw laboratory data are used for calculation. The two patient medians can be used to confirm both and are useful as independent estimates of analytical bias during constant calibration periods. In contrast to the gender combined median, the estimate of analytical bias can be confirmed further by calculation of the ratios of medians for males and females.

Biological variation: Evaluation of methods for constructing confidence intervals for estimates of within-person biological variation for different distributions of the within-person effect.

BACKGROUND: Precise estimates of the within-person biological variation, CVI, can be essential both for monitoring patients and for setting analytical performance specifications. The confidence interval, CI, may be used to evaluate the reliability of an estimate, as it is a good measure of the uncertainty of the estimated CVI. The aim of the present study is to evaluate and establish methods for constructing a CI with the correct coverage probability and non-cover probability when estimating CVI. METHOD: Data based on 3 models for distributions for the within-person effect were simulated to assess the performance of 3 methods for constructing confidence intervals; the formula based method for the nested ANOVA, the percentile bootstrap and the bootstrap-t methods. RESULTS: The performance of the evaluated methods for constructing a CI varied, both dependent on the size of the CVI and the type of distributions. The bootstrap-t CI have good and stable performance for the models evaluated, while the formula based are more distribution dependent. The percentile bootstrap performs poorly. CONCLUSION: CI is an essential part of estimation of the within-person biological variation. Good coverage probability and non-cover probabilities for CI are achievable by using the bootstrap-t combined with CV-ANOVA. Supplemental R-code is provided online.

The variation in high sensitive cardiac troponin concentration during haemodialysis treatment is not similar to the biological variation observed in stable end stage renal disease patients.

BACKGROUND: Cardiovascular mortality is high in end stage renal disease (ESRD). This study aimed to: (1) calculate within-week within- and between-subject biological variation (CVI and CVG) for hs-cTn in ESRD; (2) determine the magnitude of hs-cTn concentration changes during haemodialysis (HD) treatment; and (3) compare the CVI and CVG to the within and between-subject variation of cTn concentration changes during HD treatments (CVDIFF-I and CVDIFF-G). METHODS: Serum samples were collected from 20 patients before and after 10 consecutive HD treatments. cTn were measured using the hs-cTnT (Roche Diagnostics) and the hs-cTnI (Abbott Diagnostics). The CVA, CVI, CVG, CVDIFF-I and CVDIFF-G, were estimated using nested ANOVA. RESULTS: The within-week data showed hs-cTnT CVA, CVI and CVG of 1.6, 7.3 and 94.4%. Reference change values (RCV) were estimated to -18.7-23.0%. The Index of individuality (II) was 0.08. Corresponding values for hs-cTnI were 5.3, 13.2 and 142.4%, whilst the RCV was -32.5-48.2% and the II was 0.10. The mean concentration of cTn decreased by -6.4% (hs-cTnT) and -7.6% (hs-cTnI) during HD treatment. The CVDIFF-I and CVDIFF-G was 4.2 and 6.3% for hs-cTnT, and 10.7 and 9.4% for hs-cTnI. The RCVDIFF was -18.2-5.4% (hs-cTnT) and -39.0-23.8% (hs-cTnI), respectively, and the IIDIFF-values were 0.7 and 1.3. CONCLUSIONS: The CVI and CVG are similar to earlier findings. Mean hs-cTn concentrations decreased during HD. The within-subject hs-cTn variation during HD is similar to the between-subject variation, i.e. determining a cut-off value for hs-cTn changes during HD may be useful.

Different percentages of false-positive results obtained using five methods for the calculation of reference change values based on simulated normal and ln-normal distributions of data.

Background Reference change values provide objective tools to assess the significance of a change in two consecutive results for a biomarker from an individual. The reference change value calculation is based on the assumption that within-subject biological variation has random fluctuation around a homeostatic set point that follows a normal (Gaussian) distribution. This set point (or baseline in steady-state) should be estimated from a set of previous samples, but, in practice, decisions based on reference change value are often based on only two consecutive results. The original reference change value was based on standard deviations according to the assumption of normality, but was soon changed to coefficients of variation (CV) in the formula (reference change value = ± Z c 2½ c CV). Z is being dependent on the desired probability of significance, which also defines the percentages of false-positive results. The aim of this study was to investigate false-positive results using five different published methods for calculation of reference change value. Methods The five reference change value methods were examined using normally and ln-normally distributed simulated data. Results One method performed best in approaching the theoretical false-positive percentages on normally distributed data and another method performed best on ln-normally distributed data. The commonly used reference change value method based on two results (without use of estimated set point) performed worst both on normally distributed and ln-normally distributed data. Conclusions The optimal choice of method to calculate reference change value limits requires knowledge of the distribution of data (normal or ln-normal) and, if possible, knowledge of the homeostatic set point.

Biological Variation: The Effect of Different Distributions on Estimated Within-Person Variation and Reference Change Values.

BACKGROUND: Good estimates of within-person biological variation, CVI, are essential for diagnosing and monitoring patients and for setting analytical performance specifications. The aim of the present study was to use computer simulations to evaluate the impact of various measurement distributions on different methods for estimating CVI and reference change value (RCV). METHOD: Data were simulated on the basis of 3 models for distributions of the within-person effect. We evaluated 3 different methods for estimating CVI: standard ANOVA, ln-ANOVA, and CV-ANOVA, and 3 different methods for calculating RCV: classic, ln-RCV, and a nonparametric method. We estimated CVI and RCV with the different methods and compared the results with the true values. RESULTS: The performance of the methods varied, depending on both the size of the CVI and the type of distributions. The CV-ANOVA model performed well for the estimation of CVI with all simulated data. The ln-RCV method performed best if data were ln-normal distributed or CVI was less than approximately 12%. The nonparametric RCV method performed well for all simulated data but was less precise. CONCLUSIONS: The CV-ANOVA model is recommended for both calculation of CVI and the step-by-step approach of checking for outliers and homogeneity in replicates and samples. The standard method for calculation of RCV should not be used when using CVs.

Clinical Use of Cancer Biomarkers in Epithelial Ovarian Cancer: Updated Guidelines From the European Group on Tumor Markers.

OBJECTIVE: To present an update of the European Group on Tumor Markers guidelines for serum markers in epithelial ovarian cancer. METHODS: Systematic literature survey from 2008 to 2013. The articles were evaluated by level of evidence and strength of recommendation. RESULTS: Because of its low sensitivity (50-62% for early stage epithelial ovarian cancer) and limited specificity (94-98.5%), cancer antigen (CA) 125 (CA125) is not recommended as a screening test in asymptomatic women. The Risk of Malignancy Index, which includes CA125, transvaginal ultrasound, and menopausal status, is recommended for the differential diagnosis of a pelvic mass. Because human epididymis protein 4 has been reported to have superior specificity to CA125, especially in premenopausal women, it may be considered either alone or as part of the risk of ovarian malignancy algorithm, in the differential diagnosis of pelvic masses, especially in such women. CA125 should be used to monitor response to first-line chemotherapy using the previously published criteria of the Gynecological Cancer Intergroup, that is, at least a 50% reduction of a pretreatment sample of 70 kU/L or greater. The value of CA125 in posttherapy surveillance is less clear. Although a prospective randomized trial concluded that early administration of chemotherapy based on increasing CA125 levels had no effect on survival, European Group on Tumor Markers state that monitoring with CA125 in this situation should occur, especially if the patient is a candidate for secondary cytoreductive surgery. CONCLUSIONS: At present, CA125 remains the most important biomarker for epithelial ovarian cancer, excluding tumors of mucinous origin.

Monitoring performance of progression assessment criteria for cancer antigen 125 among patients with ovarian cancer compared by computer simulation.

BACKGROUND: Cancer antigen 125 (CA125) is used to monitor tumor burden among patients with advanced serous epithelial ovarian cancer. The purpose is to compare the monitoring performance of seven previously proposed criteria. MATERIALS & METHODS: The CA125 assessment criteria were applied to simulated datasets. We investigated the ability to provide information on CA125 increments as well as their robustness against false positive signals. RESULTS: For baseline concentrations above cut-off, the best performing criterion was based on a confirmed increment ≥2.5-times the nadir concentration. For baseline concentrations below cut-off, the best performing criterion was based on a confirmed increment from ≤ cut-off to >two-times cut-off. DISCUSSION: Computer simulation models may be useful for a preclinical validation of criteria to be investigated in clinical trials.

Median of patient results as a tool for assessment of analytical stability.

BACKGROUND: In spite of the well-established external quality assessment and proficiency testing surveys of analytical quality performance in laboratory medicine, a simple tool to monitor the long-term analytical stability as a supplement to the internal control procedures is often needed. METHOD: Patient data from daily internal control schemes was used for monthly appraisal of the analytical stability. This was accomplished by using the monthly medians of patient results to disclose deviations from analytical stability, and by comparing divergences with the quality specifications for allowable analytical bias based on biological variation. RESULTS: Seventy five percent of the twenty analytes achieved on two COBASs INTEGRA 800 instruments performed in accordance with the optimum and with the desirable specifications for bias. DISCUSSION: Patient results applied in analytical quality performance control procedures are the most reliable sources of material as they represent the genuine substance of the measurements and therefore circumvent the problems associated with non-commutable materials in external assessment. CONCLUSION: Patient medians in the monthly monitoring of analytical stability in laboratory medicine are an inexpensive, simple and reliable tool to monitor the steadiness of the analytical practice.

Performance criteria based on true and false classification and clinical outcomes. Influence of analytical performance on diagnostic outcome using a single clinical component.

BACKGROUND: In the general classical model for diagnoses based on a single analytic component, distributions of healthy and diseased are compared and several investigations of varying analytical performance on the percentage of misclassifications have been published. A new concept based on an alternative type of diagnosing, based on sharp decision limits has been introduced in diagnostic guidelines, but only a few publications on investigation of analytical performance have been seen. METHODS: The two diagnostic models (bimodal and unimodal) based on natural logarithmic Gaussian distributions are simulated. RESULTS: In the bimodal model it is possible to evaluate the influence of prevalence of disease in combination with varying analytical performances. In the unimodal model the prevalence is pre-decided by the chosen decision limit. In this model the influence of analytical performance is investigated for diagnosing diabetes using haemoglobin A1c (HbA1c), and for patients with high and low risk for coronary heart disease defined by serum-cholesterol concentrations. CONCLUSIONS: For HbA1c the guidelines and recommendations define a maximum inter-laboratory coefficient of variation of 3.5%, but this is in DCCT units (without a true zero-point), so after transformation to IFCC units (which are proportional) it was 5.2%, which allows for analytical bias as high as approximately ±9%. Consequently, analytical quality specifications should be separated as maximum bias and imprecision.

Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model.

BACKGROUND: Reference change values provide objective tools to assess the significance of a change in two consecutive results of a biomarker from an individual. However, in practice, more results are usually available and using the reference change value concept on more than two results will increase the number of false positive results. METHODS: A computer simulation model was developed using Excel. Based on 10,000 simulated measurements among healthy individuals, a series of up to 20 results of a biomarker from each individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial result. These successive serial differences were computed to give limits for significant bidirectional changes with constant cumulated maximum probabilities of 95% (p < 0.05) and 99% (p < 0.01). RESULTS: From an individual factors used to multiply the first result were calculated to create limits for constant cumulated significant changes. The factors were shown to become a function of the number of results included and the total coefficient of variation. CONCLUSIONS: The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.

Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model.

BACKGROUND: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. METHODS: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% (P < 0.05) and 99% (P < 0.01). RESULTS: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. CONCLUSIONS: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

Weekly and 90-minute biological variations in cardiac troponin T and cardiac troponin I in hemodialysis patients and healthy controls.

BACKGROUND: Myocardial infarction (MI) is diagnosed by the finding of a single cardiac troponin value above the 99th percentile and a significant time-dependent change in cardiac troponin concentration. The aim of this study was to determine the 90-min and weekly biological variations, the reference change value (RCV), and the index of individuality (II) of high-sensitivity cardiac troponin T (hs-cTnT) (Roche Diagnostics) and hs-cTnI (Abbott Diagnostics) in patients receiving hemodialysis (HD) and in healthy individuals. METHOD: Blood samples were collected from 19 HD patients (on an HD-free day) and 20 healthy individuals at 90-min intervals over a 6-h period (between 08:30 and 14:30) and before the midweek HD treatment for 10 weeks. The within-person variation (CVi), between-person variation, RCV, and II were calculated. RESULTS: During the 6-h sampling period, the concentrations of hs-cTnT (both groups) and hs-cTnI (HD patients only) decreased on average by 0.8% to 1.7% per hour, respectively. These declining trends were included in the calculation of a 90-min asymmetric RCV: -8%/+5% in HD patients (hs-cTnT), -18%/+21% in HD patients (hs-cTnI), -27%/+29% in healthy individuals (hs-cTnT), and -39%/+64% in healthy individuals (hs-cTnI). The II was low in both groups for both assays. The weekly CVi values were approximately 8% (hs-cTnT) and 15% (hs-cTnI) in both groups. CONCLUSIONS: When using a cardiac troponin change of 20%-50% to diagnose an MI, the false-positive rate is likely to be lower for the hs-cTnT assay than for the hs-cTnI assay. The low II suggests that use of a diagnostic cutoff value can be omitted.

Changing from glucose to HbA1c for diabetes diagnosis: predictive values of one test and importance of analytical bias and imprecision.

BACKGROUND: In Denmark, the use of HbA1c in the diagnosis of diabetes was adopted from March 2012. We evaluated the change in the number of diabetes cases diagnosed by haemoglobin A1c (HbA1c) versus fasting venous plasma glucose (FPG), and estimated the influence of analytical variation and bias on the HbA1c-based prevalence of diabetes. METHODS: The study population constituted 4239 individuals not known to have diabetes randomly selected from all inhabitants aged 25-75 years in the former County of Vejle, Denmark. The number of undiagnosed patients with diabetes in the study population using FPG or HbA1c as the diagnostic criterion was estimated. Furthermore, changes in the analytical bias and coefficient of variation (CV) for HbA1c analysis were simulated and the effect on the number of diabetes cases was observed. RESULTS: Changing the diagnostic test from FPG to HbA1c reduced the number of patients with diabetes by approximately 46% based on one measurement. The predictive value of one test of HbA1c was 91% versus only 66% for one test of FPG. Analytical variation had a much greater impact on the number of patients with diabetes than bias. At a bias of 0%, an increase of CVanalytical from 2.7% to 3.7% increased the number of diabetes cases by 90%. CONCLUSIONS: In the study population, the percentage of undiagnosed patients with diabetes aged 25-75 years was reduced from 3.6% (95% CI 3.0%-4.2%) based on one FPG measurement (FPG ≥7.0 mmol/L) to only 1.9% (95% CI 1.5%-2.3%) if the diagnosis of diabetes was based on the criterion of HbA1c ≥48 mmol/mol (6.5% DCCT).